Fall 2010 Student Study Guide
From Biomedical Informatics Student Wiki
(if you are not in the class, feel free to browse but please do not alter these pages...)
If you are contributing to this page and new to wiki technology, please consider visiting the Help page to learn more about how to mark up entries, create lists and subsection headings.
Also, consider visiting Editing Wikipedia for more information on wiki editing. For a proposal on a standard format for the study guide, visit and contribute to the Study Guide Style Manual.
Week 1: Introductions, course basics, the discipline of healthcare informatics; healthcare informatics as a career
Reading Summary - Monday, Week 1 (by Mark Ebbert)
Hersh - Who are the Informaticians? What we know and should know
- What we know
- There is need for a large healthcare information technology (HIT) workforce
- Essential components of HIT are: Clinicians, IT professionals, health information management (HIM) professionals, health science librarians
- The current challenge is knowing what resources we have (as far as the needed professionals) and how to train what we will need.
- There needs to be a liaison between IT and the clinical users. This may very well be 'informaticians'
- What we should know:
- If informatics is going to become a true 'profession,' we need to define what it takes to become one.
- We need to determine:
- The optimal organization of the HIT workforce
- The best training for said individuals
- How 'professionalization' of the HIT workforce can improve the implementation of HIT
Shortliffe - Chapter 1 - The Computer Meets Medicine and Biology: Emergence of a Discipline
- 1.1 Integrated Information Management: Technology's Promise
- Paper medical records had their day, but are no longer sufficient
- The elements that interact with or are included in medical records are many and vary by situation
- Paper is not easily polled for information
- Paper put an emphasis on natural language, which is difficult to compile into meaningful information
- Electronic health records have the potential to increase efficiency, accuracy and overall documentation of patient care, history and progress
- Barriers for creating an efficient health record:
- The need for standards in the area of clinical terminology
- Concerns regarding data privacy, confidentiality and security
- Challenge of data entry by physicians
- Difficulties associated with integration of record systems and other information resources in the health care setting
- Barriers for creating a unified health record:
- Encryption: Protection for the individual's information
- HIPPA: HIPPA complicates the ability to share medical information
- Infrastructure: Setting up an infrastructure (e.g. networking, protocol, etc.) that allows easy sharing of information
- Standardized data definitions: Common terminology and database schemas (this seems redundant to me)
- Potential benefits of a unified health record. Physicians may be able to:
- Recommend steps for health promotion and disease prevention
- Detect syndromes or problems in community
- Detect trends and patterns for public health
- Develop clinical guidelines for patient-specific decision support
- Have opportunities for distributed clinical research
- What we need:
- Better infrastructure (internet)
- Education and training for physicians to be adept with computers
- Organizational and management change to build better healthcare records
- Paper medical records had their day, but are no longer sufficient
- 1.2 The Use of Computers in Biomedicine
- There are and have been many different names for fields that involve informatics in medicine, but the common term used today is 'Biomedical Informatics.' Also a very broad field with many applications.
- Shortliffe's definition of Biomedical Informatics:
- Biomedical Informatics
- biomedical informatics is the scientific field that deals with biomedical information, data and knowledge--their storage, retrieval and optimal use for problem solving and decision making.
- Computers have a long storied history which you can read about in the chapter. Computers are obviously important for all informatics disciplines.
- There's a really cool advertisement with a doctor standing way too close to an old computer trying to look like he knows what he's doing. This basically means we need to get doctors and computers working in harmony
- Biomedical informatics can be separated into two types of research:
- Basic research: searching for new knowledge
- Applications research: using the new knowledge for practical ends
- 1.3 The Nature of Medical Information
- Medical information is often inherently different from other types of data because it is often not easily quantified. This presents a challenge for informatics research.
- Medical information is also often more complex (higher-level) than data from other fields (lower-level) such as physics
- 1.4 Integrating Biomedical Computing and Medical Practice
- Global forces affecting biomedical computing and the assimilation of computers into medical practice:
- New developments in hardware and software
- Gradual increase in the number of professionals who are trained in both clinical medicine and biomedical informatics
- Ongoing changes in healthcare financing designed to control medical expenses
- Computer technology has made many aspects of medical treatment cheaper and more efficient.
- Global forces affecting biomedical computing and the assimilation of computers into medical practice:
- Why is information management a central issue in biomedical research and clinical practice?
- Medical records can be complex and are difficult to integrate between users. This affects patient care.
- What are integrated information management environments, and how might we expect them to affect the practice of medicine, the promotion of health, and biomedical research in coming years?
- Environments that combine all information for a patient to improve medical care and decrease costs
- What do we mean by the terms medical computer science, medical computing, biomed- ical informatics, clinical informatics, nursing informatics, bioinformatics, and health informatics?
- There are several subsets of Biomedical Informatics with varying specialties.
- Why should health professionals, life scientists, and students of the health professions learn about biomedical informatics concepts and informatics applications?
- All users will need to be capable of using technology in order to make adequate use of the advances
- How has the development of modern computing technologies and the Internet changed the nature of biomedical computing?
- Shown us that it is possible to integrate and improve upon technologies that are very beneficial
- How is biomedical informatics related to clinical practice, biomedical engineering, molecular biology, decision science, information science, and computer science?
- All pieces of the bigger pie.
- How does information in clinical medicine and health differ from information in the basic sciences?
- Clinical information is more complex (higher-level) and can be more challenging to interpret
- How can changes in computer technology and the way medical care is financed influ- ence the integration of medical computing into clinical practice?
- There is pressure to continually improve hardware and software and to continually decrease medical expenses.
Lecture Summary - Monday, Week 1 (by Robin Palmer)
- Introduction and navigation within WebCT/Blackboard was reviewed (for technical help contact TACC 801-585-0536, or UOnline Help Desk 801-585-5959)
- Dr. Sward recommended using Mozilla's FireFox as your web browser for the course (handles multiple pages and attachments better than Internet Explorer)
- Participation grade will be based on classroom input (including through Wimba for those who cannot attend in person), contributions to this Study Guide, and posts to course discussion threads.
- Students will present special topics, beginning approximately Week 6, either individually or as a team. These presentations can be based upon topics provided by the instructors or suggested by the student(s) with instructor approval. The presentations should be 20-30 minutes in length.
- Instructors and Students introduced themselves, and identified where they felt they fit within the "Three Circles".
Reading Summary - Wednesday, Week 1 (by Eungyoung Han)
William Hersh - A stimulus to define informatics and health information technology
- Policy makers and most people outside HIT do not understand the terminology of health information technology as well as biomedical and health informatics, though interests are growing among leaders.
- Definitions and discussion:
- Hersh begins by defining HIT, since the American Recovery and Reinvestment Act (ARRA) legislation focused on health information technology (health IT or HIT).
- Health Information Technology : the application of computers and technology in health care settings. Sometimes the term Information and Communications Technology (ICT) is used instead when the use of HIT has a strong networking or communication component.
- A more important term
- Informatics :
- 1. Hersh defines informatics as the discipline focused on the acquisition, storage, and use of information in a specific setting or domain. It emphasizes on information and relies on technology as a tool.
- 2. Friedman defines informatics in terms of a “fundamental theorem” that describes informatics as a discipline characterized by the use of technology for helping people to do cognitive tasks better, not about simply building systems to mimic or replace human expertise.
- Informatics :
- Hersh describes the “adjective problem” as being one of the biggest ongoing problems. The most comprehensive term is Biomedical and health informatics (BMHI) or health and biomedical informatics.
- All of them refer to the field that is concerned with the optimal use of information, often aided by the use of technology, to improve individual health, health care, public health, and biomedical research.
- Informaticians (Informaticists) : Practitioners of informatics. They view their focus more on information than technology.
- IMIA: International Medical Informatics Association.
- Medical (Clinical) Informatics refers to informatics applied in health care settings.
- Use of informatics in biomedical and health-related areas:
- Bioinformatics : the application of informatics in cellular and molecular biology, often with a focus on genomics.
- Translational bioinformatics: is used to describe bioinformatics applied to human health.
- Imaging informatics: informatics with a focus on imaging, including the use of PACS systems to store and retrieve images in health care settings.
- The application of informatics focusing on specific health care disciplines are nursing informatics for nurses, dental informatics for dentistry, pathology informatics for pathology, etc.
- Consumer health informatics : the field devoted to informatics from a consumer view.
- Research informatics: the use of informatics to facilitate biomedical and health research including clinical research informatics and translational research informatics.
- Clinical research informatics : informatics applications in clinical research.
- Translational research informatics : is aims to accelerate research findings from bench to bedside and into widespread clinical practice.
- Public health informatics : the application of informatics in areas of public health, including surveillance, reporting, and health promotion.
- Health Information Management (HIM) is the discipline that has historically focused on the management of medical records.
- The difference between HIM and informatics is the educational path of practitioners. HIM professionals have historically been educated at the associate or baccalaureate level whereas informaticians often come from clinical backgrounds and include those with doctoral degrees.
- Professionals in IT and computer sciences are people who have broad skills including development, programming, engineering, and supporting personnel.
- The term Electronic Medical Record (EMR) was most commonly used when the individual health records were first computerized.
- EMR was supplanted by the term Electronic Health Record (EHR), which is a broad collection of information about the patient.
- Personal health record (PHR) refers to the patient-controlled aspect of the health record.
- Health information exchange (HIE) is the exchange of health information for patient care across traditional business boundaries in health care.
- HIE is special case of secondary use or re-use of clinical data. It can be used for quality assurance, clinical research, and public health.
- Regional Health Information Organization (RHIO) are administrating over HIE.
- Regional Health Information Technology Extension Center(RHITEC) is named by ARRA and is supported by many leaders.
- Hersh standardized prefix term 'tele-':
- Telemedicine refers to the delivery of health care when the participants are separate by time of distance. Telehealth is more emphasizes on direct interaction with health on ICT.
- eHealth is a related term to the 'tele- ' prefix.
- Evidence-based medicine (EBM) is part of the BMHI, argues Hersh. Not everyone shares his view.
- The purpose of this debate is to introduce a standardization of the nomenclature in the field of HIT.
Lecture Summary - Wednesday, Week 1 Jason Gagner
Notes from white board and team activity
Question: What can informaticist do and where?
Project / Job: Research System Development Software Development Vendor / Corporate Project Manager Training end users CIO Consultants Hospital Record Manager Data Analyst System Analyst Data mining Encounter management Patient Flow Publishing Governance
Where: Universities DOH Hospitals Government Corporate- Pharmaceuticals Medical Devices Payer Systems IT Department Hospital vendor Consultant Software company
Key points from Lecture:
Biomedical and health informatics has to do with all aspects of understanding and promoting the effective organization, analysis, management, and use of information in health care. (please note slide 5 from the ppt)
Clinical Informatics is a sub-field of biomedical informatics. It focuses on computer applications that address healthcare data (collection, analysis, representation). It is a combination of information science, computer science, and clinical science … designed to assist in the management and processing of data, information, and knowledge… to support the practice and delivery of clinical care. (please note slide 11 from the ppt)
Nursing informatics (NI) is a specialty that integrates nursing science, computer science, and information science… to manage and communicate data, information, knowledge and wisdom… in nursing practice.
NI supports consumers, patients, nurses, and other providers in their decision making in all roles and settings. This support is accomplished through the use of information structures, information processes, and information technology. (please note slide 12 from the PPT)
Theory Biomedical data Vocabulary/standards Database theory Knowledge mgmt. Infrastructure (hardware, networks, grids) Cognitive science Information theory, communications Change management Behavioral theories
Applications EMR/EHR (the health record)* Patient monitoring Ancillary services (Lab, Radiology…) Regional health networks Decision support (CPOE, Alerts…) Personalized Medicine Telehealth Clinical research Clinical education
(please note slide 13 from the PPT)
Week 2: Survival guide; Models and modes of healthcare delivery
Reading Summary - Monday, Week 2 (by Julie Martinez)
ONC-COORDINATED FEDERAL HEALTH INFORMATION TECHNOLOGY STRATEGIC PLAN: 2008-2012
ABOUT THE OFFICE OF THE NATIONAL COORDINATOR FOR HEALTH INFORMATION TECHNOLOGY National Coordinator was charged with ensuring coordination of federal health IT policies and programs and of relevant executive branch agency outreach and consultation with public and private entities.
GOALS AND ORGANIZATION OF THE PLAN
Patient-focused Health Care: Enable the transformation to higher quality, more cost-efficient, patient-focused health care through electronic health information access and use by care providers, and by patients and their designees. Population Health: Enable the appropriate, authorized, and timely access and use of electronic health information to benefit public health, biomedical research, quality improvement, and emergency preparedness.
PRIVACY AND SECURITY
Objective 1.1 –Facilitate electronic exchange, access,and use of electronic health information, while protecting the privacy and security of patients’health information. Objective 2.1 –Advance privacy and security policies, principles, procedures, and protections for information access in population health.
Objective 1.2 –Enable the movement of electronic health information to support patients’health and care needs. Objective 2.2 –Enable exchange of health information to support population-oriented uses.
Objective 1.3 –Promote nationwide deployment of electronic health records (EHRs) and personal health records (PHRs) and other consumer health IT tools. Objective 2.3 –Promote nationwide adoption of technologies to improve population and individual health.
Objective 1.4 -Establish mechanisms for multi-stakeholder priority-setting and decision-making. Objective 2.4 –Establish coordinated organizational processes supporting information use for population health.
HOW HEALTH INFORMATION TECHNOLOGY CAN HELP TRANSFORM HEALTH AND CARE
Over time, as information begins to move among EHRs and PHRs, individuals will connect with their clinicians, clinicians will connect with other care providers, and health-related communities will connect with each other to enable the improvements in health and care that everyone wants. As these connections are made, the Nationwide Health Information Network, or NHIN, will evolve fully and provide communities across the entire nation with the ability to securely exchange electronic health information.
The Role of Systems Factors in Implementing Health Information Technology
Here is my favorite take away thought from this article, "Perhaps an even more important role may be to help practice managers identify areas where there is considerable variation in practice or multiple workarounds,which would be ideal targets of further research or a potential system redesign project."
Lecture Summary - Monday, Week 2 (by Danielle Sample)
Foundations of Healthcare Informatics: The Survival Guide
Important Organizations within the field:
-NIH: National Institute(s) of Health: the 'mother' of all research funding institutions; up to $30 billion budget- single largest funder of biomedical research in the world (www.nih.gov)
-NLM: National Library of Medicine: Part of the NIH; provides the training grant within the BMI Department here at the University of Utah (www.nlm.nih.gov)
-ANIA-CARING: American Nursing Informatics Association: Involved in many educational programs across the country. UNIN is a comparable local agency. (www.ania-caring.org )
-AMIA: American Medical Informatics Association: The 'big' professional association for biomedical informatics. AMIA has a large conference this fall; presentations, new information, etc. are all a part of this conference. AMIA also sponsor two different 'summits'. (www.amia.org)
-IMIA: International Medical Informatics Association: well known for its widely popular journal. (www.imia.org)
Top Informatics Programs Across the Country Include:
Columbia University Health Sciences (New York) Vanderbilt University (Tennessee) University of Utah
Useful Acronyms to Know:
ITS: Information Technology Services: Enterprise Data Warehouse is maintained by ITS (uuhsc.utah.edu/ITS/)
caBIG: caner Bio-Informatics Grid: Huge grid (compared to a utility grid- this tool shares information as a utility grid shares electricity). Lewis Frey within the BMI Department has worked extensively on this project. (cabig.nci.nih.gov)
UPDB: Utah Population Database: Very unique and important database here in Utah; it is one of a kind. (www.hci.utah.edu/group/sharedFacilities~/ccsg/UPDB.jsp). The UPDB connects several relevant organizations such as: The LDS Church, IHC, Cancer Data Registry, U of U Hospitals and Clinics, The Department of Public Safety, Social Security Death Index, and the local Department of Health. These databases are not actually all mixed; but with permission from each, data can be pulled, collected, analyzed, and linked to a question or problem being examined. Any organization has the right to refuse access to the others.
Sidenote: These systems rely on the basically mandatory release of peoples' information when they visit doctors, clinics, etc. This is a hot topic of debate: large problems could be solved and efficiency increased, yet there is a looming problem of loss of confidentiality. Our law school has become interested in this bioethical dilemma. One popular solution is rather than to ask everyone for permission, we educate people about the types of positive things that their information are being used for.
CTSA/CCTS/FURTHeR: FURTHeR (discussed more below) is a project created and being implemented by the BMI Department here at the University of Utah. FURTHeR: Federated Utah Research and Translational Health e-Repository: this project essentially plans to draw information from 'the world' (patients, researchers, health departments, etc.), into the FURTHeR system, and bring it all together in a manner for which it will be able to be seen and used in a concise manner. Metadata servers/services are being built in order to help communicate between the systems and overcome the large barrier of variability in terminology. (www.ncrr.nih.gov/clinical_research_resources/clinical_and_translational_science_awards/)
Other 'things we should know'
IRB: Institutional Review Board (http://www.research.utah.edu/irb/index.html). The IRB must approve all research projects that involve human subjects in any way prior to the research beginning. This link (http://www.research.utah.edu/irb/clerkFAQ/first_submission.html) provides a great overview of the steps that must be completed in order to submit an application to the IRB. It was mentioned during lecture today that all BMI students will have to submit an IRB proposal as part of a project during the program.
HIPAA: Health Insurance Portability and Accountability Act: Passed in 1996. This is an important government mandate dealing with confidentiality and ethics. According to the Department of Health and Human Services, Health Information Privacy is crucial: "The Office for Civil Rights enforces the HIPAA Privacy Rule, which protects the privacy of individually identifiable health information; the HIPAA Security Rule, which sets national standards for the security of electronic protected health information; and the confidentiality provisions of the Patient Safety Rule, which protect identifiable information being used to analyze patient safety events and improve patient safety. " (http://www.hhs.gov/ocr/privacy/). This website provides information about health privacy for consumers, providers, and other specialized groups such as public health professionals, researchers, health information technology, genetics issues, etc. The above linked website has a comprehensive overview and details about this Act.
Utah CHIE: Clinical Health Information Exchange (http://uhin.org/pages/products-services/chie.php). According to this website, "The Clinical Health Information Exchange (cHIE) provides physicians a way to share and view patient information in a secure electronic manner. This information is accessible, with patient consent, to authorized users while maintaining the highest standards of patient privacy." Use the link above to explore this tool more thoroughly.
Utah BEACON: HealthInsight of Utah is the recent recipient of a Beacon Communities Grant Award. HealthInsight had this to say about it: "Utah will soon benefit from the award of $15,790,181 in federal funds for achieving meaningful and measurable improvements in health care quality. In early May, the U.S. Department of Health and Human Services announced that 15 communities across the country will serve as pilot communities for eventual wide-scale use of health information technology through the Beacon Community Cooperative Agreement. The Beacon Community will use health information technology resources within the state as a foundation for bringing doctors, hospitals, community health programs, state and federal health programs, and patients together to design new ways of improving quality and efficiency to benefit patients and taxpayers. HealthInsight will lead the project in Utah." (http://www.healthinsight.org/assets/pdf/pubs/QualityInsight%20Summer10.pdf). Another useful article may be found at (http://www.axolotl.com/news/384-axolotl-congratulates-customers-on-beacon-community-awards-.html)
- I could not find much more information about this specific grant, if anyone knows more about it please add to this or let me know so that I can add more.
Liz Workman presented on "Healthcare Informatics in the Literature: Information Retrieval."
-The Eccles Library Page was examined (Quick Links on the left hand side of the page are very helpful) -Liz explained the 2 main ways of searching a database: String searching/keyword searching, and subject headings -We walked through the steps of searching on Pubmed, Medline, and CINAHL -We discussed how the Cochrane Library is very useful as it uses MeSH, in addition to keyword searching -A good summary of the interesting points made by Liz:
- There are Hundreds of Databases Available Through University of Utah Libraries.
- Ask a Librarian, or Just Browse the Database Lists of the Three Campus Libraries to Find Out More
- New Approaches like Semantic MEDLINE are Changing Information Retrieval
Reading Summary - Wednesday, Week 2 (by Jason Jacobs)
Zhou - The Relationship between Electronic Health Record Use and Quality of Care over Time
- "We found no association between duration of using an EHR and performance with respect to quality of care..."
- "...simply having an EHR may not be sufficient to improve quality and safety of health care."
- "Nonetheless, randomized controlled trials demonstrate clearly that quality improvement occurs when specific decision support is in place. Other factors...are likely also needed to achieve higher levels of quality and safety."
- "...quality and safety benefits of EHR adoption and use may be time-dependent..."
- Purpose of study: to examine how the quality of care delivered in ambulatory care practices varied according to duration of EHR adoption and usage.
- HEDIS used to measure quality of care.
- 6174 active medical and surgical practices in MA whittled down to 506 respondents.
- Participants chosen based upon response to surveys and on value or existence of desired qualifiers as determined by researchers.
- (graphs on page 462 included)
- "We found no evidence that quality of care improved with increasing duration of EHR usage."
- "...results suggest that simply implementing EHRs is unlikely to result in improved quality. Other strategies, such as paying more for higher quality care and ensuring that physicians are using EHRs to their full capacity through education and workflow transformation, may be essential."
- Limitations of this study and options for further research discussed in the last few paragraphs.
- "In summary, our results show that there was no association between duration of using an EHR and quality of ambulatory care, and in general, EHR use was not associated with improved quality of car."
- "Health care policies should consider strategies to increase the efficient use of clinical decision support and other possible tools to improve quality of care."
Kolodner - Health Information Technology: Strategic Initiatives, Real Progress
- "...deployment of health information technology (IT) is necessary but not sufficient for transforming U.S. health care."
- ..."it would be "magical thinking" to believe that health information technology (IT) alone can solve the current problems with U.S. health care."
- "Health IT adoption and use are necessary ingredients of a vibrant, patient-centered system that promotes the health and well-being of individuals and communities."
- "Federal Health IT Strategic Plan" released June 2008
- Two Goals:
- Enabling high-quality and efficient patient-focused health care.
- Supporting population health activities, including public health, biomedical research, quality improvement, and emergency preparedness.
- Four focus areas:
- Collaborative Governance
- Privacy and Security
- Two Goals:
- Notable Progress within the four key areas of focus in the Strategic Plan
- "The rate of EHR adoption has been accelerating despite remaining impediments..."
- Collaborative governance
- "Early self-organizing initiatives are developing at all levels..."
- Privacy and Security
- "Advances [regarding privacy and security policies] have begun in a few states and the private sector, and some are under way at the federal level."
- Healthcare Information Technology Standards Panel (HITSP)
- Creates standards of exchange for health information
- Certification Commission for Healthcare Information Technology (CCHIT)
- Creates criteria for certification of health IT products and services
- Nationwide Health Information Network (NHIN)
- "A reliable, secure solution for exchanging electronic health information over the Internet."
- Healthcare Information Technology Standards Panel (HITSP)
- "Together we should embrace and advance a transformational national health strategy supported by a robust health IT strategy, while remaining open to constructive comments and creative ideas."
Lecture Summary - Wednesday, Week 2 (by Jeff Duncan)
Topics: overview of healthcare system, theoretical models and conceptual frameworks
The Healthcare System can be defined in terms of:
• Locations—hospitals, clinics, dr. offices
• Actors—patients, providers, families, payers
Patients—differ by setting, acute, chronic, inpatient,outpatient
Providers—physicians, nurses, pharmacists, therapists, social workers, alternative (chiro, acupuncture), PA’s, dieticians, EMT
Four nurses per physician in the inpatient setting, higher ratios in outpatient and other settings
Clinicians are knowledge workers. Up to 80% of a clinician’s time is spent accessing, acquiring, documenting information.
Classic healthcare delivery in US; -starts with an illness, pt. goes to primary care provider, hospital, or just stays home -from primary care to home, or hospital -from hospital to home care, home, nursing home, morgue
(This is one “episode of care” from illness through treatment to some resolution)
Challenges to current healthcare system—
-knowledge: huge growth in quantity of information available -patients are becoming more active participants in decision making -changes in practice populations due to things like telemedicine, aging population
--current system is an “illness model”. System is evolving into a “wellness” model with increasing focus on preventive care, public health, surveillance
www.healthypeople.gov public health initiative with a goal of wellness and prevention. Sets public health objectives for things like access to care, cancer, diabetes, chronic diseases, food safety, environment
More challenges to current healthcare system— -quality issues, including medical errors, fragmented information, low adoption rates for “best practices” -increasing demands, such as population growth, growth of elderly population, shortage of clinicians
-estimates of additional 5K medical students and 20K nursing students per year to meet projected demand and meet levels of service
-another challenge is increasing costs (US healthcare per capita costs increased from $891 in 1960 to $5670 in 2003, from 5.1% of GDP to 15.3% of GDP
-healthcare costs are increasing faster than any other sector of the economy, and the US population is aging, increasing demand.
Vision of “new” healthcare system: -increased reliance on IT, EHR, information sharing, longitudinal records -different ways to organize data, decision support linked with workflow -more consumer control
Issues with adopting “new” system: -change is disruptive -numerous technical challenges brought on by different systems, organizations, technology adaptation -cost is immediate but benefit can take years to accrue
Theoretical Models and Conceptual Frameworks
Models: are abstractions that describe, explain, predict, sometimes describe. They are “a way to think about something”
Models provide a framework for organization, terminology, data collection, research approach
Models used in informatics: -Structural/Architectural (e.g. FURTHeR, 3 circles) -Methodological/Approach (e.g.phenomenology, software development lifecycle, agile programming) -Defining Relationships (What are specific variables and how do they interact with each other?) Models used in informatics come from numerous disciplines including CS, IS, Communications, Cognitive Science
Wiki with a number of models used in informatics research:
Week 3: Information flow in healthcare; the health IT imperative
Reading Summary - Wednesday, Week 3 (No new readings this day)
See previous weeks readings from Zhou & Kolodner
Lecture Summary - Wednesday, Week 3 (by <Charles Reed>)
Information flow in healthcare and the health IT imperative Information flow – Goal is to move information between providers and organizations and to maintain meaning.
- Syntactic interoperability – correctly formed according to the rules or accepted structures of syntax.
- Symantec interoperability – could not find information on this term.
History of Healthcare informatics
- Different hardware and software systems. Fragmented and isolated. Information silos.
- Shadow records and copies. From system to system to system.
- Mismatching of records.
- Separate vendors, support, terminals
Transition of Informatics through the years
- 1960’s - main frame computers - huge, expensive, space hogs
- 1970’s - smaller, cheaper, space piglets, slow
- 1980’s to today – micro-computers, LAN, internet
Healthcare informatics is about integration - mindset changes, informatics must adapt with these changes from old care to new care model. Refer to PowerPoint slides number five. Many problems can be tied to communication. There needs to be a nation-wide implementation in information systems. It is important to protect patients’ rights and privacy. Idea is having the right information to the right people at the right time. Incentives need to be made for adoption.
HITECH - The Health Information Technology for Economic and Clinical Health Act. Refer to healthit.hhs.gov. Zhou’s article not enough supporting information and questions about sampling. Both articles indicate IT is likely to be helpful, but not the solutions. To what extent were these people using the systems, to their full extent? It must be used to its full extent. Not just adoption. To get optimal improvement in how we change healthcare. Interoperability and standards presents a big challenge. Politics may interfere. Many systems and standards exist. Formal organizations are emerging to help deal with the HIE challenge:
- NHIN - National Health Information Network - Developed by the ONC
- No vision of a central data repository, instead were talking about transmitting data though agreements.
- How do the patients maintain some control over their information? Are smart chips the answer? Sounds like a good future presentation.
Week 4: The nature of biomedical data; Communicating and capturing biomedical data
Reading Summary - Monday, Week 4 (by Susan Pollock)
Biomedical Data from Shortliffe - Chapter reading
What are medical data? A medical datum is any single observation of a patient. A single datum is defined by four elements: the patient, the parameter, the value and the time of observation. Medical data are multiple observations.
How are medical data used? Medical data is used to document patient care, support population studies, provide a historical record of the patient, enable process improvement experiments, communicate between clinicians, ensure continuity of care, anticipate future problems, guide and document patient education, record interventions, identify trends and deviations, provide a legal record and support clinical research.
What are the drawbacks of the traditional paper medical record? It may be unavailable, difficult to navigate, poorly organized, hard to read, disjointed, large and bulky, redundant, unable to support research and passive.
What is the potential role of the computer in data storage, retrieval, and interpretation? Computational techniques for data storage, retrieval, and interpretation make it possible for patient records to: (1) monitor their contents and generate warnings or advice for providers based on single observations or on logical combinations of data; (2) provide automated quality control, including the flagging of potentially erroneous data; or (3) provide feedback on patient-specific or population-based deviations from desirable standards.
What distinguishes a database from a knowledge base? A database is a collection of individual observations without any summarizing analysis. A knowledge base is a collection of facts, heuristics, and models that can be used for problem solving and analysis of data.
How are data collection and hypothesis-generation intimately linked in medical diagnosis? In the hypothetico-deductive approach there is sequential, staged data collection, followed by data interpretation and the generation of hypotheses, leading to hypothesis-directed selection of the next most appropriate data to be collected. This generally results in a differential diagnosis somewhere along the line.
What are the meanings of the terms prevalence, predictive value, sensitivity and specificity?
- Predictive Value - The post-test (updated) probability that a disease is present based on the results of a test.
- Prevalence - The prevalence of a disease is a measure of the frequency with which the disease occurs in the population of interest
- Sensitivity - The likelihood that a given datum will be observed in a patient with a given disease or condition. Example: Female gender is a highly sensitive indicator of pregnancy (there is a 100 percent certainty that a pregnant patient is female), but it is not a good predictor of pregnancy (most females are not pregnant).
- Specificity – The ability of an observation to support a hypothesis. An observation is highly specific for a disease if it is generally not seen in patients who do not have that disease.
- Pathognomonic – Tests that evoke a specific diagnosis and immediately prove it to be true (Pap smear with abnormal cells). This is unfortunately uncommon.
How are the above terms related? The PV (Predictive Value) of a positive test depends on the test’s sensitivity, specificity, and prevalence.
What are the alternatives for entry of data into a medical database?
- Clinicians can enter the data using keyboard, point-and-select, light pens, mouse, hand-held tablet, PDAs.
- Patients can enter the data.
- Physician completes a form and the data entry is done by clerical staff.
- Data is automatically entered by the device.
Biomedical Data - Summary sheet describing biomedical data and data organizations.
- Data can be subjective (observations) or objective (measurements).
- Data is a key ingredient of clinical decision-making.
- Different clinicians have different data needs .
- Clinical data changes over time (slowly changing dimensions). Should one hang on to the old data or delete it?
- Longitudinal data is important.
- Data use:
- The medical record evolves alongside the evolving healthcare process:
- Single provider/single record(paper)->Multiple providers/single record(paper)->Multiple providers/multiple records(paper)->Multiple providers/ multiple records(mixed paper/electronic)->Multiple providers/ multiple records(all electronic)…a realistic or even proper goal?
- The medical record evolves alongside the evolving healthcare process:
- How medical data is used: research; prediction and prevention; deviation flagging; legal record.
- Drawbacks of paper: single point of failure (missing chart); inefficient access in the paper record; inconsistent entry/organization; legibility issues; multiple-volumes possible; record is time-serial; redundancy frequent (and no means to keep it aligned if one part changes); research on paper records daunting and error prone; paper records are passive: no way for paper to enforce consistency or standards.
- Consistent and standard vocabularies (i.e., nomenclature) are essential; impossible on paper, but more likely in a well-designed EHR. Suddenly the EHR starts to influence the healthcare process itself.
- Coded data is much easier to analyze. Popular coding systems: ICD-9/10; SNOMED; CPT; UMLS.
- Information Hierarchy: data (the observations); knowledge (interpreted data); information (integrated data and knowledge that informs).
- The user interface is critical. If it is awkward, it leads to a poor utilization, which leads to non-acceptance. It is a huge problem in the clinical world where the primary focus is treating the sick. Our tolerance for putting up with a poor interface varies directly with the stress on us to achieve a rapid and useful outcome. Warner’s Rule: medical informatics is 10% technology and 90% psychology.
- Heuristic – A rule-of-thumb or default approach based on experience.
- True positives (TP) - When measuring a collection of data, the number of correct (really real) positive results found.
- False positives (FP) - When measuring a collection of data, the number of wrong (falsely real) positives found.
- True negatives (TN) - When measuring a collection of data, the number of correct (really false) negative results found.
- False negatives (FM) - When measuring a collection of data, the number of wrong (falsely wrong) negative results found.
- Positive predictive value (PPV) – Of all the positive results, how many were right? [e.g., TP/(TP+FP)] The false positives were incorrectly positive, skewing the value of a positive result.
- Negative predictive value(NPV) - Of all the negative results, how many were right? [e.g., TN/(FN+TN)] The false negatives were incorrectly negative, skewing the value of a negative result.
Lecture Summary - Monday, Week 4 (by <your name here>)
Reading Summary - Wednesday, Week 4 (by <Deena Farmer>)
De Keizer – Understanding Terminological Systems I: Terminology and Typology
Article was published in 2000. deKeizer et al., recognizing the increased use of EMR’s saw the need for a standard vocabulary which they called the “terminological system”. While such systems did exist they felt that these were inadequate and created a framework for a system that would work for medical diagnoses.
Semiotic triangle composed of objects, concepts, and designations. The article discusses only concepts but expands on them with terms, definitions, codes, characteristics, and relationships. The framework they use seeks to “order and define” the concepts by using the relationships which can be non-hierarchical or hierarchical (further divided into generic or partitive).
Various terminological systems are defined in brief: terminology, vocabulary/glossary, nomenclature, classification, taxonomy (included with classification in this article), coding system, and ontology. Such systems are used (terminological and coding) and are described using five examples (ICD 9/10, SNOMED, NHS, UMLS, and GALEN).
ICD 9 CM and ICD 10 – best known, a classification of either “generic related diagnostic terms” or “chapters arranged by anatomical system or etiology”, also includes additional classifications lists and a nomenclature, coding schema is significant
NHS clinical terms – has evolved into a nomenclature with “a vocabulary character”
SNOMED – possibly a nomenclature, also with “vocabulary character”, includes ICD 9 terms and codes, coding schema is significant
UMLS – a thesauraus but also characteristic of a classification and a vocabulary
GALEN – has three modules, one of which is an ontology, one is a nomenclature and a vocabulary
Bodenreider – The Unified Medical Language System (UMLS): integrating biomedical terminology
Article was published in 2003. -developed by National Library of Medicine -2.5 million names for 900,551 concepts -12 million relations among concepts -comprised of the Metathesaurus, the Semantic Network, and lexical resources -knowledge sources are updated quarterly (note again – this article is seven years old) -not developed for informaticists but includes vocabulary, etc. of NCBI, MeSH, SNOMED, ….. -tried to put the diagram of UMLS here but I can't figure it out, sorry, see article for diagram (or our ppt from 9/15)
-organized by concept, linked by relationship, and categorized by semantic type
-example of how UMLS can cross reference with other resources: also tried to put other diagram from article here but again, couldn't figure it out, sorry, see the figure at the top of the third page of the article
-use is free but users have to sign a license agreement -several ways to access and use UMLS -UMSL info website: http://umlsinfor.nlm.nih.gov
Bernstam – What is Biomedical Informatics?
-informatics has been “emerging for decades”, no real definition has taken hold, the authors think a definition could help with educational program design, administrative decisions, communication, and research agenda -definitions have focused on aspects of informatics such as information technology, role/task/domain, and concepts, these definitions are not all-encompassing and lack the depth of the field which they attempt to define
-data, information, and knowledge are at the core of most definitions
-two predominant concepts - Ackoff's DIKW heirarchy and definitions from philosophy
- Ackoff's: data = symbols, infomation = data processed to be useful; knowledge = application of data and info, wisdom = evaluated understanding
-philosophy of information: data = lack of uniformity, information = meaningful data, knowledge = information that is true, justified, and believed
- another way of thinking of these is that "data is the syntatic part of information and meaning is the semantic part"
-in representational system data = form and meaning = content
-proposed definition - "informatics is the science of information where information is defined as data without meaning. Biomedical informatics is the science of information applied to, or studied in the context of biomedicine"
-challenges for the field suffice and include - automating the processing of meaning, manipulating knowledge with available tools, and the gap between human information needs and information technology capabilities
-authors formulate implications: 1. defining informatics as the study of data + meaning clearly distinguishes informatics from important related fields 2. computation is an important tool for informatics, but it is not the primary object of study and is neither a necessary nor sufficient condition for informatics 3. defining informatics as the study of meaningful data informs informatics curriculum design 4. the emphasis on meaning allows us to see why some informatics problems are easier than others
Lecture Summary - Wednesday, Week 4 (by <Jitsupa Peelay>)
DIKW Framework -Data : is what we put on electronic system. As we start collecting data, we will be able to get meaning around. -Information: Data that is processed. As we take information, collect from different sources, we will start to develop rules and recognize relationship between different pieces of information. -Knowledge: When you start thinking about the rules, incorporating with your baseline knowledge. Then you will quite understand what is going on. -Wisdom: is unique human attribute supported by computer. It is defined as applying knowledge appropriately. It has couple different aspects compared to 3 loops. It’s application-based. It has implications of value, ethic. So, it’s really personal attribute but like knowledge, everything in knowledge and wisdom loop depends on information and data. They start to build on each other -Clinicians collect, gather, aggregate data and develop information from the data. They synthesize it to create knowledge. Data that we have written down. The way we do that is by using some symbols such as word, code to represents these things from health care environment.
• Why do we document? - Historical record is a primary mean of communication. - Communication is the no.1 reason why we document. - We can manage what is going on with the patients. We also use document as clinical decision support such as detecting trends. Therefore, we will know whether patients are getting better or not. -One of the issue is that how much time it takes.
• Data Capture: is how we take the data that we gather and record it somehow.
-There are two structure formats of clinical documentation:
1. Unstructured charting : free text note which is the vast majority of clinical data. 2. Structured charting : forms, templates, and coded data. It is much easier to process data - Getting data into computer is still a significant barrier - A of data still remains on the paper. - How do we capture the data. Input device: from PDA , Laptop, handheld devices. There are many kinds of equipment that get data into computer. It depends on where you are at. May chart directly to medical record or have to be read and transcribed by a clinician and enter to computer. May have to transcribe or go directly - There are different types of data Text, images, picture.
• Communication: one of the primary reasons we capture in document.
- “Depends upon the prior existence of an agreed upon set of semantic and syntactic rules” (ISO, 1987).
1. Semantic has to do with meaning 2. Syntactic has to do with grammar, structure, form, shape of the conversation.
• Information - 3 components = Ability to communicate and manage data, knowledge, between people across time and institutions.
• Standards - A document, established by consensus and approved by a recognized body. - Basically, a set of terms, definitions, and rules for manipulating. - It’s very important in the aspects of: communication, quality, comparability, and to enable computerized decision support and secondary research use. - We are dealing with the idea of longitudinal records of things that need to be able to communicate between people, health care organizations, third party like payer.
- We may have different record and data. So, better solution is to find some way for these systems to talk to each other in standard way rather than uniquely creating system.
• Health Care Standards - The primary one that we use in the U.S. is HL7 which primarily deals with how we structure those messages. - NCVHS : as part of HIPAA regulations, was charged with the tasks of saying which standard would be the one that need to be used for electronic records and charged for the task of making sure that those standards comply with HIPPA regulations for security and privacy.
• Semiotic triangle -Concepts, objects, terms – basic elements -Terminology: A lot of work we do around terminology is how do we differentiate those different meaning. - Code:
-Significant: human readable, tie to the meaning, good when working pretext code. - Nonsignificant : the code that doesn’t have in transit meaning related to what it mean. It could be a random number. - Multidisciplinary Terminologies: such as ICD-9-CM, ICD-10, CPT, SNOMED, and LOINC. In addition, ICD and CPT together determine DRGs.
- ICD and CPT are too broad/general for clinical decision-making. - They go off and develop the new one that they think it would work better. - There are a lot of nursing terminologies that emerge because the medical terminology did not cover the things that nursing do.
• Mapping : Idea of linking related terms with the goal of maintaining the unique good parts
- UMLS : One of the big sort of mapping effort . It came out from national library of medicine. - caBIG: EVS is subset of UMLS. They have different code for the different physical representation
Week 5: Metadata, ontologies, and knowledge management; Overview of hospital information systems
Reading Summary - Monday, Week 5 (by Robin Palmer)
BODENREIDER - Biomedical ontologies in action: Role in knowledge management, data integration and decision support 
- 1 Introduction
- "The need for standardizing biomedical vocabulary is not recent. As long ago as the 17th century, health authorities in London used a standard list of about 200 causes of death"
- "The last decade has seen a marked increase in the number of artifacts created for representing biomedical entities, their terms and their relations, often referred to as vocabularies, terminologies, and ontologies... For the sake of simplicity, we henceforth refer to these various types of artifacts as ontologies"
- "The ontologies under investigation in this survey include SNOMED CT...LOINC...FMA...the Gene Ontology...RxNorm...the National Cancer Institute Thesaurus...the International Classification of Diseases...MeSH...and the Unified Medical Language System.
- 2 Knowledge management
- "One major role of biomedical ontologies is to serve as a source of vocabulary"
- "The terminological component of biomedical ontologies is an important resource for natural language processing systems and supports knowledge management tasks such as annotation (or indexing) of resources, information retrieval, access to information and mapping across resources"
- 2.1 Annotating data and resources
- "Virtually every ontology in our survey serves as a source of vocabulary for the purpose of annotating data or indexing documents"
- "Indexing is principally used in reference to the assignment of entries from a controlled vocabulary to documents, e.g., the biomedical literature. While the indexing of large collections such as PubMed/MEDLINE is still performed manually for the most part, automatic indexing systems have been developed"
- "Systems such as GoPubMed co-annotate the biomedical literature to both MeSH and the Gene Ontology
- "The indexing of clinical documents is generally referred to as coding--and biomedical ontologies are sometimes called 'code sets'. The International Classification of Diseases (ICD) has been used for over a century for coding morbidity and mortality, and more recently, as a coding system for reimbursement"
- "SNOMED CT is becoming adopted as a standard terminology for electronic health records by a growing number of countries, and has also been evaluated as a source of vocabulary for clinical research"
- "The UMLS Metathesaurus as a whole has also been used to support the coding of clinical documents, such as surgical pathology reports"
- "In biology, the functional description of experimental data is usually referred to as annotation"
- "Functional annotation is not limited to the annotation of gene products to the Gene Ontology....(used) SNOMED CT and the NCI Thesaurus to annotate tissue microarray data in the Stanford Tissue Microarray Database...MeSH was used to annotate mentions of human diseases..."
- "Related to the notion of indexing is that of term recognition, i.e., the process of automatically identifying mentions of entities of interest in text through natural language processing (NLP) techniques"
- Examples of "biomedical term recognition systems" include MetaMap, MetaPhrase,Termine, and Whatizit
- 2.2 Accessing biomedical information
- "The main function of the indexing of large document collections such as MEDLINE is to support accurate retrieval, i.e., with high recall and high precision"
- "...by providing lists of synonyms, relations among concepts, high-level categorization and co-occurrence information, the UMLS plays a major role in the retrieval of various types of documents..."
- "Several biomedical search engines exploit MeSH and the UMLS to provide access to the biomedical literature" (examples listed: SAPHIRE, Essie, Textpresso, WRAPIN, MedicoPort)
- 2.3 Mapping across biomedical ontologies
- "The availability of several dozen biomedical ontologies is both a blessing and a curse. On the one hand, users can choose from a variety of ontologies and select the artifact that best fits their purpose. On the other hand, resources annotated to different ontologies become more difficult to integrate, unless mappings are created among ontologies in order to identify equivalent concepts across ontologies"
- "The UMLS Metathesaurus is a terminology integration system, in which synonymous terms from various terminologies are clustered into concepts, allowing for the seamless mapping between terms from different terminologies through a UMLS concept"
- 2.1 Annotating data and resources
- 3 Data integration, exchange and semantic interoperability
- ""Biomedical ontologies are often cited as an important element of semantic interoperability and information exchange"
- "For example...in the standardization of patients data to be exchanged across electronic health record (EHR) systems, contributing to connect 'islands of data'"
- 3.1 Information exchange and semantic interoperability
- "The use of RxNorm, UMLS, and SNOMED CT is reported as part of a mediation strategy to exchange medication data between the Veterans Affairs (VA) and the Department of Defense (DoD) clinical information systems"
- "LOINC is used widely in the exchange of laboratory data, often in conjunction with HL7"
- "The BRIDG model, developed by the Biomedical Research Integrated Domain Group, is an information model designed to 'support practical application and data interchange' for clinical research. Semantic interoperability between clinical trials information systems is supported in BRIDG through semantic harmonization"
- "The HL7 Clinical Document Architecture, Release 2...CDA R2 associates the HL7 Reference Information Model with terminologies such as LOINC, SNOMED CT, and RxNorm for representing semantics of a clinical document"
- 3.2 Information and data integration
- "Ontologies support data integration in two different ways":
- "...by providing a controlled vocabulary in a given domain, ontology support the standardization required from warehousing approaches to data integration, in which the sources to be integrated are transformed into a common format and converted to a common vocabulary"
- "...mediation-based approaches use ontologies for definig a global schema (in reference to which queries are made) and mapping between the global schema and local schemas"
- Examples listed by the author include: TAMBIS, the BioMediator, and OntoFusion
- "Ontologies support data integration in two different ways":
- 3.1 Information exchange and semantic interoperability
- 4 Decision support and reasoning
- "Five broad kinds of applications of ontologies are discussed next":
- 4.1 Data selection
- "Many clinical and epidemiological research studies involve the creation of groups (from an independent variable) whose characteristics (dependent variables) are examined for differences...ontologies can help define groups"
- "The (ICD) is used pervasively for selectikng groups of patients in association with a high-level disease category"
- "Many other ontologies are used for data selection purposes, including SNOMED CT"
- "A hierarchical structure was added to LOINC in order to facilitate public health reporting"
- 4.2 Data aggregation
- "In addition to data selection, ontologies are used for identifying the charateristics of groups...ontologies support the aggregation of characteristics and the ICD is often used..."
- "One limitation of data aggregation based on hierarchies is the heterogeneous density of terms throughout the ontology (i.e., some branches are more richly developed than others)"
- 4.3 Decision support
- "Clinical decision support systems generally benefit from ontologies in two principal ways":
- "...ontologies provide a standard vocabulary for biomedical entities, helping standardize and integrate data sources"
- "...ontologies are a source of computable domain knowledge that can be exploited for decision support purposes, often in combination with business rules"
- "Besides clinical decision support, ontologies support reasoning in applications. The Foundation Model of Anatomy (FMA) was used as a source of anatomical knowledge for reasoning about penetrating injuries, more exactly for predicting the consequences of penetrating injury"
- "Clinical decision support systems generally benefit from ontologies in two principal ways":
- 4.4 Natural Language Processing applications
- "...Natural Language Processing (NLP) techniques support term recognition, exploiting the vocabulary provided by biomedical ontologies"
- "Ontologies also provide the domain knowledge necessary for advanced NLP applications, including information extraction for a specific task, relation extraction, document summarization, question answering, literature-based discovery, and more generally, text mining"
- "Systems such as BioCaster and EpiSpider apply term recognition techniques to health news feeds and integrate the extracted information with other resources (including ontologies), creating what is known as 'mashups'. These resources can help track cases of, say, avian influenza and support biosurveillance and public health"
- "...some systems extract relations (i.e., facts asserted in text), thus 'interpreting' the text"
- Examples listed in the article include: SemRep, (Bio)MedLEE, and Tessi.
- 4.5 Knowledge discovery
- "By supporting the high-throughput processing of biological and clinical data, ontologies are a component of the data-drive approach to biomedical research, synergistic with the traditional hypothesis-driven approach"
- 4.1 Data selection
- "Five broad kinds of applications of ontologies are discussed next":
- 5 Discussion
- "Ontologies have become important resources for biomedical research....There are still barriers, however, to the use of ontologies in biomedical applications":
- Availability - The author notes that some ontologies are freely available, some require license agreements, and some require residence in a country which supports the ontology.
- Discoverability - There are over 140 ontologies, and no central registry exists to provide links to them all
- Formalism - The formats used by ontolgies vary; some formats listed by the author include RRF, OBO, and OWL.
- Integration - "There are basically two approaches to integrating ontologies....the UMLS realizes the post hoc integration of ontologies, from the bottom up, without interfering with the development process or governance of the ontolgies being integrated....the OBO Foundry promotes a model of coordinated development of ontologies"
- Quality - "...assessing the quality of biomedical ontologies with intrinsic criteria is difficult and might be futile if disconnected from practical applications"
- "Ontologies have become important resources for biomedical research....There are still barriers, however, to the use of ontologies in biomedical applications":
- 6 Conclusions
- "Ontologies play an important role in biomedical research...they provide the controlled vocabulary required for the annotation of biological datasets, the biomedical literature, and patient records, facilitating the retrieval of...information"
- "...facilitates the exchange of information and contributes to semantic interoperability among systems"
- "...many applications use ontologies as a source of computable domain knowlege, including natural language processing applications and decision support systems"
CIMINO - Desiderata for Controlled Medical Vocabularies in the Twenty-First Century 
- "The need for controlled vocabularies in medical computing systems is widely recognized"
- "In one attempt (to combine patient data from different systems) the differences between the controlled vocabularies of the two systems was found to be the major obstacle--even when both systems were created by the same developers"
- "The solution seems clear: standards" (note: no "one" set of standards agreed to by all experts currently exists)
- Definition: latin for "desired things" 
- "The task of enumeration of general desiderata for controlled vocabularies is hampered in two ways"
- "First, the desired characteristics of a vocabulary will vary with the intended purpose of that vocabulary and there are many possible intended purposes. I address this issue by stating that the desired vocabulary must be multipurpose."
- "A second obstacle to summarizing general desiderata is the difficulty teasing out individual opinions from the literature and unifying them."
- "The need for controlled vocabularies for medical computing is almost as old as computing itself"
- Content, Content, Content
- "...the importance of vocabulary content can not be over stressed"
- Cimino offers examples of how content can be developed:
- "One approach to increasing content is to add terms as they are encountered"
- "An alternative approach is to enumerate all the atoms of a terminology and allow users to combine them into necessary coded terms...The trade-off is that, while domain coverage may become easier to achieve, use of the vocabulary becomes more complex."
- "The real issue to address in considering the "content desideratum" is this: a formal methodology is needed for expanding content"
- Concept Orientation
- "Careful reading of medical informatics research will show that most systems that report using controlled vocabulary are actually dealing with the notion of concepts"
- Concept orientation means that terms must correspond to at least one meaning ("nonvagueness") and no more than one meaning ("nonambiguity"), and that meanings correspond to no more than one term ("nonredundancy")
- "However, a distinction must be made between ambiguity of the meaning of a concept and ambiguity of its usage"
- "Concept orientation, therefore, dictates that each concept in the vocabulary has a single, coherent meaning, although its meaning might vary, depending on its appearance in a context (such as a medical record)"
- Concept Permanence
- "...the meaning of a concept, once created, is inviolate. Its preferred name may evolve, and it may be flagged inactive or archaic, but its meaning must remain"
- "For example, the old concept "pacemaker" can be renamed "implantable pacemaker" without changing its meaning..."
- Nonsemantic Concept Identifier
- "If each term in a vocabulary is to be associated with a concept, the concept must have a unique identifier"
- "If a concept may have several different names, one could be chosen as the preferred name and the remainder included as synonyms. However, using a name as a unique identifier for a concept limits our ability to alter the preferred name when necessary"
- "Because many vocabularies are organized into strict hierarchies, there has been an irresistible temptation to make the unique identifier a hierarchical code which reflects the concept's position in the hierarchy"
- "One advantage to this approach is that, with some familiarity, the code becomes somewhat readable to a human and their hierarchical relationships can be understood"
- "Another advantage of hierarchical codes is that querying a database for members of a class become easier..."
- "There are several problems with using the concept identifier to convey hierarchical information"
- "It is possible for the coding system to run out of room"
- "...if a concept belongs in more than one location in he hierarchy (see "Polyhierarchy", below), a convenient single hierarchical identifier is no longer possible"
- "There seems to be almost universal agreement that controlled medical vocabularies should have hierarchical arrangements. This is helpful for locating concepts (through "tree walking"), grouping similar concepts, and conveying meaning (for example, if we see the concept "cell" under the concept "anatomic entity" we will understand the intended meaning as different than if it appeared under the concepts "room" or "power source").
- "There is some disagreement, however, as to whether concepts should be classified according to a single taxonomy (strict hierarchy) or if multiple classifications (polyhierarchy) can be allowed"
- "Zwiegenbaum and his colleagues believe that concept classification should be based on the essence of the concepts, rather than arbitrary descriptive knowledge"
- "...strict hierarchies are more manageable and manipulable, from a computing standpoint, than polyhierarchies"
- "General consensus seems to favor allowing multiple hierarchies to coexist in a vocabulary without arguing about which particular tree is the essential one"
- Formal Definitions
- "Many researchers and developers have indicated a desire for controlled vocabularies to have formal definitions in one form or another"
- "The important thing to realize about these definitions is that they are in a form which can be manipulated symbolically (i.e. with a computer), as opposed to unstructured narrative text variety, such as those found in a dictionary"
- "The creation of definitions places additional demands on the creators of controlled vocabularies"
- "...the effort required to include finitions may help not only the users of the vocabulary, but the maintainers as well: formal definitions can support automated vocabulary management, collaborative vocabulary development, and methods for converging distributed development efforts"
- Reject "Not Elsewhere Classified"
- "The problem with such terms is that they can never have a formal definition other than one of exclusion - that is, the definition can only be based on knowledge of the rest of the concepts in the vocabulary"
- "...as the vocabulary evolves, the meaning of NEC concepts will change in subtle ways"
- "The controlled vocabularies should reject the use of "not elsewhere classified" terms"
- Multiple Granularities
- "Each author who expresses a need for a controlled vocabulary does so with a particular purpose in mind"
- "For example, the concepts associated with a diabetic patient might be (with increasingly finer granularity): 'Diabetes Mellitus', 'Type II Diabetes Mellitus', and 'Insulin-Dependent Type II Diabetes Mellitus' (note that the simpler term 'Diabetes' is so coarse-grained as to be vague)"
- "It is essential that medical vocabularies be capable of handling concepts as fine-grained as 'insulin molecule' and as general as 'insulin resistance'"
- Multiple Consistent Views
- "If a vocabulary is intended to serve multiple functions, each requiring a different level of granularity, there will be a need for providing multiple views of the vocabulary, suitable for different purposes"
- "We must be careful to confine the ability to provide multiple consistent views, such that inconsistent views do not result"
- Beyond Medical Concepts: Representing Context
- "Many researchers have expressed a need for their controlled vocabulary to contain context representation through formal, explicit information about how concepts are used"
- "If drawing the line between concept and context can become difficult, drawing the line between the vocabulary and the application becomes even more so. After all, the ultimate context for controlled medical vocabulary concepts is some external form such as a patient record"
- "...what hope is there for standardizing on a record structure? One possible solution is to view the recording of patient information from an 'event' standpoint, where each event constitutes some action, including the recording of data, occurring during an episode of care which, in turn occurs as part of a patient encounter"
- Evolve Gracefully
- "It is an inescapable fact that controlled vocabularies need to change with time"
- "All too often, however, vocabularies change in ways that are for the convenience of the creators but wreak havoc with users"
- "...good reasons for change (such as simple addition, refinement, precoordination, disambiguation, obsolescence, discovered redundancy, and minor name changes) can be understood and bad reasons (such as redundancy, major name changes, code reuse, and changed codes) can be avoided"
- Recognize Redundancy
- "...redundancy is the condition in which the same information can be stated in two different ways. Synonymy is a type of redundancy which is desirable: it helps people recognize the terms they associate with a particular concept..."
- "...the ability to code information in multiple ways is generally to be avoided"
- "As vocabularies evolve, gracefully or not, they will begin to include this kind of redundancy. Rather than pretend it does not happen, we should embrace the diversity it represents, while, at the same time, provide a mechanism by which we can recognize redundancy and perhaps render it transparent"
- "The intense focus previously directed at such issues as medical knowledge representation and patient care data models, is now being redirected to the issue of developing and maintaining shareable, multipurpose, high-quality vocabularies"
- "'High quality' has been difficult to define, but generally means that the vocabulary approaches completeness, is well organized, and has terms whose meanings are clear'"
- "The solutions necessary to meet the above list of desiderata vary from technical to political, from simple adoption to basic shifts in philosophy, and from those currently in use to areas ripe for research"
- "The simple solution of 'add more terms until they're happy' is not satisfying vocabulary users; they want content, but they want more"
- "Despite their perceived infancy, the currently available standards should be the starting point for new efforts"
- "It is likely that vocabularies will become concept-oriented, using nonsemantic identifiers and containing semantic information in the form of a semantic network, including multiple hierarchies"
- "Some of the other desiderata, such as context representation, multiple consistent views, and recognition of redundancy will probably be late in coming"
- "This list of desiderata is not intended to be complete; rather, it is a partial list which can serve to initiate discussion about additional characteristics needed to make controlled vocabularies shareable and reusable"
- "...vocabularies are undergoing their next molt"
CIMINO, ZHU - The Practical Impact of Ontologies on Biomedical Informatics 
- 1 Introduction
- "New methods for representing terminologies have been explored, including those that make them more usable by computers. In particular, terminologies have been evolving to include knowledge about their terms, especially definitional knowledge"
- "The purpose of this paper is to review some of the current work in the ontological approach to controlled biomedical terminologies. Particular attention is given to work that has resulted in practical impact on the terminology development or on the use of terminologies in health care"
- 2 Defining Ontology and Impact
- The term 'ontology' has long been used to refer to a branch of philosophy that deals with the study of being. When used in the context of knowledge representation, however, an 'ontology' is 'a formal specification of a conceptualization"
- "In the biomedical informatics field, ontologies have been used for representing a variety of knowledge bases; this paper focuses on terminology knowledge bases - that is, repositories of knowledge about the meanings of terms in terminologies"
- 3 Ontologies and Their Impact
- 3.1 GALEN
- GALEN - General Architecture for Languages, Enclopedias (sic) and Nomenclatures in Medicine
- "...evolved from the knowledge-based terminology of Alan Rector's Pen&Pad electronic medical record system"
- "Pen&Pad's terminology was represented using a formalism called Structured Meta Knowledge (SMK), in which terms were defined through relationships to other terms, and grammars were provided to allow combinations of terms into sensible statement"
- "A consortium of European universities, agencies, and vendors formed the GALEN project to develop standards for representing coded patient information"
- "...OpenGALEN was established to distribute the reference model free of charge and work with software vendors and terminology developmers to support it's extension and use....the GALEN model has been used to study nursing terminologies, a pain terminology, decision support knowledge, surgical procedures, and anatomy"
- 3.2 Unified Medical Language System
- "...UMLS was created by the US National Library of Medicine...as a kind of meta-terminology, subsuming the contents of other terminologies. It has grown to include over 100 terminologies, 1 million concepts, and 4 million names for those concepts"
- "The UMLS model identifies terminologic entities at three levels: the string (any name for a term in a terminology), the lexical group (to which strings of identical or near-identical lexical structure can be mapped), and the concept (to which strings of identical meaning can be mapped)"
- "The UMLS has probably had greater impact on biomedical ontology work than any other terminology effort. This can be attributed to its long history, its early focus on knowledge representation, and its free availability"
- 3.3 The Medical Entities Dictionary
- "...the (MED) is the controlled terminology developed by (Cimino) at Columbia University to provide a single unified coding system for data collected in the repository of the New York Presbyterian Hospital (NYPH) clinical information system"
- "Begun in 1988, it is, in effect, a 'local UMLS'"
- "The main purpose of the MED is as a coding system for data originating in various systems. However, the knowledge in the MED has been used to support a variety of functions, including summary reporting, automated decision support, terminology translation, and linking online health knowledge resources to clinical applications, including clinical applications at other institutions"
- 3.4 SNOMED-CT
- ""...is the result of a merger between two terminologies that each contained ontological information: SNOMED-RT (from [the College of American Pathologist] in the United States) and the Read Clinical Terms (from [the National Health Service] in the United Kingdom)"
- "Each of these terminologies has a long history of developing from simple lists of terms, through hierarchical representations, and...definitional knowledge about a subset of their terms"
- Uses descriptive logic, which ..."was not found useful when integrating SNOMED-CT with the...UMLS"
- "Interest in SNOMED-CT has increased dramatically in recent years. This is in part because of its availability free of charge in the US, but it is also viewed as being a comprehensive, high-quality terminology..."
- 3.5 LOINC
- "Developed...as an ad hoc standard for coding clinical observations in HL7 messages, the ontological approach taken in the development of the Logical Observations, Identifiers, Names and Codes (LOINC)"
- "...LOINC started as a terminology model for a very limited domain and then accumulated terms that were described by the model"
- "The knowledge in LOINC is expressed through the use of a structured naming system"
- "During it's initial development, researchers used the logical structure of LOINC terms as a way to translate local terms into LOINC for the purpose of sharing patient data"
- "The LOINC model has also facilitated identifying LOINC codes for local laboratory test terms, merging LOINC terms with other terminologies, and guidelines, as well as mapping other terminologies for nursing assessments and home health care
- 3.6 FMA
- "Developed and maintained by...the University of Washington, the Foundational Model of Anatomy (FMA) is a frame-based domain ontology that represents declarative knowledge about human anatomy. The FMA was specifically developed to provide concepts and relationships pertaining to human anatomical structures, with the intent of expanding the anatomical content of UMLS"
- "Instead of designing a terminology model to meet a particular purpose, three models were included in the FMA: an ontological model for representing classes of anatomical structures, a structural model for representing spatial and topological relationships, and a transformational model for representing morphological changes such as those that occur with development and aging"
- "In addition to its frame-based representation, FMA has been represented with DL... By representing FMA in DL, researchers have been able to take advantage of generic reasoning tools"
- "...a complete conversion of the FMA into DL overwhelmed the computational abilities of available reasoning systems"
- "The FMA knowledgebase facilitates content authoring, information presentation, automated student assessments, an injury propagation modeling environment, and a surgery simulator"
- 3.7 Gene Ontology
- "The Gene Ontology (GO) is a controlled biological terminology being created by a consortium of bioinformaticians...involved in genomics and proteomics"
- "...it is a fairly minimal ontology...The GO Consortium has focused instead on the task of creating and agreeing on the 'semantic concepts' of its domain"
- The GO Consortium is working to make associations between the ontologies and the genes and gene products in the collaborating databases"
- 3.8 ISO Reference Terminology Model for Nursing Diagnosis
- "The nursing informatics community has been particularly active with respect to terminology development, with over twelve terminologies just for the domains of assessments, interventions, goals and outcomes"
- "The ISO model for nursing actions contains six attributes (site, route, means, target, recipient of care, and timing), while the diagnosis model contains five (dimensions, focus, judgment, site, and subject) that, in turn, have attributes of their own"
- "Subsequent to its development, a number of studies have shown that the ISO model can be applied successfully to represent the various extant nursing terminologies"
- 3.9 NDF-RT
- "The Veteran's Health Administration (VHA)...as part of its Health Data Repository (HDR) project, has computerized a variety of clinical transactions, including physician orders and documentation. VHA's initial reference terminology project was National Drug File Reference Terminology (NDF-RT)"
- "The core of the NDF-RT model comes from the existing VHA database file, including approximately 400 VHA drug classes. The classes are legacy classes developed by the VA, informed by the US Food and Drug Administration's approved labeling"
- "...the NDF was expanded into a formal reference terminology (NDF-RT) that is being made freely available"
- "...approximately 4000 updates per month. The goal is for a large portion of routine maintenance (e.g. adding a drug with a defined set of ingredients) to be automated via a series of electronic transactions..."
- "NDF-RT is exerting its impact largely through its contribution to the RX norm project"
- 3.10 RxNorm
- "RxNorm, developed...at the National Library of Medicine, is a standardized nomenclature for clinical drugs that addresses both the lack of an adequate standard for a national terminology for medications and the need for formal organization of medication terms within the UMLS"
- "Thanks to collaboration with the Veterans Affairs work on NDF-RT, the content of RxNorm has quickly reached over 14,000 terms, each of which has full representation in the ontologic model"
- "The principled, nonproprietary approach has paid off: RxNorm has been recommended by the NCVHS as one of the standard terminologies for the core patient medical record information"
- "In addition, publishers of on-line health information have begun to index their material with RxNorm codes, facilitating automated queries using patient data"
- 3.11 NCI Thesaurus
- "The US National Cancer Institute...has developed a DL-based terminology called the NCI Thesaurus (NCIT), to support cancer research based on current biomedical science"
- "A major design goal of NCIT is to facilitate translational research, for example, to index clinical trials and document expert summaries"
- "Today, the NCIT contains 100,000 terms and 34,000 concepts, covering chemicals, drugs and other therapies, diseases (more than 8,500 cancers and related diseases), genes and gene products, anatomy, organisms, animal models, techniques, biologic processes, and administrative categories, including definitions and synonyms"
- 3.12 DOLCE+
- "The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE), developed...[in Italy], is a high-level, domain-independent conceptual framework for representing meaning"
- "...extended DOLCE with a Descriptions and Situations Ontology to create DOLCE+, which they applied to the task of disambiguation of medical polysemy in the semantic web"
- 3.13 Protege
- "Originally developed as a tool supporting the knowledge acquisition needed to represent cancer treatment protocols, Protege evolved into a general-purpose knowledge representation environment"
- "...it has further developed into an open source platform that uses standards such as the Open Knowledge Base Connectivity (OKBC) knowledge model, XML and RDF. These standards, in turn, have supported the development of a library of useful plug-ins. The most important of these is the OWL plug-in, which enables the creation and maintenance of ontologies described in the OWL syntax"
- "An additional application, the RACER system, is frequently used together with the Protege-OWL environment, as it provides reasoning services such as consistency checking and automated classification of concepts"
- "Protoge has been used by hundreds of research groups around the work and the Protege user community has more than 7000 members. Besides its original protocol-based use, it has been used to represent clinical guidelines and expert system rules"
- "Researchers have used it for managing GO and FMA, as well as for constructing ontologies in cardiovascular medicine, traditional Chinese medicine, hospital incidents, and congenital heart defects"
- 3.1 GALEN
- 4 Discussion
- "...a terminology includes explicit information about the meanings of its terms; this information, in turn, is used to help humans and computers to recognize the intended meanings of the terms for proper coding of, retrieval of, and inferencing about biomedical data, as well as for maintenance of the terminology itself"
- "The terminologies that have made the greatest strides...are those that focus on particular domains"
- ""The orientation of terminology development toward ontologies is due, in part, to the evolution of logic-based representations"
- "While the recent growth in ontologies has been encouraging, researchers and developers are now beginning to tackle the tougher domains, such as symptoms, physical findings, radiologic findings, and diseases"
- "Despite being, in some sense, in their infancy, biomedical ontologies have already had significant impact"
- "The agreement on terminology standards is crucial for the success of standardized messaging and health information systems"
- "Improvements in biomedical research and health care through information technology have lagged behind the progress made in other fields. The lag has been generally attributed to the complex, conceptual nature of biomedicine"
- "The approach of solving the conceptualization problem through terminologies, rather than the other way around, has led to the ontologic approach"
- "The next decade will show us if the approach will pay off in terms of improved health information systems, improved translation of research into practice, and, ultimately, improved health outcomes"
Links to recommended browsing sites listed with Week 5 (Monday) readings
- Protege 
- RxNav (link is slightly different from that listed on WebCT, but it will get you there!)
- Metadata Summary 
Lecture Summary - Dr. Hurdle - Monday, Week 5 (by Robin Palmer)
- The student who took the shortest amount of time to complete the quiz (5 min 46 sec) scored a 10
- The student who took the longest amount of time to complete the quiz (29 min 36 sec) scored a 9
- Mean was 8.9
- Take-away message: don't over analyze when answering the quiz questions
- Question #3 had the lowest correct number of responses (63%). Most users who implement an EHR incur immediate costs and no immediate savings (and may, in fact, NEVER see any $$ savings), most $$ savings go to third-party payers (through more efficient/electronic billing), it is difficult to assess the true cost of a system. Utilization of decision support tools benefits end-users the most. There is no equivalent to Microsoft in the EHR world.
- Data about the data (or information about the information)
- A computable thing, helps computers to process information
- Issues: who entered the data? is it accurate? how up-to-date is it? is it compatible between different systems?
- The National Library of Medicine has compiled a metadata schema for everything in their database: 
- One of the most cited references which attempts to define metadata: “Metadata is all physical data (contained in software and other media) and knowledge (contained in employees and various media) from inside and outside an organization, including information about the physical data, technical and business processes, rules and constraints of the data, and structures of the data used by a corporation.” (Marco, D., Building and Managing the Meta Data Repository: A Full Lifecycle Guide. 2000, New York: JohnWiley & Sons)
- A system used in Germany was reviewed: "3LGM²: Three-layer Graph-based meta model to describe, evaluate and plan health information systems" 
- "The What question: Objects in the world, with their properties, with their relations to other objects (also: events, processes, and states)"
- "Ontology - defining types of things and their relations; terminology - naming things in a domain; thesaurus - organizing things for a given purpose; classification - placing things into (arbitrary) classes; knowledge bases - asserts knowledge"
- UMLS Semantic Network is an example of an ontology in the biomedical domain 
- The NLM maintains "RxNav (a browser for RxNorm)", which is a "repository of standard names for clinical drugs" 
- Knowledge - "(Paraphrase from the [Shortliff] text, where it is mentioned 587 times!) Interpreted data, an integration of data, experience, and analysis"
- Knowledge-based reasoning - "Experiential-based, data-driven application of knowledge to reason about (in our case) a clinical situation."
- Knowledge Management - "Systems for organizing knowledge; especially to make knowledge computable"
- Why manage biomedical knowledge? - "volume of biomedical data, steep growth curve, care delivery is time constrained, computing with knowledge"
- Do clinicians use knowledge differently than other experts? - "Arocha and Patel argue yes: '…expert clinicians tend to work backwards from a diagnosis to fit the facts (e.g., data) rather than deducing a diagnosis from the facts. Not at all clear this is true of other clinical providers'" (J Biomed Inform. 2005 Apr;38(2):154-71 )
- Clinical experts versus novices - "Important: one size does not fit all…experts approach problem solving differently than novices (cognitive issue)"
- While ontologies hold the knowledge, software is used to "compute" the knowledge to permit mapping and drive decision support
- The "DIKW" Pyramid was reviewed (data, information, knowledge, wisdom)--an effective information system can hold data and information; some high-level systems can even be programmed for a certain amount of "knowledge", but achieving wisdom is reliant on the clinician (a future goal for EHR systems?)
- Examples of (health care) knowledge management systems: National Library of Guidelines , UMLS Knowledge Source Server , RxNorm , Protégé-OWL 
- Providing automatic decision support at the point of care has been shown to improve care, why don't more providers use it? One study showed that providers had 2 questions for every 3 patients they saw, more than 50% of these questioned remained unanswered. 85% of these questions could have been answered by on-line resources (aka "info buttons") available to the clinician at the point of service, yet less than 10% of clinicians used them. An interesting question: How can informaticists identify and remove barriers to using decision support tools so that clinicians will use the resources available to them at the point of care?
Reading Summary - Wednesday, Week 5 (by Susan Pollock)
CLAYTON - Building a Comprehensive Clinical Information System from Components: The Approach at Intermountain Health Care
Open architecture is a design in which multiple different individual software applications from a variety of sources are interfaced to create a central unified clinical repository.
- Allows users to see all patient data regardless of the application in which the data were collected
- Removes the need to enter redundant data into multiple systems
- Requires expertise and resources
- The costs of interfaces will be reduced in the future as standards for vocabulary and messaging become increasingly mature and functional
- Developers try to avoid too much granularity by using components that encompass an entire discipline (pharmacy, electrocardiography, nurse charting)
Monolithic architecture is a design in which the applications and the database are tightly integrated because all software applications have been developed using the same environment. The terms monolithic and integrated have similar meanings. A monolithic architecture is used by most vendors and many home-grown systems.
- Similar look and feel, similar database definitions, similar processes and structure.
- The presentation layer and the database layer are separated from the actual application code (services act as intermediaries between the application code and the database or the presentation layer). Therefore it is possible to make changes in the architecture without changing all of the programs.
- Long development time
- Any changes in the database structure or the presentation layer usually mean that all the application programs need to be changed.
- It is also extremely difficult to switch platforms or vendors without significant effort and simultaneous disruption of enterprise operations (so called big-bang approach to change).
- Generally have some interfaced applications (lab, scheduling)
- Monolithic systems are seldom 100% monolithic. It is less expensive to buy an existing system and interface to that system than to build something from scratch (the vendors do it this way).
Options for starting from scratch:
- 1 - Build everything from scratch
- Duplicates for a single site, the efforts a vendor can spread across many customers requiring excessive time and money
- 2 - Purchase everything from a single vendor (monolithic)
- Least expensive and least risky
- Vendors frequently go out of business
- Mediocre…no one is really happy
- 3 - Build a system from interfaced, disparate components
- Feed data into and retrieve from a common longitudinal, data repository
- Intermountain’s post-HELP1 choice
Intermountain’s System includes the:
- Master Patient Index which assigns a unique system-wide identifier to each patient
- Clinical Data Repository(CDR)a database which uses ASN.1 models to store clinical events both as an event object and in separate tables and is tuned for single patient queries
- Enterprise Data Warehouse a database which stores system-wide data and is tuned for population-based queries
- Heathterm Data Dictionary contains un-instantiated data models and NCIDs for all concepts
- HL7 interfaces running on eGate to facilitate table-driven translations and communication between components
- Inference Engine a rules engine that evaluates the patient’s status
- Clinical Desktop a Windows-based desktop user interface with a single sign-on (LDAP) that can call various applications
- Various home-grown data entry (ChartNotes) and results review tools (Results Review)
- Clinical Workstation is a thick-client app that allows the MD to enter data at the point-of-care and include a Message Log (clinic staff communication), Hot Text (narrative notes), Common Lists (dynamic, smart lists), and Info Buttons (answers to common questions).
- Its big (~1.5 million pts/369 GB storage) and handles many users
- Redundant back-up, fast, minimal down time
- Interfaces to 51 apps
- 4% of IS budget spent on interfaces and vocabulary
- More cost-effective over a 30-year period
- Flexible and scalable
- Allows incremental change
- High initial cost of interfaces…however, “the cost of choosing a monolithic system supplied by a vendor that does not stay in business far outweighs any interface costs”
- Data is stored in more than one place (this can be a good thing too)
- Duplicate master index numbers
- New genetic content
- Expand knowledge-based systems
STEAD - Integration and Beyond: Linking Information from Disparate Sources and into Workflow
- Ideas and techniques from all three generations that co-exist in successful modern projects
- The goal is to integrate information from a variety of sources into the way people work and to improve decisions and processes.
First generation - everything is self-contained; had to create databases;provided integration by using a single system for all functions
- Automated history taker (Duke 1970)
- Obstetrical medical record system (Duke)
- TMR project (dictionary and metadata)
Second generation - integration through enterprise architecture; enabled by LAN technology; single-user interface to multiple resources; each system a discrete unit; the collection of databases did not equal an integrated database
- StatLan (best-of-breed)
- HL7 standard defines the interchange format, messages, and triggers
- Visula Human dataset was one of the 1st standard datasets
- LOINC is another standard
- MCIS-1 architecture manages the data model, metadata,knowledge bases, and databases as enterprise information resources.
Third generation - data and knowledge that are outside a system or enterprise may be linked to the data and work processes that are within it
- Unified Medical Language System (UMLS)- contains the terms from source vocabularies,together with an explicit many-to-many mapping between terms
Transformation: From Generations to Dimensions
- Workflow—data capture,communication, visualization, decision support, role modification, and change facilitation.
- Structure—to represent data without ambiguity and support regularization of content and componentization of software.
- Extraction—exploring data to discover information or knowledge.
Each situation requires a different balance of ideas from the dimensions. For example, the techniques involving structure can be exploited wherever homogeneity is practical. Extraction is needed when crossing boundaries. An ideal scenario involves use of extraction to implement structure, obtaining the benefits of homogeneity while decreasing what has to be done inside the enterprise and preparing for a change in boundaries. Similarly, use of structure and extraction reduce the need for data capture, increasing the amount of effort that can be devoted to the residual data capture requirement....The use of structure and extraction can reduce the need for data capture
- Generation 1
- Source data capture
- Role-specific displays
- Decision support
- Engage customer directly
- Multi-use data models
- Metadata tables
- Attribute-value data structures
- Generation 2
- Interchange standards
- Information architecture
- Standard data
- Componentized software
- Generation 3
- Distributed knowledge
- Distributed work process
- Mark-up languages
- Object request broker architecture
- Relationship among data
- Approximation of structured data
- Machine learnong
- Data filters
Information Workflow Integration
- The critical factor in implementing clinical decision support systems is to do so in a manner that makes a measurable impact on patient care processes and outcomes.
- The work required to implement significant clinical systems, such as care provider order entry, at least 75 percent is in social engineering and only 25 percent is technical.
- By representing business rules and other information externally to any dedicated system, this collective content can be made accessible to all users, and the chaos created by version changes and vendor changes in dedicated systems can be diminished.
Ontologies: Data Representations to Support Linkages
- Distributed software architectures require a common data model and terminology
- An ontology is an explicit representation of the concepts that system builders define to exist in a particular domain
- Ontologies form the basis for human–computer interaction and for information exchange throughout the health care environment
- Ontologies allow software developers to conceptualize and formalize what they know about an application domain
- Ontologies permit modern component-based software systems to refer to a single, sanctioned description of
the types of data on which they operate
Extraction: Data Mining and Filtering
- Patient-specific information is information generated in the care of patients
- Knowledge-based information is the scientific literature of health care
- Data Mining is the use of historical data to discover regularities and improve future decisions. It is also known as knowledge discovery from databases(KDD).
- Access to primary literature is facilitated by better organization and indexing of content.
- The filtering of patient-specific data may allow more effective mining. Mining of filtered knowledge-based information may show us new directions for future research.
EHR Electronic Health Record (HIMSS) http://www.himss.org/ASP/topics_ehr.asp
- According to HIMSS, the Electronic Health Record (EHR) is a longitudinal electronic record of patient health information which is generated by patient encounters in any care delivery setting. The EHR:
- Includes patient demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data and radiology reports
- Automates and streamlines the clinician's workflow
- Has the ability to generate a complete record of a clinical patient encounter and supports other care-related activities directly or indirectly via interface(evidence-based decision support, quality management, and outcomes reporting)
EHR Electronic Health Record (CMS) http://www.cms.gov/ehealthrecords/
- According to CMS, an Electronic Health Record (EHR) is an electronic version of a patient’s medical history, that:
- Is maintained by the provider over time
- May include all of the key administrative clinical data relevant to that persons care under a particular provider, including demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data and radiology reports
- Automates access to information
- Has the potential to streamline the clinician's workflow
- Has the ability to support other care-related activities directly or indirectly (evidence-based decision support, quality management, and outcomes reporting)
- Are the next step in the continued progress of healthcare that can strengthen the relationship between patients and clinicians and will enable providers to make better decisions and provide better care by:
- Reducing the incidence of medical error by improving the accuracy and clarity of medical records
- Making the health information available
- Reducing duplication of tests
- Reducing delays in treatment
- Informing patients to take better decisions
Lecture Summary - Wednesday, Week 5 (by Melissa Bunker)
Class Presentations Review presentation guidlines (clinical decision support and end-of-term) posted on WebCT (Week 5 Wed Sept. 22). If working in a team, remember that each member must participate in each aspect of the project.
- Computer-based systems used to manage the medical and administrative information of the hospital
- used to perform tasks more efficiently
- systems include EMR, billing, pharmacy, radiology, and many more
- Clinical Subsystems: designed to collect, store and manipulate clinical information; has clinical decision support as a goal; designed to improve patient safety
- Administrative Subsystems: handle administrative aspects of the hospital
- Financial Subsystems: handle to daily costs of running the hospital; includes billing, accounts receivable, payroll, etc.
- Hardware: includes servers, disks, LANs, terminals, etc.
- Software: includes applications, security, communications
- Staff: IT, vendor, clinicians, administrators, etc. Staff are key to integration.
- Data acquisition and presentation
- Record keeping and access
- Communication and integration of information
- Surveillance and quality measures
- Information storage and retrieval
- Data analysis
- Decision Support
Academic version of Cerner PowerChart shown(https://cernaes.cernerworks.com/UTAH/auth/login.aspx) Instructions posted in WebCT (Mod2EHRAssignment Instructions in Week 5 Sept 22) for those interested in playing with this system (virtual environment with test patients)
- Accessed by login: logins act as electronic signatures, important for determining who does what, can be used to set up role-based access, support HIPAA in that user activity within the system can be tracked, justified, etc.
- Longitudinal: provides a picture of patient's visits through time (even when information is entered into different systems)
- Data coding: problem lists are common coded data; users can search within terminology sets (ICD-9 codes, SNOMED, NANDA, NIC, NOC, CPT, etc.). Think about how to support clinicians who don't want to use these tools.
- Infobuttons: allow access to additional information (link to PubMed); IHC uses context-specific infobuttons; some systems do not have external links.
- Forms: allow for structured data entry/documentation; allow for multiple structured views: flow sheets, clinical notes, etc.
- Medication List: a major function recommended by IOM to promote patient safety surrounding medication administration; required for a fully functional EMR. PowerChart has barcoded meds, built-in ties to Micro MedEx, performs drug allergy checks, preforms drug-drug interactions checks. If a user attempts to order a drug where the patient is allergic, system provides an alert and requires user to cancel order or override system (justification for order required). An example of clinical decision support at the time it's needed.
- CDR Clinical Data Repository: day-to-day HIS operations; application-oriented; many users entering a larege amount of raw and current data in incremental amounts; requires fast transactions (users need access to information now)
- EDW Enterprise Data Warehouse: analytical; all data from HIS subsystems merged; organized around subjects of use to special interest groups; used for research; historical data; few users pulling large amounts of data; slower transactions (users can wait hours or days for information)
Options (for building an HIS) Build from scratch (ACIS at UU, VA, IHC); buy commercial off-the-shelf (Cerner); buy "best of breed" modules and link them; use a combination of all these approaches
Considerations Approach (oftern determined by organizational culture); costs; functions needed (clinical, administrative, financial), satisfaction (multiple users across multiple applications/ functions); maintenance needs; regulatory requirments Group exercise in designing an HIS. Take home: There is no single best answer. Each decision represents a trade-off between two or more considerations.
- Maintenance ~75% of cost over system lifetime
- Staff like current system and change is really painful
- Vendor options carry risk of vendor might fold
- Reliability needs are extreme
- Research Support (IRBs, Data warehouses)
Week 6: HIS case studies: the VA, Intermountain Healthcare, and the UUHSC
Reading Summary - Monday, Week 6 (by <your name here>)
Lecture Summary - Monday, Week 6 (by Danielle Sample)
Michael Strong, MD
Chief Medical Information Officer University of Utah
Background of the EMR System at the University of Utah
Pre-1996: -ACIS (Ambulatory Care Information System) was developed 'in house' at the University in 1996
-Primarily used as a results review tool
-Search for EMR Vendor Began (Committe formed, narrowed down field) -Decided on OACIS -Wanted a 'best of breed' environment -The project ended up falling through as a result of the downfall of partnerships -Lessons Learned: Size matters, being an alpha site (test site) is very risky, interfacing is costly and time consuming, a highly configurable product is very difficult, especially in an academic setting (it almost always takes longer and costs more to implement than originally thought)
-New EMR Search Committee Formed -Building was viewed as too expensive and takes too long -Decided to buy core functions then customize
- The idea of the need for integration had not sunk in yet
-Narrowed selection down to EPIC, IDX/PICIS, and CERNER -Weighed Pros/Cons of each -Decided on Cerner- purchase the majority (about 80%) and use their main install, then customize the other 20%. -Proposed a kick-off in 13 months (August 2005)
-The project turned into an in-house building of the system because so much customization was needed. -Took much longer- new kick-off date was supposed to be March 2008- this was extended due to infrastructure instabilities (now they are glad they put it off!) -Official kick-off: May 2009...very successful
-A pilot program in the Burn Unit for a few weeks before kick off was very helpful to solidify everything and fix last minute glitches
(Burn unit chosen because of its complexity of patients, specialization, small department size, and willingness of staff)
As a Result:
-Hospital Mortality Index has decreased (not solely attributed to this launch, but a good sign that it did not increase) -Decrease in pharmacy variable cost per discharge -Decrease in radiology variable cost per discharge -Decrease in adult length of stay time
-The new efficiency of documentation by nurses allowed by this system has already saved tens of thousands of dollars (specific, complicated documentation is needed to be able to bill for certain things- this is made much easier by the system = hospital saves money)
-There were a few minor glitches that are still being workedout, but overall the launch was extremely successful
-more/new work for physicians -workflow issues -new kinds of errors -over dependence on technology -All minor issues (expected when implementing a huge emr system!)
Next Steps and The Future:
-CPOE in Same Day Surgery -Implementation of EPIC in Outpatient Clinics -Experiencing frustrations with Cerner: -EPIC is the newly favored system (received a very high review compared to Cerner in KLAS and new CEO likes EPIC better anyway) -EPIC is going live in clinics November 1 (this includes all billing, registration, and scheduling departments...very big deal)
-In the future, EPIC is going to slowly phase out Cerner in all systems -Web Overlay System- expected launch date of June 2011 -Continue working on improving the current Cerner System (it will still be around for at least 5 years so optimization is still a work in progress and a concern) -Work on Clinical Documentation/Building Power Notes -Knowledge Management is an issue for the future: structured documentation, presenting data in easy to assimilate ways, decision support, and personal medicine are all considerations -Clinical Decision Support at the point of care is one of the biggest concerns/goals. -Continuing challenges include funding (as always) and the challenge of being an academic institution (much more specialized and hard to customize a system for- compared to business or financial institutions)
- This presentation was exceptionally interesting, I would recommend reviewing the slides from it (on webct) if you missed it!
Reading Summary - Wednesday, Week 6 (by <your name here>)
Lecture Summary - Wednesday, Week 6 (by <your name here>)
Week 7: HIS case studies: the VA, Intermountain Healthcare, & the UUHSC; Evaluating clinical information systems
Reading Summary - Monday, Week 7 (by <your name here>)
Lecture Summary - Monday, Week 7 (by <your name here>)
Reading Summary - Wednesday, Week 7 (by Rachel Bergvall)
CSC: Meaningful Use for Hospitals- the Top 10 Challenges by Metzger, Drazen and Bell
HITECH provisions of ARRA (2009)
- Provide $45 billion in incentives for providers and hospitals
- After 2015, provide penalties
- The incentives and the penalties are both contingent upon success or failure at "meaningful use" of an implemented EHR.
- This article:
- Describes meaningful use as applied to hospitals
- which includes a table of the criteria, or individual goals, arranged by topic and by year required (2011, 2013 or 2015)
- and lists 10 key challenges salient to the achievement of those goals.
- Describes meaningful use as applied to hospitals
Meaningful Use- discussion of, as relevant to hospitals
What it is
- Meaningful use refers to a set of requirements approved by the HIT Policy Committee.
- They are informed by five National Health Policy Priorities, which are listed in a sidebar on page 1
- There are quite a lot of them, with many more to come, and they are pretty specific.
the implementation timeline has 3 stages (3 dimensions of meaningful use, you might say) -
- 2011: data capture
- 2013: integrating the EHR into processes
- 2015: achieving improved outcomes
The table fills slightly more than one page of this 9 page article. It is very informative and I recommend you refer to it directly. I am hesitant to hazard a summary of something which itself is summarizing a source matrix spanning 10 pages. Here, though, is a list of the domains listed in the table, to give you a sense of the scope. All of these areas are subject to some requirements in at least one of the three stages of implementation; most have multiple goals, at all stages:
- Computerized Physician Order Entry
- Medication Reconciliaton
- Physician Documentation
- Nurse and Interdisciplinary Documentation
- Performance Improvement
- Performance Measurement/ Reporting
- Health Information Exchange (HIE): Pharmacies
- HIE: Patients
- HIE: External Providers
- HIE: Public and Private Payers
- HITECH Privacy and Security
10 Key Challenges These are prefaced by two brief dicussions:
- barriers to meaningful use
- lots o' change
- new emphasis on outcomes: improvement and documentation of it (as addressed in 11/3 lecture by U of U Quality Team!)
- all this change, really fast- 2 yrs for each stage- that's not long in implementation process terms
- reasons for optimism:
- these standards provide structure and context to a process which previously lacked it.
- performance expectations of EHR system are specified- this should help with product selection
- the clear requirements save time in implementation process by eliminating some other ruminations- mandatory features are not up for internal debate
- certification of EHR systems is being expanded, refined and accelerated to accommodate the expectations.
- The trail has been blazed- each new implementation can learn from those that went before
- The financial incentives are significant.
OK, now the 10 challenges:
- No do-overs, apparently.
- "...past experience shows that getting CPOE right on the first try is important because subsequent attempts are much harder." This intriguing statement gets no elaboration; I'd like to know more about why this is. Anyway...
- Authors go on to cite necessity of strategic implementation and respect for the massive changes involved
- The physician buy-in.
- Major increase in documentation required of providers.
- medication reconciliation
- allergy list
- current problem list
- providers' use of hospital EHR has often been voluntary in the past; cannot be, for compliance
- their input should be incorporated early and consistently in the adoption process.
- Major increase in documentation required of providers.
- Get appropriate, effective CDS in the CPOE.
- use of the right tools (order entry sets emphasized here)
- integrate this with the quality department
- consider other benefits of order sets
- Start the inpatient record in the ED
- Roughly half of inpatient stays really start in ED; the status quo has been to silo the two records (ED and inpatient) which has associated problems such as poor communication of ED staff findings and redundant information gathering
- recommendation: Get the ED into an EHR, and get that linked to the inpatient record.
- New strategies needed to handle newly required info
- Med rec, allergy list and problem list all must be maintained for clinical usefulness
- This requires assignment of responsibility (process specification), as well as the tools to handle the sharing of these between departments
- Need to access data for Quality Measurments
- This will require more coded data...
- but coded data documentation can be much more time consuming for clinicians
- so balance is needed.
- "How to approach the coding issue is probably the biggest challenge."
- Improve clinical data analytics
- Many hospitals have been putting this off
- Multiple needful uses for analytics
- advise investment in skills and processes as well as systems
- Electronic data sharing expectations
- the most unexpected aspect
- push to share data with both patients and other providers
- much effort needed to package it in usable terms for patients
- New privacy/ security standards
- meaningful use= more electronic data= more potential security risk
- HIPAA, 10 years on, still a challenge for some organizations
- start with a frank assessment of security status
- ICD-10 transition
- This deadline not until 2013- but:
- wise to consider the changes when implementing the nearer-term documentation changes- because
- documentation facilitates billing coding.
- also, planning ahead can prevent a "two-step transition".
- (authors believe ICD-10 will be allowed in 2011.)
Next, there's a boxed list of "10 guiding principles". They are succinct and highly readable; reference directly. (It's advice like 'start now' and 'consider clinicians', but not quite that succinct.)
Summary- hospitals should follow the spirit of the law rather than the letter, and go beyond bare minimums to implement a system that truly optimizes patient care at their organization.
Use of EHR in U.S. Hospitals - NEJM Special Article- by Jha et al. (2009)
Authors identified a lack of reliable estimates for EHR adoption rates at US hospitals; surveyed all American Hospital Association member hospitals to achieve a reliable estimate; and examined relationships between EHR adoption rates and
- specific hospital characteristics and
- perceptions of various factors as barriers or facilitators of adoption
They concluded that
- current adoption rates are dismally low, and this poses a significant challenge for policymakers and for hospitals
- money is seen as the biggest barrier, and the best facilitator
- policy should include financial resources, interoperability strategy and provisions for tech support training to be successful
US health system challenges
- rising costs
- inconsistent quality
There is potential for HIT and EHRs to help. These have bipartisan support. (ARRA is referenced.)
EHR have slow adoption despite widely recognized benefits... but how slow?
Prior estimates have ranged from 5%-59%, undermining credibility. The variance can be attributed to:
- non standardized definitions of EHR
- use of convenience samples
- low survey response rates
ONC-HIT and DHHS commissioned this study to begin reliable serial measurements of adoption (plan to monitor trend).
EHR defined, per key functionalities
- survey development discussed
- the thorough process included:
- review of past 5 yrs similar surveys' instruments
- survey experts' input
- input from CIOs and other hospital leaders
- then, review by a concensus panel including experts from
- health services research
- survey research
- health policy
- final instrument approval granted by IRB of Partners HealthCare.
- survey sent to all AHA member acute care general medical and surgical hospitals
- sent to CEOs as supplement to annual member survey
- commonly delegated to CIO or equivalent authority
- calls and letters employed with nonrespondents
- 63.1% responded (n=3049); after excluding federal hospitals and hospitals located outside 50 states + D.C., n=2952.
- survey period= Mar-Sep 2008
- which of [these 32 clinical functionalities] does your hospital have?
- do you have them in
- all major clinical units
- at least one unit
- or no units?
- which of [this list of factor]s do you consider major; which minor; which not a barrier?
- which of [this other list] would you consider to have a positive effect on EHR adoption; which a negative effect?
With expert input, designated which EHR components needed for comprehensive and basic systems. Definitions established:
- Concensus that comprehensive= specific 24 of the 32, in use in all units
- Less concensus for basic- were able to agree that at minimum
- needed specific 8 of the 32, in at least 1 unit, to be basic system
- some argued that clinician documentation should be added (nurse assessments and physician notes) for total 10 component functionalities
- The main analysis required the clinician documentation (the 10/32 version) to qualify for "basic system" inclusion.
Statistical analysis- described in comprehensive detail.
Respondents and Nonrespondents
- were compared by
- private for profit
- private nonprofit
- teaching status
- major teaching hospital
- minor " "
- non" "
- member of a hospital system vs freestanding
- urban vs nonurban
- presence or absence of "high-tech institution" indicators. (CCU presence chosen as representative indicator.)
- Modest differences were found.
- All reported results were adjusted for nonresponse bias.
- The high-tech indicator showed the biggest differential- 25% of non responders vs 35% of responders.
Not including VHA hospitals:
- 1.5% met comprehensive EHR definition
- 7.6% more met basic EHR definition (the stricter basic, with nurse and MD notes)
- including VHA hospitals improves the numbers, especially the comprehensive: up to 2.9% for comprehens., 7.9% for basic
(Note: major variation in adoption of various components- lab and radiology applications most common, adopted at about 75% of hospitals.)
Hospitals more likely to have an EHR are: (or, qualities that positively correlate with EHR adoption are:)
- major teaching hospital
- part of a larger hospital system
- and have a CCU. (High-tech indicator.)
Quality categories with poor correlation to EHR use are:
- private/ public
- for-profit/ nonprofit
The most commonly cited barriers to EHR adoption
- among hospitals without EHR systems:
- inadequate capital- 74%
- maintenance costs- 44%
- physician resistance- 36%
- unclear return on investment- 32%
- not enough staff with adequate IT expertise- 30%
- hospitals with EHR were less likely to cite any of these- except physician resistance.
Major incentives cited:
- by hospitals with EHR:
- increased reimbursement for EHR use- 82%
- financial incentives for adoption- 75%
- tech support available for implementation- 47%
- 3rd party evaluations of EHR products available- 35%
- hospitals without EHR were:
- less likely to cite first two
- same citing of second two.
- about 9/10 US hospitals are without even basic EHR
- some isolated pieces fairly common (lab, rad)
- pretty common to have something "in the works", or at least say so
- this still leaves a lot of work ahead
- CDS was reported more often than CPOE
- probably the excess are referring to a pharmacy CDS.
- "thereby overstating the preparedness of hospitals to provide physicians with CDS for patient care".
- definition used here for basic system is pretty lenient; this limits likelihood of associated quality increases
- financial incentives seem to be promising as policy.
- success at the VHA in both adoption and quality improvement noted
- study limitations:
- potential for nonresponse bias cannot be entirely eliminated. Nonresponders probably less likely to have EHR.
- measured adoption- not actual use, nor effectiveness
- systems in use not assessed for certification status
- low adoption levels limit understanding of adoption of (comprehensive vs basic) systems.
- satisfaction of users of EHR not assessed.
Conclusions- see above.
Lecture Summary - Wednesday, Week 7 (by <your name here>)
Week 8: FALL BREAK!
Week 9: The Utah Master Patient Index Project; Clinical decision support system case study
Reading Summary - Monday, Week 8 (by <Sunghee Lee>)
Building an Enterprise Master Person Index (AHIMA Practice Brief)
Enterprise master person/patient index (EMPI)
- A software application that identifies persons in an integrated delivery network (IDN) across disparate registration, scheduling, financial, and clinical system
- Have been used in healthcare since 1990s.
- Interest in EMPI has markedly increased due to the shift to a more customer-centric focus in,
- Healthcare operations
- Consolidation of healthcare organizations
- Implementation of electronic health records
- A need to define the population being served
- Healthcare operations
- Many vendors and healthcare organizations have switched to a person index as opposed to a patient index.
- Financial, marketing, and customer satisfaction initiatives rely heavily on the basic premise that an organization can accurately identify customers being served, determine frequency of customer use of specific services, and determine profitability of services.
- May assist organizations in addressing HIPAA patient identification and tracking requirements
Types of EMPIs
- Vendor-neutral or ‘best of breed’
-This implies that the EMPI can be integrated readily with any other vendor application.
- Core vendor EMPI
-An inherent or add-on module to other vendor applications, and the EMPI does not readily integrate multiple disparate systems.
Active / passive mode
EMPIs are commonly deployed in either an active or passive mode using existing Health Level Seven (HL7) message, with additional data requirements being defined during the vendor selection process and implementation.
Initially launch in a passive mode and then migrate to active mode.
(Initial business goals, timelines, and budget will determine the deployment method for any given organization.)
- EMPI is at the front end of the registration or scheduling process
- Patient identification is undertaken using the EMPI software – requires integration of the EMPI and the legacy systems
- User will identify the patient from an enterprise or corporate level, and at a select point in the identification pathway
- User drops to the facility level registration or scheduling system
- This process is generally transparent to the user
- EMPI is at the front end of the registration or scheduling process
- Passive ;
- Does not directly impact the registration or scheduling pathway
- Identification is undertaken behind the scenes or on the back end of the registration function
- Thresholds are established whereby a person is automatically linked with or merged to existing data (if the threshold is not met, the registration data is held in a work queue for later resolution.)
- Does not directly impact the registration or scheduling pathway
Both methods should have the ability to identify persons at a corporate and local level, as initial deployment would involve loading all databases to facilitate initial corporate and local level identification and duplicate identification.
- Duplicate – more than one entry or file for the same person in a single facility level MPI
- Overlap – more than one MPI entry or file for the same person in two or more facilities within an enterprise
- Overlay – one MPI entry or file for more than one person (i.e., two people are erroneously sharing the same identifier)
- Used for identifying potential duplicates or overlays and for linking various identifiers across the enterprise is a critical component of a successful MPI solution
- Must be sophisticated, powerful, flexible, and accurate
- Types of matching algorithms
- Deterministic (or “exact match”); used in most hospital legacy information systems. Name, birth date, gender, and social security number. 20-40% accuracy.
- Rules-based (sometimes known as ad hoc weighting); more sophisticated technique. “fuzzy logic”. Assign weights, or significance values. Accuracy is varies with ranges of 50-80% of the potential duplicate record.
- Probabilistic ; most sophisticated technique. 90% or higher accuracy.
- Deterministic (or “exact match”); used in most hospital legacy information systems. Name, birth date, gender, and social security number. 20-40% accuracy.
- Data has integrity if it is complete, accurate, and consistent.
- A clean MPI contains only one record, or a unique identifier, for each person
- The following all affect the quality of MPI data;
- Decentralized registration
- Converted data
- Lack of standards
- Lack of staff training
- Difficulties associated with registering laboratory specimens
- Accepting data from physician offices without verification procedures
- Decentralized registration
Methods and thresholds
- The duplicate error rate describes the quality of the EMPI data
- Error rate is calculated by the total number of duplicate records by the total number of records, multiplied by 100.
- Error rate assigned to the EMPI based on the file size and the number of duplicate records identified
- A threshold measure is used to interpret comparison scores – the score is the result of the comparison between two records
- When scores are above the upper threshold, the records are assumed to represent the same person and an enterprise identifier is automatically assigned.
- The threshold reflects the trade-off between potential incorrect linkages within the EMPI and possible duplication.
EMPI Data Overview
- Data Elements
- Accurately match persons with their single EMPI record
- Facilitate access to longitudinal (lifetime) patient records
- Facilitate linkage with clinical data repositories, pharmacies, and laboratories
- Improve access to patient information resulting in significant benefits for patients and healthcare providers
- Accurately match persons with their single EMPI record
- Recommended Data Elements to be included in EMPIs by AHIMA
- Enterprise identification number
- Facility identifier
- Internal patient identification
- Person name
- Date of birth
- Alias/previous/maiden name
- Social security number
- Telephone number
- Enterprise identification number
- The issue of data ownership is a potentially difficult one that organizations must address early in their planning process, particularly if the corporation that is purchasing the EMPI software license does not own all the participating facilities or sites
- This challenge is further complicated by the implementation of HIPPA, as organizations must consider the relationship between covered entities, organized healthcare arrangements, business associates, land the obligations for disclosure of information to patients.
Maintaining the EMPI
- AHIMA recommends that the responsibility for EMPI maintenance be centralized under the direction of HIM professionals.
- A comprehensive maintenance program should include;
- Ongoing process to identify and address existing errors
- Advanced person search capabilities for minimizing the creation of new errors
- Mechanism for efficiently detecting, reviewing, and resolving potential errors
- Ability to reliably link different medical record numbers and other identifiers for the same person to create an enterprise view of the person interfaces and correction routines to other electronic systems that are populated or updated by the EMPI
- Ongoing process to identify and address existing errors
- Adequate staffing is needed to maintain and ensure the quality of the EMPI.
- Staff members should have the authority to resolve duplicates, investigate demographic overlays, and link persons across the enterprise
- A working knowledge of EMPI, facility-level MPI, registration procedures, and duplicate correction procedures is recommended
Education and training of EMPI staff
- Training should be performed before staff begins the resolution activity
- Staff should receive in-depth training to perform the identification, research, and resolution of duplicates
- Each staff member should be able to demonstrate competency in the areas of duplicate identification and resolution
- Ongoing education should also include feedback from a well-defined quality monitoring program
- Updates to the training program should be performed periodically and should be based on a review of the initial training to incorporate system modifications and upgrades and internal process improvements
Lecture Summary - Monday, Week 8 (by <Sean Igo>)
UTAH STATEWIDE MASTER PERSON INDEX by Scott Narus, Ph.D. DBMI, U of U
This was one of the stimulus-funded grants when money came available; Dr. Narus put the grant together very quickly. Dr. Narus credits luck and timing.
An ambitious project! With far-reaching consequences for clnical work!
- What does MPI do?
- Uniquely identifies members of service population
- Resolve multiple conflicting identities
- Create a longitudinal record - i.e., all records for the same actual person linked
- Ensure compliance w/privacy/confidentiality rules
- Possibly prevent fraud/abuse
- people present to many different systems and may have different or mis-entered identities at different places; the MPI tries to resolve that. The only way to tie it all together is with a master patient index;
- also different people might have the same name and you don't want to share private/confidential data with the wrong one.
- people may also be dishonest and trying to defraud or get illicit prescriptions; MPI might help notice that.
Different systems that might participate within one enterprise: possibly multiple EMRs like how we have Cerner/EPIC at the U; LIS; RIS / PACS; ADT for registration. These might interact or might all have separate inputs. All may have different ID numbers for the same patient, possibly different versions of the name (e.g. Jim/James) and/or misspellings.
Knowing a bit more about the patient, like their age and occupation and such, helps to reconcile the different records.
- active/passive - active - if we're using the MPI directly at registration, that's active; if we let the local system collect and later MPI batch-processes it, that's passive mode. Trend is toward active to positively ID people right away; currently passive still faster at intake time when timing may be critical e.g. emergency.
- Messages e.g. HL7 ADT - communication among all the modules e.g. LIS / PACS / ADT. ADT = Admit/Discharge/Transfer
- Data Standardization - name, address, phone number might be reported in several ways, like 1200 E. or 1200 East or Street vs. St.; Data Standardization attempts to use one standard format.
- Match, Link, Resolve
- Match: for a new record, what does it match among existing record? may match to some degree with multiple records; if it matches above a certain threshold, we call it possible match. When in doubt, throw it to human experts who are slower but better.
- Link - construct database pointer or similar links between matching records
- Resolve - what out of all the linked records is the best information - best estimate of birthdate, address, phone number, etc.
- Duplicate, Overlap/Tromp Record - Some fraction of records in the MPI are duplicates, like when we have incomplete/incorrect information or for other reason the duplication isn't recognized. However, we may find what we think is a duplicate patient which really isn't so we incorrectly merge / erase. If we incorrectly overlap, we could provide the wrong treatment or distribute confidential data incorrectly. We would rather err on the side of duplication. Duplication might create divergent histories and we'd lose information about history, like we might not see that they have an allergy and you'd give them the wrong drugs. Overlaps also much harder to fix than duplicates.
- Merge / Unmerge - merge is the process for combining records we believe to be the same. Keep some history about how it was done so that we could back it out with an unmerge if we later find it shouldn't have been done.
- Matching Algorithms - the heart of figuring out how records match. Active research topic, many vendors
brag about the quality of their matching.
Data elements in MPI:
- Demographic: Name(s), DOB, Sex(?), Address(es), Phone(s), Email address(es), Race/Ethnicity
- we need things that distinguish people. In the case of sex, it may be undifferentiated at
birth, it can actually change; have to be clear about whether it was at birth, genetically, current, etc.
- Race / Ethnicity is tricky; not always accurate, not well collected, useful in certain research
- Identifiers, internal and external:
- internal e.g. local radiology number, encounter number, in-system ID of people
- external e.g. SSN, driver's licence, passport #; these can be good but can be shared or mis-entered
a "good" number depends on how it's distributed, whether they're correctable (like has a scheme for spotting transposed digits like Luhn code, etc.) Insurance numbers?
- MPI number not currently used as a personal ID, just a database number
- Relations - family, providers, insurance, emergency contact;
- knowing e.g. that someone's mother was Person X narrows down who they are.
- emergency contact not necessarily good for automated ID, but humans can call and ask.
- Biometrics - fingerprints, retina, iris, DNA (once it becomes faster), pictures - not used much yet.
- Encounter information - say there were multiple people with similar names and info; can ask the
patient if they've been in recently, and can link based on that. This is a bit iffy because it edges into private/confidential information. Not usually recorded in most MPIs.
- Clinical information e.g. if you're diabetic, cancer survivor, etc. Could differentiate people but
again a privacy / confidentiality concern.
Statewide MPI project
- 2-year NIH Grant Opportunities (GO) grant, $2.7M
- Started end of September '09
- U of U BMI, UPDB, HIM, IRB/RGE, Legal, IT Security
- IHC - large patient population database
- Utah Dept. Health - for public health reasons and has several local MPI projects with
millions of instances of patient data
- UHIN - cHIE integration - Utah Health Info. Network - runs the local information exchange
and has recently started clinical data. Want to integrate MPI with cHIE as our final goal.
- Uniquely identify individuals seeking health care/ public health. Support research, operational, and public health purposes. This is unique among state MPIs to support 3 aims like this.
- Establish guidelines for the use of MPI - policy committee among the members listed above and
other community members
- develop MPI repository and supporting services - the technical details
- test MPI in a research setting - developing use cases
- Develop integration strategy for cHIE - overall aim
- Contributors e.g. providers, public health, those with their own MPIs. We're not trying to
be the local MPI for everyone. As they create / update records, they send them to statewide MPI via a set of public services available to known/approved members.
- under the public services layer is a security layer that validates their access.
- under the security layer is an access layer that allows reading and writing of records.
- Data consumers, i.e. people who want to find out exactly which John Smith they're looking at.
- Administrative users who can do manual review, de-tromp records, etc; they are still subject
to security checks but can bypass the public services layer.
- Similar with researchers who want to use data for informatics research, more direct access.
Might also use the public access if they're not researching the mechanics of the MPI itself, but rather with the kind of data accessible to public users.
- see diagram.
- Audit services - track accesses for security purposes
- Services for matching, linking, standardization of data, publication services that can
automatically broadcast certain kinds of activity
- consent management; records whether patient has given permission for certain types of access
- workflow services govern the path any message / piece of data follows through the system
- Knowledge Base (KB) / Terminology services - analyzes / determines appropriate algorithms
for matching etc.; also aids standardization of data & terminology.
FURTHeR = gateway for accessing all these services
Important concepts for sMPI:
- "Master of Masters" - every local MPI which is the master index for all the local services,
sMPI is a master over them. It means to link records among these smaller MPIs and link/resolve to produce the best possible "golden record" which most accurately describes the individual.
- "Slave of Masters" - sMPI doesn't originate any records; the local systems/MPIs do that, so it
is only a consumer of data (though it may refine and update data.) We want to rely on the local systems to do much of the matching / linking / merging so we only worry about linking across local systems.
- currently sMPI only has permission to use demographic information, which isn't quite enough
to do the best matching. Local MPIs know e.g. encounter, provider, etc. information so they do a better job of matching within their systems.
- open architecture: adoptable by other states, usable by whatever institutions want to
- Transaction based; as records are added or updated, generate a message, instead of doing
e.g. monthly batch fetch and process. Keeps things as up to date as possible, support active MPI use at the local level or even use the sMPI in active mode.
- how fast is it in active mode? It hasn't been tested yet. Depends on DB size, connection quality,
matching algorithm speed; so, overhead + processing time. Theoretically, could be a few seconds, but it is not yet known. That's for next year.
- should be flexible enough to support active or passive use within a single provider.
- sMPI does not (at least initially) store clinical or encounter information.
- Consent: we intend different uses, like clinical and research, that have different consent
patterns and needs for tracking consent. Can we, statewide, say that we don't have to get consent, if a person has registered at a provider or public health, to send their data for matching purposes to the sMPI? Seems OK for HIPAA, still looking into it.
- Research vs. Operational use policy
- can a provider then use that sMPI record for clinical operational purposes? Do we have to let the patient
choose which providers are allowed to see their demographics?
- how can it be used for research? We believe that will be covered through normal IRB
processes. Again, under consideration.
- Sustainability - long term, how do we keep it running?
- Ownership / governance - none of the partners want to own it, because of liability and cost issues;
Might do a non-profit organization that is specifically organized to govern it, a la UHIM (?) How do we collect input about use cases people want, features, and so on.
- Management: central vs. distributed - day-to-day operations, who does manual resolution of tricky merges, &c.
- Financing - how do we pay for it once the grant runs out? Subscription model? Transaction-based fee?
Don't know yet. Legislature says not to expect long-term state money or mandate.
How did they manage to get all the stakeholders to agree to all this so quickly?
- Grant money; provided for a lot of local projects, e.g. fraud/abuse detection for IHC, and also
helping them reduce costs due to tromps and such.
- Some are still a bit balky, even within the University. There is not yet complete agreement about
use consent, policies, management, etc.
- There is broad agreement.
- Difficulties: recent security breach during the time the sMPI people were going to talk to the Legislature;
weighs on people's minds. Contributors want a safe harbor rule s.t. if there is a breach outside their facility of information they contributed, they can't be held liable. Also, contributors are concerned about their responsibility in money / time to make sure their records work properly, how soon do they have to have things ready, etc.
Milestones (see slide, this isn't all of it)
- Market survey of commercial MPIs - can we buy pieces? Looking into it. Matching algorithms might
be supported with at least partial commercial software.
- Internal testing - allows for testing without concern about real-world security, so can test things
like performance of the system
Q&A: Utah Population DB is on the grant team. They have their own very good MPI statewide, and is a very useful comparison resource for things sMPI wants to do. It was created just for research purposes, so can't be used operationally. Also updated infrequently, so their model is not entirely appropriate for sMPI. May be useful for gauging match quality in sMPI.
UPDB has some legislation behind it wrt permission/consent; can sMPI do something similar? There is a current piece of legislation which created the all-payer DB; refers to a state-designated sMPI, we're working with legislators to say that this project IS that sMPI. Also looking into other avenues to legitimize contribution & use of records and legal safe harbor rules.
What is the financial incentive/motivation for institutions to participate in this? e.g. from IHC, they said it can cost like $60 or $100 per record that they have to de-duplicate, so they believe that sMPI-type intelligence would reduce those costs. Also once they're tied into cHIE, they expect a flood of duplicate records so are very interested in systems to decrease those costs. Others might look at it altruistically, just in the interest of better treatment; might lower health-care cost through non-duplication of lab tests (though that doesn't make any money for the participant). sMPI can help with privacy / confidentiality, reducing costs associated with revealing data improperly, such as disclosure and legal costs. From the research perspective, allows researchers to span systems, we can apply for future grants for research purposes since it's such a unique resource. Any participant in cHIE for clinical information exchange should be interested in a related MPI; if this becomes part of the cHIE it may be rolled into the cost for that.
What kinds of fraud/abuse was IHC worried about and how does sMPI help?
- person trying to use someone else's insurance; demographics could spot they're not the right age, etc.
- people looking for illicit prescriptions - maybe someone uses different names but same address to get
script sent there; that could pop up and spot them.
There was much competiton for GO grants - why did we get the funding for this project though there were likely similar applications from elsewhere?
- Dr. N doesn't know how many similar ones there were; but our experience in MPI development, especially
among the partners like UPDB and IHC with its enterprise-wide thing; UDOH, and cHIE which needed an MPI; that environment made it clear there was experience here. Also since the use case for both operational and research applications. Plus the commitment to give it back to the community as open architecture / open source which can help other states to do it too.
Clinical decision support system case study #1 (by <Sean Igo>)
Clinical decision support system case study #2 CPOE (by <Robin Palmer>)
- Definition and a brief history of CPOE
- Pros & Cons
- Barriers to implementation
- Recommendations for successful implementation
- Why should informaticists care?
- Computerized physician or provider order entry ("physician" is used most often by CMS)
Where did it come from?
- Birthplace: El Camino Hospital in Mountain View, California
- Concept development began in 1971 (although had been discussed since 1969)
- Funded by a grant (and assistance) from Lockheed Martin
- Originally included no decision support, eventually evolved into Eclipsys system with decision support
- Was upgraded to a Windows-based system on March 25, 2006
- Med errors can be reduced by 29%-96%
- Pharmacists greatly benefit: the amount of time required for pharmacist verification of orders can be reduced by an average of one to two hours; the number of medication errors requiring clarification dropped from 2.8% to 0.4%
- Ability to normalize critical K+ values within 24 hours improved from 50% to 70% of patients 
- Implementation of "best practices" through the use of standardized order sets 
- Institutional cost savings (for example: Brigham & Women's Hospital saw a net savings of $9.5 million over 10 years)
- There is no regulatory requirement for system testing; untested systems may miss up to 50% of routine med errors, and up to 33% of fatal errors
- Users may become dependent upon an electronic system, and chaos can result in the absence of sufficient back up systems
- "E-iatrogenic" (health care caused) events - "patient harm caused at least in part by the application of health information technology"
- Campbell, et al. identified eight categories of concern, including more work for clinicians, undesirable workflow issues, and problems related to paper persistence
Barriers to Implementation 
- Cost: $10-$30 million (or more) for a large hospital
- Physician/provider issues: difficulty training providers, "physician rebellion", potential negative impact on provider workflow
Recommendations for successful implementation
- From Poon, et al. study: patient safety must be driving reason; when addressing physician/provider issues - utilize pressure from outside sources (such as CMS, Leapfrog, etc.), identify physician/provider champions, careful selection of vendors to provide customization to meet end-user needs, enlist hospitalists
- Ash, et al  identified twelve areas of emphasis, including: must be fast, must meet info needs of users, provide "seamless access" to other systems through CPOE, do not underestimate financial costs, acknowledge tradeoffs (and emphasize value to end users), leaders must be on board, ensure "help at the elbow" during implementation, must have strong "Foundational Culture", collaborative project management to include all stakeholders, agree upon a common language (avoid terminology with negative connotations), improve CPOE through evaluation and refinements, consider motivation and context
- Have an appropriate number of work stations
- Test CPOE system with an outside evaluation tool
Why should informaticists care?
- According to the Leapfrog Group, implementing CPOE nationwide would: save $9.2 billion per year, avert 3,105,021 adverse drug events
- HITECH Act and Meaningful Use begin in 2011, requires CPOE, financial incentives for hospitals and providers who implement CPOE early (and eventual penalties for not implementing)
Week 10: Clinical decision support system case studies
Clinical decision support system case study #3, Monday (by <your name here>)
Clinical decision support system case study #4, Monday (by <your name here>)
Clinical decision support system case study #5 CDSS for Nurse Change of Shift Report, Wednesday (by <Julie Martinez>)
1. Benson, E., Rippin-Sisler, C., Jabusch, K., & Keast, S. (2007). Improving nursing shift-to-shift report. Journal of Nursing Care Quality, 22(1), 80-84. This paper describes a reconfiguration project for Winnipeg Regional Health Authority with a final framework consisting of a definition, principals and guidelines for nursing shift to shift report. A “Resource Tool Kit” was developed containing various documentation tools that could be used as worksheets. Nurses were educated with in-services and written materials as well as posters. Results of efficacy were not reported.
2. Caruso, E. M. (2007). The evolution of nurse-to-nurse bedside report on a medical-surgical cardiology unit. MEDSURG Nursing, 16(1), 17-22. The process of change is complex and usually filled with challenges. To guide the change process, a conceptual framework was needed. Lewin’s change theory was used to guide the change from an already-established practice of report in the nurses’ station to report at the patient’s bedside. During the first meeting with the implementation group, a template of information to include during report was created to ensure a consistent report format (see Figure 2). Initially the template was to be completed by the nurse at the end of the shift and passed to the oncoming nurse. Nursing staff currently completed individual report sheets and expressed the opinion that using an additional form was redundant and time consuming. The implementation group members believed report template was critical to ensure safe, effective, consistent communication. They decided to keep the template and ask the nurses to follow this format for report without completing an additional form. The report template was laminated and given to all nurses as well as posted at the patients’ bedside to allow for easy access during report.
3. Dowding, D. (2001). Examining the effects that manipulating information given in the change of shift report has on nurses' care planning ability. Journal of Advanced Nursing, 33(6), 836-846. To investigate the effect of manipulating the style (retrospective vs. prospective) and content (schema consistent vs. schema inconsistent) of nurse change of shift report has on the ability to plan patient care. Sample of 48 nurses measured ability to recall information as well as plan care. Results indicated the type of report had significant effects on recall and planning.
4. Dracup, K., & Morris, P. E. (2008). Passing the torch: the challenge of handoffs. American Journal of Critical Care, 17(2), 95-97. Increasingly, transfers of patient care from one person to another are recognized as a point of vulnerability in the process of care; a time when valuable information can and often will be omitted or garbled, leaving critically ill patients at high risk for an error to occur. Generally speaking, the more caregivers who are involved in patient care in a serial fashion, the higher the risk to patients. But what exactly is a handoff and how has it become such a point of vulnerability?
5. Lamond, D. (2000). The information content of the nurse change of shift report: a comparative study. Journal of Advanced Nu This study examines the role which the nursing change of shift report may have in aiding nurses to process information and plan care. It also aims to identify whether any of the information found in the shift report can be considered as 'forceful feature' information, the key features of a situation which allow an individual to access appropriate knowledge within their long-term memory store. The content of the medical notes, nursing documentation and shift reports for a total of 60 patients, selected from two acute medical and two acute surgical wards across two National Health Service Hospital Trusts in south-east England were subjected to content analysis. The types and amount of information contained in each source were examined, along with the order of information given in the shift reports. A multidimensional scalogram analysis (MSA) was also carried out on the data to examine the patterns of information content across sources. In general, more information was recorded in the patients' notes than communicated during the shift report. However, both the frequency data and the MSA plots indicated that particular types of information (identified here as global judgements) were often communicated in the shift report but not recorded in the patient notes. The results suggest that there is evidence that the change of shift report contains 'forceful feature' information. The presence of such 'forceful features' may facilitate the processing of patient information during the shift report communication, leading to more efficient care planning.
6. Matic, J., Davidson, P. M. and Salamonson, Y. , Review: bringing patient safety to the forefront through structured computerisation during clinical handover. Journal of Clinical Nursing, no. doi: 10.1111/j.1365-2702.2010.03242.x This review aims to examine critically, the methods and modes of delivery of handover used in contemporary health care settings and explore the feasibility of a computerized handover system for improving patient safety. Clinicians play a critical role in promoting patient safety, and the handover ritual is recognized as important in exchanging information and planning patient care. To date, the focus of research has primarily been on the vehicle of the handover, rather than the content and processes involved in ensuring the reliability and quality of clinical information. Employing a computerized handover system in the clinical arena has the potential to improve the quality and safety of clinical care. Whilst the handover performed from shift-to-shift is a valuable communication strategy, ambiguities and incomplete information can increase the risks of adverse events. Given the importance of effective communication, its key link to patient safety and the frequency of nursing handover, it is imperative that clinical handover undergo increased scrutiny, development and research.
7. Nelson, B. A., & Massey, R. (2010). Implementing an electronic change-of-shift report using transforming care at the bedside processes and methods. Journal of Nursing Administration, 40(4), 162-168. The electronic shift report template developed by bedside nursing staff using TCAB methods and processes led to sustained improvement in the change of-shift report process both from a qualitative and quantitative perspective in a comprehensive cancer center. Nurse satisfaction with the information being received improved, and the process became more focused and timely. Cost savings were also achieved. This staff-driven process, an outcome of dissatisfaction with the previous process, resulted in improved teamwork and a new format and process that spread across all inpatient units in the organization. This process and the outcome have underscored bedside nurses’ abilities and motivation to identify and make improvements that benefit practice and operations when they have the tools and support to do so. The format and information fields of the final shift-to shift report template will be integrated into the electronic medical record system in development within the institution.
8. Raines, M., & Mull, A. (2007). Give it to me: the development of a tool for shift change report in a level I trauma center. JEN: Journal of Emergency Nursing, 33(4), 358-360. The SBAR technique was used to develop our tool, which we titled “SBAR Shift Report” (Figure 1). Overall, the Shift Change Report Tool has been a positive performance improvement tool, especially for nurses new to the emergency department. Compliance with the JCAHO hand-off reporting rule also has increased with the use of this form. Increased satisfaction verbalized by the nurses receiving report, along with increased satisfaction expressed verbally by patients regarding their being kept informed about the plan of care and who is caring for them, has made the tool a worthwhile change endeavor in our department.
9. Staggers, N., & Jennings, B. M. (2009). The content and context of change of shift report on medical and surgical units. Journal of Nursing Administration, 39(9), 393-398. This study was conducted to describe the current content and context of change of shift report (CoSR) on medical and surgical units and explore whether nurses use computerized support during the CoSR process. Bedside, face-to-face, and audiotaped CoSRs were audiotaped and observed on 7 medical and surgical units in 3 acute care facilities in the Western United States. Conventional content analysis revealed 4 themes: the Dance of Report, Just the Facts, Professional Nursing Practice, and Lightening the Load. Observations exposed the lack of content structure, high noise levels, interruptions, and no use of electronic health records in these facilities as a part of the report process. Improvements to CoSR include determining a consistent and tailored structure for report, evaluating types of report suitable for particular units, reducing interruptions and noise, and determining content amenable to computerization.
10. Strople, B., & Ottani, P. (2006). Can technology improve intershift report? What the research reveals. Journal of Professional Nursing, 22(3), 197-204. Shift report is a multifaceted process that serves to provide nurses with vital patient information to facilitate clinical decisions and patient care planning. A shift report also provides nurses with a forum for functions, such as patient problem solving and collaboration. The authors conducted a literature review, which indicates that current methodologies used to collect and convey patient information are ineffective and may contribute to negative patient outcomes. Data incongruence, legal implications, time constraints augmented by the nursing shortage, and the financial impact of shift report are also addressed. The literature reveals significant rationale for pioneering new and innovative methods of shift-to-shift communication. In the report To Err is Human: Building a Safe Health System, the Institute of Medicine attributes the deaths of up to 98,000 hospitalized Americans to medical errors, including communication failures [Institute of Medicine. (1999). To err is human: Building a safe health system. Report by the Committee on Quality of Health Care in America. Washington, DC: National Academy Press]. As a result, government policy makers and health care agencies have focused their attention on determining the root cause of errors to identify preventative measures, including the use of information technology [Institute of Medicine. (2004). Keeping patients safe: Transforming the work environment of nurses. Report by the Committee on Quality of Health Care in America. Washington, DC: National Academy Press]. Under these premises, the authors examined the process of nursing shift report and how it impacts patient outcomes. The use of computer technology and wireless modes of communication is explored as a means of improving the shift report process and, subsequently, health care outcomes and patient safety.
Clinical decision support system case study #6, Wednesday (by <your name here>)
Week 11: Overview of translational informatics; Quality Issues
Reading Summary - Monday, Week 11 (by <Deena Farmer>)
Biomedical informatics and translational medicine Biomedical Informatics and Translational Medicine – Indra Neil Sarkar
Translational medicine – (innovation, validation, adoption), (bench to bedside to community to policy) Barriers – T1 (between bench to bedside), T2 (between bedside to community), T3 (between community to policy)
Biomedical informatics – (molecules and cells, tissues and organs, individuals, populations), (bioinformatics, imaging informatics, clinical informatics, public health informatics) The translational medicine continuum aims to improve upon existing practice through research moving from “bench to policy”. Translational barriers that are met at each step of the process (innovation to validation to adoption) may be more easily overcome with the integration of biomedical informatics. The author stresses that unsuccessful research can be as relevant as successful research. As we discussed in lecture, we can learn from our (or others) mistakes and improve upon methods, not do doublework, etc. and move forward collectively toward future policy adoption.
Biomedical informatics when married with translational medicine is best divided into two areas: translational bioinformatics and clinical research informatics. Sarkar says that the key to this is to enhance current methodologies, not to come up with new ones. Translational bioinformatics is especially usefull in crossing the T1 barrier with clinical research informatics assisting in crossing T2 and T3. Both are “knowledge-centric” vs. “patient-centric” thereby meeting the “myriad of research and information needs of basic science, clinical, and public health researchers”.
Decision support applications must do at least one of the following: knowledge acquisition, knowledge representation, inferencing, and explanation. Such systems exist in many forms in bioinformatics; adoption in translational medicine will be best accomplished by multidisciplinary teams using these activities to move from bench to policy.
Natural language processing is generally either understanding (human to machine) or generation (machine to human). Again, biomedical informatics has numerous applications. Translational informatics has a quickly growing need for natural language processing due to the increasingly common use of electronic health records. Additionally, bridging various electronic records, articles, communications, etc. will require tools to do so quickly, efficiently, and accurately.
Standards are key for the correct “representation of data” and ensures that such data “can be readily exchanged with other systems”. Standards developed and used in biomedical informatics will be useful when applied to translational informatics but some new ones will be needed for “specialized domains (eg cancer and neuroimaging)”.
What is the point of having a database if it is difficult to extract the relevant data easily? Information retrieval applications will assist in identifying and retrieving the relevant data. And again, with the explosion of electronic sources and the sheer volume of data available to researchers and clinicians – getting what you want, and only what you want, in a timely manner is significant.
Electronic health records add to the available data and allow for more “genotype” and “phenotype” data. Translational medicine can use this to move bench and bedside research to community practice and eventual policy.
Translational medicine will benefit by having biomedical informatacists included in their multi-disciplinary teams. These professionals will interject their unique experience and training and will work together with all involved (in fig. 2 of the article the biomedical informatician interfaces with biologists in bench phase, clinicians and clinical researchers in the bedside phase, epidemiologists in the community phase and health services researchers in the policy phase) in order to continually advance medicine and health.
Lecture Summary Monday, Week 11 (by <Robin Palmer>)
Overview of translational informatics - presentation by Dr. Hurdle & Ms. Davis
Translational Science - What is it?
- Getting science from "the bench to the bedside" (and back...)
- Classic View: Basic Science to Clinical Research (must cross the "T1" junction), Clinic Research to Clinical Practice (must cross the "T2" junction), Clinical Practice to Policy (must cross the "T3" junction), Policy to Dissemination (must cross the "T4" junction)
- "..80% of translational science [involves informatics]" JF Hurdle, InfoFair Translational Informatics review, Apr 2006
- Translation research has accelerated since 2004
- “It is the responsibility of those of us involved in today’s biomedical research enterprise to translate the remarkable scientific innovations we are witnessing into health gains for the nation... At no other time has the need for a robust, bidirectional information flow between basic and translational scientists been so necessary.” Elias Zerhouni, MD, NIH Director under President Bush, NEJM, 353:1621-1623, October 13, 2005
- “…the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data into proactive, predictive, preventative, and participatory health…The end product of translational bioinformatics is newly found knowledge from these integrative efforts that can be disseminated to a variety of stakeholders, including biomedical scientists, clinicians, and patients.”
- Keys are information, computation & communication
Examples of Translational Informatics
- New workflows
- Interoperable data repositories
- Data Pooling
- Integrated ontologies and knowledge bases
- Integrating data collected in research studies with patient clinical data
- The "omics" (genomics, proteomics, etc)
- (above from Burgun A, Bodenreider O., Accessing and integrating data and knowledge for biomedical research.Yearb Med Inform. 2008:91-101)
Clinical Research Informatics
- Researchers finding patients: post a flyer (old way), use EMR to find suitable patients (such as UPDB)
- Patients finding researchers: see a flyer (old way), go to clinicaltrails.gov (better way)
- ERICA: automated IRB
- Can you use PubMed or Google to facilitate research?
- caGrid - allows sharing of data, tools and computing power across institutions
- caTrip - Cancer Translational Research Informatics Platform (see slide #15 for an example of how this is used in the caGrid system)
- i2B2 - "Informatics for Integrating Biology with the Bedside"
- open-source software, free clinical database repository, has self-service query and analysis tools
- divided into interrelated "hives"
- REDCap - Research Electronic Data Capture
- web based, inexpensive
- has 12,973 users, 4,230 studies, 163 institutional partners
- see slides #20-21 for example
- Research Match - voluntary registry for research participants, free recruitment tool for research teams (see slides #22-24 for example)
Reading Summary - Wednesday, Week 11 (No Readings This Day)
Lecture Summary - Wednesday, Week 11 (by <Robin Palmer>)
Quality Issues in Healthcare by Carol Hadloc, John Areqo, and Steve Mitchell, Quality Department, University of Utah Hospital
- Describe how Healthcare reform impacts quality
- Define Data Reports that impact Healthcare quality
- Demonstrate the importance of "IT" in health reform
- Increasingly, quality will be the focus, based upon improved care, patient satisfaction, and improved efficiency
- Evidence-based medicine is a key component
- Never occurs by accident; it is the result of "high intention, sincere effort, intelligent direction, and skillful execution" (William A. Foster)
- Cumulative effects due to regulations (such as CMS and OSHA), benchmarks (such as Society of Thoracic Surgeons), and reporters (such as Hospital Compare and Health Grades)
- US News & World Reports - uses data purchased from Medicare only on top 9 diagnosis, 30% of score based upon reputation (problem: scores solicited from doctors nationwide, have they heard of your hospital??), uses old data
- Health Grades - uses same data purchased from Medicare, also only on top 9 diagnosis, uses 36 month rolling data, for-profit organization (for example, would cost UofU $80,000 to purchase data); consumers can purchase data on individual MDs for $12.95 per report. Example shown of 2011 Pneumonia Health Grades shows St Mark's Hospital and LDS score highest (5 stars) in three categories (initial hospital care, 1 month, and 6 month follow up). UofU scored 5 stars in initial hospital care, but only 3 stars at 1 month and 6 months. UofU does not participate in this report.
- Hospital Compare - available on the internet to everyone at no cost, uses old data, based upon Core Measures, allows side-by-side comparison of up to 3 hospitals at a time (see slide for example, or better yet, go to the website yourself 
- University HealthSystem Consortium - "an alliance of 107 academic medical centers and 241 of their affiliated hospitals representing approximately 90% of the nations non-profit academic medical centers"; looks at Core Measures, readmission rates, mortality, 9 patient safety indicators, as well as patient satisfaction. UofU currently ranked in first place, an improvement from 31st place in 2009 and 50th place in 2008.
- Improve patient outcomes (consistent, evidence-based)
- Reduce cost of care (minimize errors, more efficient)
- Meet Standards/Regulations (Joint Commission, etc.)
Examples of Quality Issues
- "7% of hospital patients experience one or more serious medication errors"
- "CDS estimates 90,000 deaths from Hospital-Acquired infections in the USA"
- Quality Improvement Process: PDCA (plan, do, check act), Lean, Fishbone (see slide for example related to inability to discharge patients by 1100), Process Mapping
- Healthcare Improvement: coding, documentation, clinical improvement, UHC data
- If you can measure it, you can manage it
- If you can't measure it, you can't manage it
Possible Projects for Informaticists & Students
- Identify patients with conditions "Present on Admission" (POA), new for 2011, part of expected mortality calculation
- Related readmissions - used to be measured regardless of reason, new guidelines look for same dx/procedure within past 30 days (how can those be identified and teased out?)
Week 12: Clinical research iinformatics; Consumer health informatics and personal health records
Reading Summary - Monday, Week 12 (by <your name here>)
Clinical Research Informatics: Challenges, Opportunities and Definition for an Emerging Domain
Lecture Summary - Monday, Week 12 (by Julie Martinez)
Clinical Research Data Management by Bernard A. LaSalle
Is really a home for skeptics, people with OCD, agnostics and non-believers. All data is suspect, guilty until proven innocent. On the other hand biomedical informatics is the personification of the glass is half-full. Optimists are everywhere and rich data sources are everywhere waiting to be discovered.
I have come to believe that these two areas of science (ideologies, practices, cults) are not two ends of a spectrum but part of a circle. They are not at odds – they are symbiotic.
The Main Objective To have a discussion about five of what I consider being key areas in clinical research data management. While it is impossible to attain any real proficiency on any of these areas in an hour the real goal is to increase your level of awareness of some key issues in regards to data management and clinical research and how this might contrast with biomedical informatics research in terms of its scope and practice.
I could have presented a very specific use case of a clinical trial which would have provided some information from a context you may not be familiar with and required you to think about in terms of your own background. I have chosen instead to present the elements that I believe are the key parts of the clinical research domain along with some specifics but hopefully keeping it recognizable from the context you are in now – biomedical informatics.
What is Clinical Research? As a point of reference some definitions are required: What is clinical research – the National Institute on Aging (NIA) has a good document defining clinical research and providing a glossary of terms required to understand what clinical research is. (1) Patient-oriented research. (2) Epidemiologic and behavioral studies. (3) Outcomes research and health.
More to the point – what types of clinical research are there?
www.nia.nih.gov/NR/rdonlyres/D95A1337-6340-4C95-96F9-BD3FD3C0F03/0/NIAGlossaryofClinicalResearchTermsFINAL.doc This web site summarizes this question.
It is worth noting that understanding what something is, and even how it works – is not the same as knowing how to use it or do it. Perhaps you are familiar with "Outliers: The Story of Success” written by Malcolm Gladwell. "The emerging picture from such studies is that 10,000 hours of practice is required to achieve the level of mastery associated with being a world-class expert in anything. In study after study of composers, basketball players, fiction writers, ice skaters, concert pianists, chess players, master criminals and what have you, the number comes up again and again. Of course, this doesn't address why some people get more out of their practice sessions than others do. But no one has yet found a case in which true world-class expertise was accomplished in less time. It seems it takes the brain this long to assimilate all that it needs to know to achieve true mastery." So whatever you’re interested in doing exceptionally well is going to require real commitment over a period of several years.
Types of Clinical Research
Phase I trials: Researchers test an experimental drug or treatment in a small group of people for the first time. The researchers evaluate the treatment’s safety, determine a safe dosage range, and identify side effects.
Phase II trials: The experimental drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase III trials: The experimental study drug or treatment is given to large groups of people. Researchers confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the experimental drug or treatment to be used safely.
Phase IV trials: Post-marketing studies, which are conducted after a treatment is approved for use by the FDA, provide additional information including the treatment or drug’s risks, benefits, and best use.
Phase IV trials often involve using an approved drug or device for which safety data already exists, applied in a different way than in the original trial(s).
The National Institute of Mental Health (NIMH) has a good description of the different types of clinical research. I would also mention briefly that within the clinical trials domain there are several sub types: Retrospective – observational studies 1) Case series – simple descriptive studies 2) Case control – retrospective studies that look backward to explain the presence or absence of an outcome 3) Cross-sectional – analysis of data collected at one time rather over a period of time Prospective studies 1) Cohort studies – defined groups 2) Placebo controlled studies 1) Single blind studies 2) Double blind studies 3) Randomized studies 4) Self-control studies
Phase IV Example: It turns out that statins have an effect on survival rates for people infected with seasonal flu. So what’s going on? Is there some biological explanation for this? It appears there is and the common link is that influenza often kills due to our immune system’s response to inflammation. Statins reduce inflammation. What does this mean? 1. Statistically 2. In terms of the general population? 3. Personally
Actual rate of fatalities (during hospitalization or in the first month afterwards) was 3%. That means that the statin using cohort’s fatality rate was 1.5% If you start extrapolating that out to the a large number of people the numbers start becoming significant. So should everyone be taking statins? What could it hurt during flu season? What about the side effects of the drugs that provides some benefit?
Data vs. Information Data is all around us but what is it? Qualitative data is descriptive – quantitative data is a measurement
Data at its most elemental level are symbols. By understanding the context in which the data are collected they now become informative (information). Someone’s weight provides no information about their health unless we know their height and compare their BMI to standardized BMI categories. We still may not know about their overall health but we would know whether or not they were under weight, normal weight, overweight or obese. Information understands the relations between data.
Information is only the beginning – it is informative because of its relation to other data or information. If collected appropriately information becomes knowledge. By collecting the appropriate information we can test the hypothesis that allows us to make certain inferences with a known degree of probability (confidence).
However, unless we understand how a device, drug or clinical method works we have no ability to infer its value under a different set of circumstances. This may explain why we repeat so many studies, procedures, trials etc. We are gaining knowledge but perhaps not enough understanding. Knowledge comes from understanding patterns of information. Wisdom comes from understanding principles
Data Sources The stakeholders for any clinical research study might be larger than you think and are almost always larger than the PI thinks. Anyone interacting with the system and/or receiving data from the system will have to be thought about in terms of data management. Each of these stakeholders have roles in the data management process that often overlap even though they are different. Just creating a security model can be problematic and politically dangerous for the data manager. Should an investigator be able to enter/change data? A recent development is direct data entry by study participants – Who controls access, assigns and recovers passwords? The DSMB wants to remain blinded but they need to compare the results of different study arms – How do you handle this? Sponsor is defined as an individual, company, institution or organization that assumes responsibility for the initiation, management or financing of a clinical trial.
Understanding In a clinical trial, for example, successful data management requires that you understand the research protocol, the statistical analysis plan, the CRFs, the study visits and the data and safety monitoring plans. Once you have this information you can start modeling a database that will collect the data necessary to support each stakeholder. You have context for all of the data elements.
Domains There are several knowledge domains that can be part of a clinical research project. These are listed as examples for illustration but certainly are not a complete representation of all of the possible knowledge domains that you may encounter when conducting clinical research. There is an emergent domain called Clinical Research Informatics Clinical research informatics: challenges, opportunities and definition for an emerging domain. Embi PJ, Payne PR. J Am Med Inform Assoc. 2009 May-Jun;16(3):316-27. Epub 2009 Mar 4.PMID: 19261934 [PubMed - indexed for MEDLINE] Data and Safety Monitoring Board (DSMB) – Determines stop date based on safety, efficacy and futility
Clinical Domains The clinical domain includes clinic visits, hospitalizations, clinical laboratory results and all of the ancillary services that are part of standard care. A person may start out as a knee transplant patient in the morning and by noon they are a study participant in a pain study to measure the effectiveness of a new drug or delivery system.
Medical Records, clinical labs, imaging reports are often the source document in clinical research. A source document is a document in which the data for a clinical trial is first recorded. Data is usually copied from these to a case report form (CRF). This is particularly true with adverse event and hospitalization reporting.
Having digital access to medical records to verify and validate CRF data would significantly reduce the amount of time spent processing data request queries (DRQs).
Research Lab Domains Congress passed the Clinical Laboratory Improvement Amendments (CLIA) in 1988 establishing quality standards for all laboratory testing to ensure the accuracy, reliability and timeliness of patient test results regardless of where the test was performed. http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/IVDRegulatoryAssistance/ucm124105.htm
Academic research institutions often have investigators who are running lab tests developed in their own labs on specimens collected in clinical research. While the results of these tests are scientifically valid they may not be used in a clinical or diagnostic setting i.e. you cannot inform a study participant of a diagnosis or condition based on non-CLIA laboratory results. You can only suggest that they should seek the same test in a CLIA approved lab.
Example: You are conducting a clinical trial involving cancer patients and one of the exclusion criteria is that women cannot have the presence of the BRCA1 cancer gene.
When you explain to the participant that they are not qualified for the study you cannot say that it’s because they have the BRCA1 mutation. Other examples might include animal studies that use human specimens.
Accuracy vs. Precision Although this topic is generally associated with research laboratory procedures it applies any clinical measurement. If a measurement is extremely precise (repeatable) but not accurate then it is of no scientific value. Conversely, imprecise measurements will create too much “noise” to be statistically useful. An example of a common data collection instrument is the Visual Analog Scale (VAS) for Pain. This is a line 10 centimeters long which is marked once by the participant. The distance is measured in millimeters/100. Two people should be measuring these scales. Each person would measure the VAS three times (precision) and their measurements would be compared (accuracy). They should establish both inter-operator (accuracy) and intra-operator (precision) before starting the trial.
Regulatory Domain • Institutional Review Board – reviews all research involving human subjects. IRBs are local (generally) and have wide jurisdiction on what is appropriate research. • Data and Safety Monitoring Boards – are almost always required in multi-center clinical trials. They are responsible for the safety, efficacy and ethical conduct of the trial. • Clinical Cancer Investigative Committee – reviews all research protocols involving oncology. • Radioactive Drug Research Committee – reviews all protocols using radiation and isotope labeled drugs. • Food and Drug Administration – reviews all new uses of investigative drugs and devices • Office of Human Research Protection oversee compliance of HHS regulations
Administrative Domain OSP requires information on all contracts and subcontracts and details regarding study budgets, including restricted categories. Res Acct – requires annual reports regarding budgets, expenditures and justifications for research funds spent. uTRAC – University Tracking of Clinical Research - Management features include Medicare coverage analysis (MCA), protocol billing grid (PBG) and budget building, clinical care coding and research pricing, clinicaltrials.gov protocol publication, research participant tracking, and sponsor invoicing.
TCO – is responsible for protecting the intellectual property rights of the University and the university faculty and staff. This includes royalties, patents and the sharing of information and technology with entities outside of the University.
Analysis Domain Database schema can be somewhat cryptic to end users like biostatisticians. Depending on the nature of the data some databases can have dozens even hundreds of tables. In addition to data tables there are meta data tables and reference tables that are part of the data set(s).
In practice we almost never use 1NF data tables, many 2NF tables and some 3NF tables. Contrast this with a data warehouse where all of the tables are in 3NF or higher.
Database “snapshots” are absolutely required for interim analysis. Once a study is done and the data have been verified and validated the database is locked. No changes or corrections can be made. Data sets form this type of database are only created once. Interim analyses are used for DSMBs, the FDA and other purposes including some publicaitons. Creating datasets from an active database is not reproducible unless a “snapshot of the database is created to work from instead of the live database. These snapshots are kept as part of the study archive.
Standards and Guidelines Health Insurance Portability and Accountability Act – 1996 Protecting Personal Health Information in Research: Understanding the HIPAA Privacy Rule: http://privacyruleandresearch.nih.gov/pr_02.asp Protected Health Information - PHI is individually identifiable health information transmitted by electronic media, maintained in electronic media, or transmitted or maintained in any other form or medium. PHI excludes education records covered by the Family Educational Rights and Privacy Act, as amended, 20 U.S.C. 1232g, records described at 20 U.S.C. 1232g(a)(4)(B)(iv), and employment records held by a covered entity in its role as employer.
Covered Entity - A health plan, a health care clearinghouse, or a health care provider who transmits health information in electronic form in connection with a transaction for which HHS has adopted a standard.
Coded Federal Regulations CFR 45: http://www.wedi.org/snip/public/articles/45CFR160&164.pdf CFR 21 part 11: http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfCFR/CFRSearch.cfm?CFRPart=11 CFR 21 part 50: http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=50 Anyone involved in clinical research should have copies of the CFRs. You will be surprised at how often you refer to them and how many people involved in clinical research do not know about them. Table 4. Brief history of United States legislation encouraging or requiring pediatric studies in the United States Year Legislation Pediatric studies required or optional with 2 years 1984 Waxman–Hatch Amendments to Drug Price Competition and Patent term Restoration Act Optional 3 years exclusivity granted for supplements for “new population” such as pediatrics 1994 Pediatric Labeling Rule Required 1997 FDAMA Section 111 Optional with 6 months exclusivity granted if submission fully meets provisions of FDA Written Request 1998 Pediatric Rule Required. FDA enjoined by US District Court, District of Columbia from enforcing this rule in 2002 2002 Best Pharmaceuticals for Children Act Optional 2003 Pediatric Research Equity Act Required – extends to 2013 More Guidelines http://csrc.nist.gov/publications/PubsFIPS.html FIPS 201--1Mar 2006Personal Identity Verification (PIV) of Federal Employees and Contractors FIPS-201-1-chng1.pdfFIPS 200Mar 2006Minimum Security Requirements for Federal Information and Information Systems FIPS-200-final-march.pdfFIPS 199Feb 2004Standards for Security Categorization of Federal Information and Information Systems FIPS-PUB-199-final.pdfFIPS 198--1Jul 2008The Keyed-Hash Message Authentication Code (HMAC) FIPS-198-1_final.pdfFIPS 197Nov 2001Advanced Encryption Standard fips-197.pdffips-197.psFIPS 196Feb 1997Entity Authentication Using Public Key Cryptography fips196.pdffips196.psFIPS 191Nov 1994Guideline for The Analysis of Local Area Network Security fips191.pdfFIPS 190Sep 1994Guideline for the Use of Advanced Authentication Technology Alternatives fip190.txtFIPS 188Sep 1994Standard Security Label for Information Transfer fips188.pdffips188.htmlfips188.psfips188.txtFIPS 186--3Jun. 2009Digital Signature Standard (DSS) fips_186-3.pdffrn-fips_186-3.pdfFIPS 185Feb 1994Escrowed Encryption Standard fips185.txtFIPS 181Oct 1993Automated Password Generator fips181.txtFIPS 180--3Oct 2008Secure Hash Standard (SHS) fips180-3_final.pdfFIPS 140--3Jul 13, 2007DRAFT Security Requirements for Cryptographic Modules fips1403Draft.pdfFIPS 1 40--2May 2001Security Requirements for Cryptographic Modules fips1402.pdfFips140-2.zipfips1402annexa.pdffips1402annexb.pdffips1402annexc.pdffips1402annexd.pdfFIPS 140--1Jan 1994FIPS 140-1: Security Requirements for Cryptographic Modules fips1401.pdfFIPS 113May 1985Computer Data Authentication (no electronic version available) ordering-pubs.html
Practical Best Practice You must be able to control access to any data management system. To do this you have to have a security model that includes all stakeholders. There are various methods available to do this but like all IT solutions it depends on a well defined process before you start coding.
Relational databases are made up of several objects (rows, tables, views, triggers and procedures). Generally access to these objects is governed by roles. Members of a particular role all have the same level of access. Roles can be created by the database administrator. Many relational databases only restrict access to the table level. If you want/need to restrict access to the row level you need a model that employs additional controls.
Clinical Research Process Typically, in investigator initiated research, the protocol, approval applications, consent forms and case report forms are all being created simultaneously. This makes it very difficult to create a data management system when everything is in a state of flux. Since the database is the underpinning of the entire enterprise even small changes in any of these higher level entities can require large changes to the infrastructure.
Ask what the research question is: Is Female Sexual Dysfunction Related to Pelvic Floor Disease? Hypothesis: There is no difference in the prevalence of FSD between women with and without PFD. Every database activity must have a well-defined process. This is the hardest part of clinical research management. It requires not only domain knowledge but the effort to walk through each process. IT people like to solve problems with technology without understanding the problem. • “That’s not how the software works” • “You can’t do it that way” Some use cases are amazingly simple others are very complex. Complex ones should be broken down into components Under ideal conditions the information gained from a clinical research study will become part of the federated data repository which can be used to create the next clinical research project. This simplified diagram attempts to reveal the complexity and connectedness of each of these entities in the clinical research process.
Database Model Most clinical research studies will fit the ‘Visit Model’ This can be a traditional longitudinal study with multiple visits or a single tube of blood obtained at the time of a procedure – a single ‘visit’. A common practice is to use a grid with visits and forms as axis to chart which forms are used at each visit. Almost all clinical research has a temporal component – including chart reviews. This makes dates and times very important. In fact in many cases missing date components are automatically filled with default values to make date/time elements meaningful and acceptable to the database.
Data collection is usually controlled in a clinical study/survey. There are no open ended questions, ambiguous answers are reduced or eliminated by check boxes, radio buttons, coded lists, etc. Data are often validated within (level 1) and between visits (level 2). When a hospital patient receives a diagnosis many decision support systems default to a stand care regimen based on the diagnosis. This has been one of the stumbling blocks to collecting clinical research data within the EMR. The research protocol is not designed to treat a patient according to best practices – it is designed to collect specific data elements in a controlled manner. Even when the protocol is followed – the CRF drives the study. If the CRF is not correct the data will not be correct or complete. It is critical to review every data element on a form with the protocol to ensure that the right data is being collected. e.g. missing units, integers versus real numbers, check boxes instead of radio buttons http://www.clinpage.com/article/paper_is_king_long_live_paper/C5 the 2008 meeting of the Society of Clinical Data Management had a presentation that clearly shows paper is still widely used and may continue to be used for some time.
Survey response for those using EDC: In 2001 16% first entered data onto paper, in 2009 it rose to 26% In 2001 66% said data was initially entered to paper file, in 2009 it rose to 71% There is no allowance for interpretation – even when the conclusion is obvious. Absence of data means no conclusion can be drawn – not even that the data is missing. There’s no way to know its missing unless it’s indicated that it is. Industry sponsored trials do not allow blanks.
There is not a lot of text in CRFs. There is simply no way to process it. NLP is not used and probably would not be trusted to provide empirical data. Text is generally used to document something in the event of an audit versus being as a data point. There is a certain cost to alter an existing database structure. This includes documenting the change(s) and testing and verifying the new system before placing on line.
Biomedical informatics has many practices and expertise that can benefit clinical research. The increased use of meta data, the inclusion of ontologies.
And really the notion that data is useable outside of the study it was collected in. This has been resisted by the clinical research community but the benefits of using existing knowledge to answer more research questions may be winning the argument.
Clinical Research vs. Biomedical Informatics • Studies performed in humans that are intended to increase knowledge about how well a diagnostic test or treatment works in a particular patient population. • The science underlying the gathering, management, and application of biomedical knowledge and information to improve individualized patient care
Reading Summary - Wednesday, Week 12 (by Jitsupa Peelay and Preethi Sankaranarayanan(Picture and some text))
Electronic Health Record Goes Personal World-wide
YC Li, PS Lee, WS Jian, CH Kuo - Electronic Health Record Goes Personal World-wide
- The study attempts to analyze prior research and to synthesize conceptual models of EHR for personal use.
- “Increasing patient demand for convenient access to their own healthcare data has led to more personal use of the Electronic Health Record (EHR). We are seeing a more “patient-centric” approach of EHR from countries around the world.”
- Studies show that proper use of EHR can decrease medical errors, facilitate the detection of adverse health events, enable more appropriate use of healthcare services, and potentially lower healthcare costs.
- Researchers have reported on issues related to EHR sharing, including concerns on privacy and security, consumer empowerment, competition among providers, and content standards.
- Personal use of EHR is an important first step in creating effective and measurable interventions to personal health maintenance.
- As “B2B-Business to Business” and “B2CBusiness to Customers”. We can denote this behavior of taking patients’ own HER to the next provider as “C2B” . By connecting “B2C” and “C2B”, the whole process can be described as “B2C2B”. It is believed to resolve the data ownership problem and also alleviated the patients’ concern on how the data will be used among healthcare providers. Also, it helps clinical research by offering more secondary use of patient data.
- We can also call it “Patient-Initiated Exchange” (PIX) because the exchange process is mostly evoked and controlled by the patients.
Methods - We review papers and case-studies for those EHR systems used nation-wide, or those designed for nation-wide use.
Models for Personal Use of HER - There are 3 models based on different information sharing mechanisms.
1. Inexpensive Data Media Model
- “Using flash memory (such as USB sticks), CD-ROM and Smart Card as the media for sharing EHR with patients is an affordable and accessible solution for most users.”
- Examples : MERIT-9 project from Japan that uses a CD-ROM, Taiwan’s TMT (Taiwan Electronic medical record Template) project uses USB, German national health IT body (Gematik) is also issuing a patient Smart Card, Malaysia MyKAD is a government multipurpose Smart Card
- A small amount of more secure information-sharing can be achieved by Smart Card as a medium.
- Advantages: (1) low media cost (2) small in size and highly portable (3) better privacy because they are carried personally.
- Disadvantages: (1) The user will need a computer, a viewer application. (2) The information is not easy to update. (3) The users can get confused and become reluctant to use them because they are unable to familiarize themselves with so many different viewers.
2. Internet Patient Portal Model
- “Using the Internet to provide information storage and user-interface, a patient will be able to access personal health information with a web browser.”
- Examples :
- In the UK, patients will be able to access a summary of their own health and care information, known as the Summary Care Record (SCR), via portal services such as MyHealthOnLine.
- Advantage: (1) It offers fast updating and easy access to the users.
- Disadvantage: (1) A user must be able to access the Internet and a reliable/ trusted back-end health information service provider (governments or non-profit organizations are preferred).
3. Personal Portable Device Model
- “Popular personal portable devices such as PDAs, cell phones, smart phones and Ultra Mobile PCs (UMPCs) allow immediate access to personal health information either stored locally in the device, or remotely in a server, (which can be readily downloadable through a live connection). “
- Combined with intelligent applications, this model can provide powerful personal reminders for a patient.
- TET / TrEHRT (Traveler’s EHR Template) , a JAVA-based viewer for a summarized patient record designed for travelers. It is based on the open Android operating system from Google. It offers two modes of storage for the personal health data: off-line and online.
- Advantages: (1) This model provides better privacy and is easier to update than the Inexpensive data media model. (2) A personal portable device can operate even without a network connection (when data are stored in the device)
- Disadvantage: It is more expensive to own and maintain.
Security and Privacy - “A public key infrastructure (PKI) is generally considered crucial to achieve a safe environment for health information sharing.”
- The cost of building a nation-wide PKI can be formidable and would require long term planning and maintenance.
- “An increasing number of examples can be found around the world including the Health Certificate Authority (HCA) Card in Taiwan, the Health Card (eGK) in Germany, the UZIcard in the Netherlands.”
--Some Steps to be taken while implementing PHR are to use a public key infrastructure (PKI), use of common standards across providers and support from health care organizations including standard vocabulary usage apart from a common format.
- The sharing of EHR with the patients may also trigger cautions for some physicians. There are physicians who believe that “too much” information can fuel malpractice suits.
-PHRs have concerns regarding security, privacy, consumer empowerment, misuse by for eg: drug seekers, but also provide lot of use for eg: in clinical trials, research purpose, keeps patients well-informed thereby reducing medical errors. This field is still in progress and will reach great heights if the caveats are properly addressed.
Lecture Summary - Wednesday, Week 12 (by Preethi Sankaranarayanan)
• Outpatient EHR capabilities - Viewing (results review), Documenting (structured, semi-structured, unstructured), Ordering (meds), Messaging (inter-provider communication), Care management (problems, allergies, decision support), Analysis and reporting (patterns across multiple patients), Patient directed (communication with patients)
• Core EHR functions that have greatest impact on cost and quality improvement are the least used.
• Barriers - Financial issues (cost), Technical issues (implementation), Social issues- Fear that it will interfere with patient satisfaction
• Missing functionality – preventive care and lifestyle modification – how to implement these? Using consumer health informatics (CHI)
• Functions of CHI - Analyzes consumer need for information, Studies & implements methods for making information accessible to consumers and trigger patient participation in personalized decision making, Models and integrates consumers' preferences into medical information systems
• PHR - Wide range of functionality - From simple data entry and review to decision support, clinical communication, disease tracking and everything in between…
• Benefits of PHR - personal engagement in a person's own care, transformation of patient-provider relationship, coordinate fragmented health information, incorporate new data sources, eg OTC meds, alternative care. Home measurements (BP, weight, glucose). Information from different people - allied professionals, wellness providers
• From a provider’s perspective PHRs will be useful to Source of health information for mobile populations, For prolonged or complex medical conditions (pregnancy, diabetes), For people taking numerous medications, For emergency room visits – quick access to allergies, history, Method of sharing info when traditional means fail (transitions between specialty care and primary care)
• Barriers for PHR - Those who can benefit the most from a PHR system may be the least able to use it. Disparities in access to and use of computers, the Internet, and PHRs may exacerbate health care inequality in the future. Computer literacy, health literacy may be low in certain "target" groups such as older adults. Providers themselves have low rate of use.
• More issues - Privacy/security concerns, more time is involved to access & review PHR information, who is responsible for reviewing patient entered data? Accuracy of the information in the record-Potential that information may be withheld, Potential that information may be “padded” (e.g., false history of pain meds), Doubts that info will be kept up to date, Implications of basing treatment decisions on bad or incomplete data
• PHRs – stand alone like health vault, google health etc, tethered – linked to a specific institution like mychart from University of Utah or integrated with EHR
Week 13: AMIA BREAK!
Week 14: Cognitive issues in informatics; Technology in clinical education
Reading Summary - Monday, Week 14 (by <your name here>)
Lecture Summary - Monday, Week 14 (by <Eungyoung Han>)
Critical Concepts In Usability and Human-Computer Interaction by Nancy Staggers, PhD, RN, FAAN
- Introduce terms and concepts - Human factors, Ergonomics, Human-computer interaction, Usability
- Analyze why usability is crucial to healthcare
- Provide salient examples of poor and exemplary usability
- Human Factors
- The design and interaction with tools or equipment
- Includes physical characteristics (ergonomics) and cognitive aspects of interaction issues with systems
- Terms (HF and ergonomics)used interchangeably (in Europe in particular)
- The design and interaction with tools or equipment
- Diagram for human factors, human-computer interaction, ergonomics, and usability
- Scope of Human Factors Issues
- Donald Norman’s work - The Design of Everyday Things (1988), Things That Make Us Smart (1993), Emotional Design (2004)
- Field of human factors - Origin in aviation and space shuttle design, Past work in nuclear safety
- Bad design is all about killing neurons - a la Ben Shneiderman
- Design of Everyday Things
- Health Sciences Education Building room arrangements - Keeping your wits about you when you’re in search of…
- Human Factors in Healthcare
- Examples from anesthesiology practice
- Anesthesia cart reorganization (George Blike)- Color-coding classes of drugs, Consistent labeling, One dose of each drug, More potent drugs placed in harder-to-reach locations
- Task interruptions - Frank Drew’s research on cell phone usage, The role of interruptions in medication safety
- Examples from anesthesiology practice
- Physical characteristics of systems and how they affect human performance
- Aspects can include - Comfort, Convenience, Function
- Car ergonomics
- Computer mouse design
- Ergonomics in Health Computing
- Equipment location in a workspace and fit with workflow
- Device types and locations for CPOE, rounds
- Selection of devices for standing, sitting, mobile work
- Keyboard placement, device integration
- Pointing devices - Nursing stations and mice
- Arrangement of desks to fit people - Footstools
- Room lighting to avoid glare
- Human-computer Interaction
- Human-computer interaction (HCI) is the design, implementation and evaluation of interactive computer systems in the context of users’ tasks, work and play (Dix et al., 2004): Includes concepts from computer science, systems design, information science, and psychology, Includes content as well as design
- The Usability of Computers
- Usability is the extent to which a product can be used by specific users in a specific context to achieve specific goals with effectiveness, efficiency and satisfaction
- Goals of HCI and Usability
- Designing better systems, supporting improved decision-making through
- Effectiveness - Safety (accuracy), Useful for the task at hand
- Efficiency-Productivity (speed), Cost, Learnability
- Satisfaction - Perceived effectiveness and efficiency User satisfaction
- Promote acceptance and use of systems
- Designing better systems, supporting improved decision-making through
- Example HCI/Usability Areas
- User modeling - Cognitive processes or information flow, Users’capabilities/limitations
- The fit of systems to work (Work Design)
- Single user-computer interaction - Tasks/task analysis/systems analysis
- Joint Cognitive Systems - E-mail, EHRs
- Social issues in computing
- Intuitiveness of icons
- Adherence of applications to known design standards
- The example of Usability of Error Messages - 403 Forbidden (See slide 21)
- EHR – Post CPOE Quotes
- From handoff observations - “You can’t get the big picture of the patient.”, “We have to jump all over the place to find the information we need.”, Summary report organized differently on the screen than when printed, Information is organized differently on the Workstations on Wheels than on terminals
- The Ambulatory Application for the Military (AHLTA) Usability Study
- Complaints about its usability after deployment
- Study undertaken to validate/refute complaints
- 17 Providers observed and screens filmed in mid-2007 - Interviews lasted 60-90 minutes, AHLTA interactions filmed and providers audiorecorded, Information coded by a human factors firm, Observations made during actual patient encounters
- Designed to support primary care but deployed to all providers - Does not work for sub-specialties
- System response time between modules averages 5 seconds - “It breaks the flow.”
- Alters the patient-provider interaction
- Findings - 175 usability issues identified across 7 categories of problems, 22 severe issues, 90% of participants used work-arounds to the new system, The top issues were related to : Pre-visit data gathering, Documenting the encounter in a SOAP note, Interactions with the EHR during the patient visit
- eMAR Usability Evaluation
- Research question: Does an existing eMAR adhere to established design guidelines?
- Methods: 4 Doctoral students evaluated a vendor’s eMAR - Determined typical eMAR tasks, Obtained standard training on the eMAR, Evaluated the application against 14 heuristics combined by Zhang et al. (2003)
- Sample eMAR Usability Issues
- Difficult to determine what meds are given, due at a glance
- No integrated capability to chart related elements – pulse, glucose level, etc.
- Allows a med to be given/charted without verification from a nurse or pharmacist
- Allows multiple doses of the same med to be given/charted without an alert
- Allows users to select incorrect routes for medications when creating an order or administering a medication, e.g. Maalox 30 cc IV
- EHRs and Cognitive Support
- Stead & Lin (2009) mentioned as follows : “…multiple sources of evidence suggest that current efforts aimed at the nationwide deployment of health IT will not be sufficient to achieve the vision of the 21st century and may even set back the cause if these efforts continue wholly without change from their present course. Specifically, success..will require greater emphasis on providing cognitive support for health care providers…”
- National Emphasis on Usability from AHRQ
- Two reports from AHRQ (Nov 2009)
- Electronic Health Record Usability Interface Design Considerations - “Very little systematic evidence has been gathered on the usability of EHRs in practice and the implications of their design on cognitive task flow, continuity of care, and efficiency of workflows.”, Promote standards in usability and information design Policy and research recommendations at http://healthit.ahrq.gov/portal/server.pt/gateway/PTARGS_0_907504_0_0_18/09(10)-0091-2-EF.pdf
- Electronic Health Record Usability Evaluation and Use Case - Outpatient, physician-focused General information about usability at http://healthit.ahrq.gov/portal/server.pt/gateway/PTARGS_0_907504_0_0_18/09(10)-0091-1-EF.pdf
- Two reports from AHRQ (Nov 2009)
- National momentum 2010
- AHRQ report, May 28, 2010
- Surveyed ambulatory EHR vendors about their usability and best practices - Majority said “usability assessments and evaluations are not common”, “Formal usability testing, user-centered design processes, and vendors with specific resources having expertise in usability engineering are not common", Accepted, common EHR usability, design standards do not exist, End-user involvement limited to workgroups, advisory panels or clinicians with a strong interest in technology, Little formal user testing, Testing is typically done after the product is developed
- AHRQ report, May 28, 2010
- Recommendations for Vendors 2010 - Include formal user-design processes, Increase user diversity in testing, Include users throughout the product life cycle, Support independent body for collaboration and standards, Develop standards and best practices for use in site customization methods
- Axioms of Usability
- Early and central focus on users
- Iterative design of applications
- Empirical measurement of effects - Effectiveness, Efficiency, Satisfaction
- Novel Designs for Monitors (See slides 44-49)
A Course for Interested Students: Human-Systems Interaction in Healthcare Informatics (NURS/BMI 6820) will be held in Spring 2011 by Faculty Charlene Weir, PhD, RN (Social Psychology) and Teresa Conway, MS, RN (Intermountain Health)
Concepts of Cognitive Support by Charlene R. Weir, RN, PhD
- Identify concepts of cognitive support
- Review basic cognitive processes
- Understand origin of information overload in terms of computerized clinical environments.
- Review concept of “task-technology” fit
- Describe how to match clinical task characteristics and implementation strategies
- The Socio-Technical Perspective - "Embracing a user-oriented perspective, socio-technical approaches emphasize that through insight into the work practices in which IT applications will be used should be the starting point for design and implementation." (p. 89 - emphasis mine)
- Definition of CDSS - "providing clinicians, patients or individuals with knowledge and person specific or population information, intelligently filtered of presented at appropriate times, to foster better health processes, better individual patient care, and better population health." (Consensus Definition of DSS by A Roadmap for National Action on Clinical Decision Support, AMIA 2008)
- ARE WE MEETING THE GOAL? (Systematic Reviews - Outcomes)
- Effects of CDSS on Practitioner Performance and Patient Outcomes - Significant improvements in practitioner performance in 64% of studies and improvements in patient outcomes for only 13%. Unable to aggregate due to significant unexplained variation, Recommendations: “Important issues include CDSS user acceptance, workflow integration...(p. 1236) by Garg, et al.(2005)
- Improving clinical practice using clinical decision support systems: a systematic review - Sig improvements in practitioner performance in 68%. Patient outcomes were not examined. Unable to aggregate effects due to significant unexplained variation, Recommendations: “The promise of evidence based medicine will be fulfilled only when strategies for implementing best practice are rigorously evidence-based themselves.” (p. 7) by Kawamoto, et al (2005).
- Unable to aggregate due to significant unexplained variation
- The Conclusion by RAND
- “In summary, we identified no study or collection of studies, outside of those from a handful of HIT leaders, that would allow a reader to make a determination about the generalizable knowledge of the system’s reported benefit.
- Even if further randomized, controlled trials are performed, the generalizability of the evidence would remain low unless additional systematic, comprehensive, and relevant descriptions and measurements are made regarding how the technology is utilized, the individuals using it, and the environment it is used in.” (Shekelle, et al, p. 4)
- What Kinds of Cognition Need Support?
- Memory includes Categorization (identifying situation model) and Knowledge (evidence-based information)
- Motivation - Prioritization, Responsibility, Social
- Reception is seeing, hearing and touching. In addition, it is a realm of some human factors. However, perception is goal-based (cat example) and limited by attentional resources (although not totally)
- Attention is our “feeling of awareness”, very limited (multi-tasking), controlled by goals (like when you have lost something), and impacted significantly by physical processes (delirium).
- MEMORY: A Tale of Two Processes
- Associative Processing
- Associative Learning: Gradual accretion of knowledge through progressive associations; e.g. Naturalistic Decision Making
- Thinking: fast, pattern-matching, effortless
- Awareness: Not required for performance
- Errors: common heuristics or “rules of thumb”
- Change: change is slow, hard; like “breaking bad habits.”
- VERY RESISTANT TO IMPACT OF COGNITIVE LOAD
- Symbolic/Rule-Based Processing
- Symbolic Learning: Fast increase in knowledge through rules/symbols/language.
- Thinking: slow, effortful, requires attention
- Awareness: Required for performance
- Errors: miss-identifed task, not understanding
- CHANGE: change may be fast
- HIGHLY SENSITIVE TO COGNITIVE LOAD
- Associative Processing
- GOAL BASED COGNITION
- All work is goal-based
- Goals are knowledge structures that are tightly coupled with action.- Activation, EX: Recall list or save items for a fire, EX: Neighbor story
- Controls perception*, attention, encoding, recall and judgment
- Thinking is Goal Directed by Johnson, V. 2001.
- Nurse Practitioners randomly assigned to goal instructions - Supportive Orientation, Analytical Orientation
- Identified problems, relevant information, and interventions.
- As motivation to be fast increases, then the work will be taken care of by the associative system (less thinking, more automatic, pattern-recognition processing)-Increasing work load, Clinical urgency/acuity
- As motivation to be accurate increases, more of the work will be done by the symbolic system (new material, high patient acuity, social pressure)- Increased complexity, Higher negative consequences, Responsibility/Accountability
- Both types of cognition are “working” simultaneously.
- Humans highly prefer to minimize cognitive load, hence they will always prefer to automatize. - Adaptive strategies or “work-arounds” are geared to “think less”
- Experts do much more with less attention and effort – they reason through pattern-matching.
- COPING STRATEGIES FOR INFORMATION INPUT OVERLOAD - Omission, Reduced Precision, Queuing, Filtering, Cutting Categories, De-centralization, Escape
- Urban Theories
- JIT: Give information that users need when they need it (and only when they need it)
- People are always planning ahead, so WHEN do they need it?
- What information is relevant depends, People are active adapters
- In work settings, people’s information needs are highly tied to possible actions (workflow)
- JIT: Give information that users need when they need it (and only when they need it)
- Information Bias (Redelmeier, et al, 2001.) - See slide 23 for graph
- Two groups of MDs
- One group given a “simple” scenario that included all necessary information.
- Second group had to search for the required information (PFTs)
- Results differed significant.
- Types of Unintended Consequences Related to CPOE
- “Great care must be taken to balance the risks of over-alerting with not alerting. Developers should re-work clinical system interfaces to: 1) reduce collection of redundant information; b) display relevant information in logical locations...“
- Information-System Related Errors
- Interface not suitable for highly interruptive context
- Data display strategies that work reasonably well with sparse data may fail when data is abundant.
- Causes cognitive overload due to overly structured information entry and retrieval
- Misrepresenting collective, interactive work as a linear, clear-cut and predictable workflow
- Misrepresenting communication as information transfer.
- Barriers to Effective Use of VA Clinical Reminders by Patterson, et al (2004)
- Workload was the primary barrier
- Inapplicability to the situation
- Lack of utility and ease of use
- Workflow - not related to core work - duplication
- “Assembly line medicine”
- “Having physicians do clerical entry tasks”
- Issues in Electronic Documentation
- There are several issues in electronic documentation : "overwhelmed", "takes too much effort to sort through everything", "I avoid reading nursing notes, they just have pages and pages of blank fields”, “There is so much stuff put into a note, I can’t find what I need.”
- Access versus Availability
- Tools to identify relevance not available
- Accuracy goals are competing with efficiency goals
- Information Overload
- Inattention to work processes in the implementation process is experienced as information overload to clinicians.
- Deviations in work-flow are perceived as interruptions
- Changes in information location and timing increases cognitive effort
- The process of adaptation results in the creation of innovative strategies to decrease cognitive burden
- Information Overload is really a “mismatch” between available cognitive resources and the task
- CONCEPTS of “INFORMATION OVERLOAD”
- Mismatch between us and context
- Disorientation/ lost
- Inability to determine relevance
- Distracting/forget goal
- High Effort
- Lack of situational awareness
- Inability to “think” or analyze
- TASK ANALYSIS - Information Management Strategies
- Systematic selection of 13 / 133 VA sites
- Random selection of a primary care clinic
- Procedures - Site visit, observations and interviews, Goal-based interviews (“in order to . .”; “by . .”)
- 88 participants (14 nurses, 53 ordering providers, 8 pharmacists, 2 dieticians)
- About 60 hours of observation
- Qualitative Analysis ( tasks, common components, and goals)
- Information Management Goals
- Relevance Screening
- Ensuring Accuracy
- Minimizing Memory Load
- Negotiating Responsibility
- Task-Person-Technology Fit
- Decision support for easy tasks should not require attention (they will be seen as interruptions). Increase Control - Order sets and protocols, Standing Orders, Administrative Control (e.g. formulary), Documentation / Order Combinations, Embedded tracking of behavior, “just in time” heuristic
- Decision support for hard/complex tasks should assist the human in active problem-solving - not replace him/her. - Provide information early in the planning phase, Display information by tasks (e.g.problems), Slow down the process in order to facilitate “deeper processing”, Info buttons, access to other experts/consults; and/or scientific authoritative sources, Enhance team communication
- Humans vary in expertise - Experts have little insight into their reasoning processes., Experts are more likely subject to heuristics and biases (availability heuristic – salient / frequent / recent, Representativeness – if we wear red, we are RED) , Spend twice the time assessing situation then novices.
- Task-Person-Technology Fit
- Impact of functional feedback by Wick, 2000
- Ophthalmology patients completed a computerized version of a visual function instrument.
- Half of the providers received the information.
- Significant differences in pt satisfaction - (Intervention = 3.8; Control = 3.5)
- Significant difference in the average number of “out of bound” domains addressed in the visit.
- Providers showed no awareness that the feedback made any difference.
Reading Summary - Wednesday, Week 14 (by <Robin Palmer>)
Clinical Informatics Board Certification: History, Current Status, and Predicted Impact on the Clinical Informatics Workforce Detmer, D., Munger, B., Lehmann, C. 
- Clinical Informatics: A new specialty
- "Medical Informatics and its subspecialties of Biomedical, Clinical, and Public Health informatics have emerged as a new discipline within health and health care in the 21st century--after a gestation period of roughly sixty years"
- "The American Medical Informatics Association (AMIA) is the professional home for biomedical and health informaticians."
- "Because Clinical Informatics is of growing importance and value to all existing medical specialties, at this point it is possible if not probable that it will be incorporated as a subspecialty certification option open to all existing primary specialties."
- Workforce Demands
- "In 2004, then President George W. Bush called for the widespread use of electronic health records (EHRs) by 2007...but it quickly became apparent that the US health care system was sorely lacking the informatics savvy workforce sufficient in number and knowledge to accomplish this goal."
- "These work force demands dictated that it was time for Clinical Informatics to evolve from an avocational or part time activity of self-identified informaticians to a fully professional career track with training, standards, codes of ethics and certification."
- "It is apparent that success in realizing [EHRs] depends more on knowledge and expertise like needs assessment, organizational leadership, and change management skills than on information technology itself."
- Certification Process
- "AMIA is the professional home to clinical informaticians representing a variety of health professions including medicine."
- "The nursing profession created a Certified Nurse Informatician and as of November 2000, 381 nurses had been certified"
- "In 2005, the membership of AMIA concluded at a town hall meeting that AMIA should move forward with creating a formal certification program for health professionals in Clinical Informatics, beginning with physicians."
- Requirements to establish a subspecialty in Clinical Informatics
- "Markers to determine the essential nature of a specialty include the availability of formal education programs of sufficient rigor and length and the definition of knowledge and skills relevant and critical to working as a professional in the discipline. The presence of one or more scholarly publications in the field that offer peer reviewed articles is another marker. The existence of an organizational home for such professionals like AMIA is a prerequisite, as is a professional code of ethics."
- "Other criteria include documentation of regular well organized meetings, with a national scale and scope that offer relevant high quality continuing educational programs."
- "Two groups were created and empowered by the AMIA Board of Directors to create the core content and training requirements."
- "Currently there are  specialty boards and 121 sub-specialties...the unique nature of Clinical Informatics...made it an ideal candidate for a subspecialty..."
- "...the American Board of Preventive Medicine (ABPM) formally agreed to become the home for the Clinical Informatics certification for physicians."
- Clinical Informatics subspecialty: A prediction for the early years
- "The ABPM has formally notified ABMS (American Board of Medical Specialties) that [they will seek formal review and action on their request to seek approval for Clinical Informatics to become a sub-board of ABPM]...by 2011 or possibly 2012."
- Establishing a new Subspecialty
- "...AMIA is collaborating with the ABPM in the preparation of the formal application."
- "The application must include extensive information about the specialty, proposed requirements for initial certification and maintenance of certification."
- "Once approved, a sub-board examination committee will be created...these experts...will create a set of examination specifications and...examination questions."
- "Policies...must be developed by the sub-board examination committee for reviewing and scoring the examinations. Applications...need to be developed...This entire process must be repeated every five years to assure currency of the examination."
- Individual Certification Process
- "Like other sub-specialties, eligibility of physicians to sit for the certification examination in Clinical Informatics will require successful completion of a Clinical Informatics fellowship."
- "All formal training programs must have a sponsoring institution. Most likely, this will be a medical school."
- Maintenance of Certification
- "After the first examination, a MOC (maintenance of certification) process must be developed."
- "AMIA will develop continuing education opportunities to meet standards for [MOC]"
- Subspecialty Board Timelines (Caveat emptor)
- "Potentially, the first certification examinations in Clinical Informatics would be administrated in the Fall of 2012 and initial certificates would be issued in January 2013."
Note to the RNs in this class
- Click this link for information on obtaining certification as a Nurse Informaticist
Lecture Summary - Wednesday, Week 14 (by <Shay Taylor>)
Simulation at college of nursing. Literature showing simulation and informatics is overlapping.
Simulation requires supportive informatics as well as research informaticists.
U of U has created new model of simulation to teaching for students. Currently students currently go to clinical setting and practice on live patients. Now with simulation center they can practice in low to high levels of realism to learn skills before taking on live patients . Low level-injection in orange Medium level-artificial arm for IV’s High level- practicing on realistic mannequin
Allows students to practice so that mistakes can be made and learned from in simulation and decrease risk to real patients. This allows students to learn from mistakes.
In practice many medication errors not reported due to fear of punishment or lack of knowledge that an error was made. In simulation everything is videotaped so it can be reviewed and everyone can learn from it.
Studio code software is able to capture points in the video/scenario when students complete certain objectives. The segment of video is captured which shows that specific point in time. In the debriefing room those specific moments can be reviewed.
Viewing errors on video helps students to see the mistakes they are making and reinforces the educational experience.
Studio code is one of most valuable tools in simulation center.
Another important tool is using EHR. Many nursing education programs do not include using an EHR. Some hospitals do not let students use inpatient EHRs because of privacy laws. These same hospitals want new grads to know how to use EHRs when they hire them. With the simulation center the real EHR is in use. They have entered patient information for all simulated patients so that students can view the entire medical record for their simulated patients.
Some simulations involve real people not just mannequins.
Research in simulation center includes a program evaluation of simulation delivery model. Controversy about whether simulation is as valuable as clinical experience. Trying to determine how much students learn with simulation and whether they can learn as much or as well as in clinical simulation. How much do students like simulations? How is simulation affecting the end users (hospitals)? Are students better prepared when entering work force?
Also researching cognitive dimensions of nurse’s ability to respond and act based on various clinical situations. Trying to determine what a nurse needs to know to intervene at various points while patient is getting sicker, using simulation to try to identify cognitive interventions.
Also looking at how nurses respond and react to patient care when interrupted, using three types of simulation methodologies.
Many project opportunities for research in simulation.
Week 15: The HITECH Act and “meaningful use”; Special topic presentations (2 teams)
Reading Summary - Monday, Week 15 (by <your name here>)
Lecture Summary - Monday, Week 15 (by Eungyoung Han)
HITECH and Meaningful Use by John F. Hurdle, MD, PhD
- A noble goal for HITECH: “Health information technology helps save lives and lower costs…”
- HITECH: Health Information Technology for Economic and Clinical Health Act
- Government was not interested in spend a lot of money at first. Instead, government passed as part of the stimulus effort The 1st time Feds committed major $:
1) To incentivize hospitals to adopt HIT, 2) To incentivize practitioners to adopt HIT …the missing link in many ways
- Some major changes
- Funds “regional extension centers”
- Based on the “farm bureau” idea
- Local training and resource centers
- Trying to reach 100,000 high-priority primary care providers in first 2 years (change management, vendor selection, etc.)
- Funds SHARP and Beacon
- Defines “meaningful use of HIT,” more on that in a moment…
- The fine print
- Increased privacy/security laws
- Makes sense, more data on the move
- Harsh penalties for disclosing: $100/datum disclosed, up to $25,000/yr, $50,000/1 yr prison if intentionally disclose
- Shifts burden of new security standards to NIST (the atomic clock people)
- Meaningful Use
- What HITECH wants to do is sensible
- How HITECH does it is debatable
- So let’s debate “meaningful use”…
- Break up into two groups: pro/con
- Pro list for the use of meaningful use as criteria for HITECH :
- Meaningful use definition needed and HITECH provides one - It's a start
- Required/optional split : "a lacarte" lets various practices adapt, requirements are good ones e.g. CPOE
- Funded mandate: reimburses expenses/provides $ incentive
- Patient access to personal health record encourages lifestyle change
- Interoperability requirement supports: decision support e.g. drug interaction
- Con list for the use of meaningful use as criteria for HITECH :
- It is not feasible to meet all the criteria.
- Eligibility requirement list is long
- Cost prohibitive not only for initiation but for maintenance. (What if you launch it and then don’t get funding from the feds)
- Will the funds last?
- Privacy will be difficult to maintain
- Short time frame to implement and get it right the first time
- No government provided infrastructure in place right now
- Difficult for providers to launch from paper to full HITECH
- Who will regulate the meaningful use and how well will measurements be regulated
- Pro list for the use of meaningful use as criteria for HITECH :
Lecture Summary Notes Wednesday December 1, 2010 by Julie Martinez
Patients at the center of an Innovative Platform: Personally Controlled Health Records and the App Store for Health by Kenneth D. Mandi, M.D., M.P.H.
US is spending more for less efficient care
Studies biased and few studies are published generally only the positive ones make it to publication. Funding sources have huge impact on results pharmacy vs. Gov. or nonprofit.
This gives us a distorted evidence base
ONC national coordinator Bloomfield 2studies show low uptake in ambulatory and hospital HIT adoption
Conclusion need to push technologies into use
Legislated “meaningful use” adoption will get incentive to adopt HIT
Evidence base that incorporates live info flowing from patient health systems and labs fed back into the system in real time
EMR’s are not well designed, adoption is painful and costly, room for computer error
EMR cannot be implemented in small practices on their own.
Pub article "no small change for the health information economy"
Need an EMR like an iPhone people line up to use this don't need to be paid to learn
Med-tactic project developed a medication list, apps adapted to many EMR systems
API common interface then could create and distribute across many EMR systems
Can change apps see which works best like trying different apps
Software is substitutable
Apps would cost less with increased evolution
Buy a system that can present an application in a useful way, display data in interesting ways eg domestic violence prediction from EMR
Innovative uses to free up need to be locked into specific system
Data sources managed by containers
Containers present data in a uniform fashion
Apps completely substitutable, need to run on multiple platform, multiple apps for the same function -competition
Dashboard with specific apps customer not a wild west scenario
SMArt Health App challenge, see what people can do, innovation and creativity, many players in the sandbox. Top notch judges
Ecosystem to integrate personal health records into patient controlled system
INDIVO server allows patient a tool to request their own information electronically eliminating the need for hospitals to talk to each other. Then a comprehensive record becomes possible under the patient control
System disruptive innovation means low end tech can topple a high end tech system. Simple, meets basic need no bells and whistles end up upgrading from there after you reach market saturation on the low end.
How to loop the patient into this?
Institutions don't share data with each other; ideally we want a universal health record, designed backwards.
Health Information Exchange
No motivation for a hospital to share info with each other, proprietary info, security concerns. Fake concerns about it not being in the patients’ best interest
HIPA meant for info to follow patient around
Patient can add to record but not delete
The model is like quicken/mint.com which goes out to all financial sites and pulls data into an app for you.
Blue button initiative, how to get data out of EMR and into a phi. Patients will see a blue button signaling a way to get your data into a repository of your choice.
Personally controlled health record platform model
Another round of attempts to create a good model Including Microsoft and Google reps after this conference Microsoft talked about using INDIVO went to health vault. Google implemented GoogleHealth, Wal-Mart deployed dossier indivo deployment
What does this mean for healthcare?
Ability for population health research not institutionally bound
Think about how patient help contribute information for use in disease surveillance. Self-repot tool may be a more valuable resource than what is in the medical record.
Patient reported data to look at drug safety
Sum Up 1 The patient is the integrator of data 2 need to add value to data portals via use of apps 3 potential gold mind by getting patient to contribute to their care 4 move to patient centered care understanding impact of med and procedures and not just the satisfaction of "comfort"