Fall 2010 Student Study Guide

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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

Shortliffe - Chapter 1 - The Computer Meets Medicine and Biology: Emergence of a Discipline


  1. 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.
  2. 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
  3. 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.
  4. 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
  5. 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
  6. 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.
  7. 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
  8. 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)


Reading Summary - Wednesday, Week 1 (by Eungyoung Han)

William Hersh - A stimulus to define informatics and health information technology

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)


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.


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.


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.


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)

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:

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..."

Zhou EHR time results.PNG

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."

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
• Administrators
• Researchers

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.

History of Healthcare informatics

Transition of Informatics through the years

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:

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?

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?

Biomedical Data - Summary sheet describing biomedical data and data organizations.

Additional terms:

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. 

- Problems:

   - 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]

CIMINO - Desiderata for Controlled Medical Vocabularies in the Twenty-First Century [2]

CIMINO, ZHU - The Practical Impact of Ontologies on Biomedical Informatics [4]

Links to recommended browsing sites listed with Week 5 (Monday) readings

Lecture Summary - Dr. Hurdle - Monday, Week 5 (by Robin Palmer)

Quiz Review



Knowledge Management

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.

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.

Options for starting from scratch:

Intermountain’s System includes the:

The Good

The Bad

The Future

STEAD - Integration and Beyond: Linking Information from Disparate Sources and into Workflow

First generation - everything is self-contained; had to create databases;provided integration by using a single system for all functions

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

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

Transformation: From Generations to Dimensions


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

Information Workflow Integration

Ontologies: Data Representations to Support Linkages

the types of data on which they operate

Extraction: Data Mining and Filtering

EHR Electronic Health Record (HIMSS) http://www.himss.org/ASP/topics_ehr.asp

EHR Electronic Health Record (CMS) http://www.cms.gov/ehealthrecords/

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.

Hospital Information Systems

Elements of an HIS

Key HIS Functions

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)

Some features of PowerChart

CDR versus EDW

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.

Implementation issues

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)

Lecture from:

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

-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)

What happened?

-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

Ongoing challenges

-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)

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)

Meaningful Use- discussion of, as relevant to hospitals

What it is


the implementation timeline has 3 stages (3 dimensions of meaningful use, you might say) -
  1. 2011: data capture
  2. 2013: integrating the EHR into processes
  3. 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:

10 Key Challenges These are prefaced by two brief dicussions:

OK, now the 10 challenges:

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

They concluded that

US health system challenges

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:

ONC-HIT and DHHS commissioned this study to begin reliable serial measurements of adoption (plan to monitor trend).

Study methods

EHR defined, per key functionalities


Questions asked:

Qualitative analysis

With expert input, designated which EHR components needed for comprehensive and basic systems. Definitions established:

Statistical analysis- described in comprehensive detail.

Respondents and Nonrespondents


Not including VHA hospitals:

(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:)

Quality categories with poor correlation to EHR use are:

The most commonly cited barriers to EHR adoption

Major incentives cited:


  1. potential for nonresponse bias cannot be entirely eliminated. Nonresponders probably less likely to have EHR.
  2. measured adoption- not actual use, nor effectiveness
  3. systems in use not assessed for certification status
  4. low adoption levels limit understanding of adoption of (comprehensive vs basic) systems.
  5. satisfaction of users of EHR not assessed.

Conclusions- see above.

Lecture Summary - Wednesday, Week 7 (by <your name here>)


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)

Types of EMPIs

  1. Vendor-neutral or ‘best of breed’

-This implies that the EMPI can be integrated readily with any other vendor application.

  1. 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.)

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.



Data integrity

Methods and thresholds

EMPI Data Overview

Data ownership

Maintaining the EMPI

Staffing resources

Education and training of EMPI staff

Lecture Summary - Monday, Week 8 (by <Sean Igo>)


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!

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.

MPI concepts:

brag about the quality of their matching.

Data elements in MPI:

birth, it can actually change; have to be clear about whether it was at birth, genetically, current, etc.

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?

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.

again a privacy / confidentiality concern.

Statewide MPI project

millions of instances of patient data

and has recently started clinical data. Want to integrate MPI with cHIE as our final goal.

Project aims:

other community members

sMPI access:

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.

to security checks but can bypass the public services layer.

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.


automatically broadcast certain kinds of activity

for matching etc.; also aids standardization of data & terminology.

FURTHeR = gateway for accessing all these services

Important concepts for sMPI:

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.

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.

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.

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.

matching algorithm speed; so, overhead + processing time. Theoretically, could be a few seconds, but it is not yet known. That's for next year.

Important issues:

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.

choose which providers are allowed to see their demographics?

processes. Again, under consideration.

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.

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?

helping them reduce costs due to tromps and such.

use consent, policies, management, etc.

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)

be supported with at least partial commercial software.

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?

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?

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>)



Where did it come from?[17]



Barriers to Implementation [27]

Recommendations for successful implementation

Why should informaticists care?

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>)

Annotated Bibliography

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?

Translational Informatics

Translational BioInformatics

Examples of Translational Informatics

Clinical Research Informatics

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


Healthcare Reform


Quality Reports

Quality's Purpose

Examples of Quality Issues

Quality Tools

Quality Mantra

Possible Projects for Informaticists & Students

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.

- Examples:

- 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


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

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. [37]

Note to the RNs in this class

Lecture Summary - Wednesday, Week 14 (by <Shay Taylor>)

Dr. Hanberg

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

1) To incentivize hospitals to adopt HIT, 2) To incentivize practitioners to adopt HIT …the missing link in many ways

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

Webmd failed

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"

Special topic presentation Team #2, Wednesday (by <your name here>)

Week 16: Special topic presentations (4 teams)

Special topic presentation Team #3, Wednesday (by <your name here>)

Special topic presentation Team #4, Wednesday (by <your name here>)

Special topic presentation Team #5, Wednesday (by <your name here>)

Special topic presentation Team #6, Wednesday (by <your name here>)

Personal tools