AMIA Board: specification of core competencies in Biomedical Informatics

In 1998 I launched a website called "Medical Informatics and Leadership of Clinical Computing" (now entitled "Contemporary Issues in Medical Informatics- Common Examples of Healthcare Information Technology Difficulties" at this link).

Its theme was that leadership of IT in healthcare was severely lacking in the formal competencies needed to reach any measure of success, and in fact the lack of informatics competencies in the usual IT actors was causing wasted resources and patient harm.

I had also commented that the term "Medical Informatics" itself was being misappropriated by anyone claiming to do anything with computers in medicine, even the creation of trivial and/or low-value programs.

Sadly, little has changed in that regard since 1998; in fact things are much worse.  The meaning of the term "Medical Informatics" itself has become severely blurred, and job listings that use the term are largely misguided.  They often seek a nurse (most common) or doctor (less common) without formal education in the domain, who's dabbled with hospital IT systems, to lead clinical IT projects.  This is a totally inappropriate and even dangerous approach (example here).

The American Medical Informatics Association has released a paper "AMIA Board white paper: definition of biomedical informatics and specification of core competencies for graduate education in the discipline" that is long, long overdue.  As of this writing, full text is available a this link:  http://jamia.bmj.com/content/early/2012/06/07/amiajnl-2012-001053.full.

This paper certainly provides a robust affirmation of ONC's recommendations on healthcare IT leadership roles that I wrote of in my Oct. 2009 post "ONC Defines a Taxonomy of Robust Healthcare IT Leadership."

Some highlights of the new AMIA paper:

Abstract

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is ‘the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.’ Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics sub-disciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

Note that Biomedical Informatics, which the Board feels is a broader term encompassing all of the information-science disciplines in healthcare and biomedical research, is defined as "a core scientific discipline underlying the breadth of the field's research, practice, and education."  One does not acquire expertise in a scientific discipline without first rigorously studying that discipline, e.g., as is done in medical school to gain optimal understanding of clinical medicine.

... The present articulation of BMI core competencies is intended to support AMIA and its members in promoting the discipline as a career choice, and to provide guidance to students and curriculum developers when choosing, designing (and implementing), or re-designing graduate-level academic BMI programs.

(Who needs graduate education in Biomedical Informatics when all that seems to be needed is a little on-the-job dabbling?)

... Defining BMI as the scientific core of a discipline that has broad applications across health and biomedicine highlights its foundational role and refutes the kind of reductionism that superficially explains BMI simply as the application of information technology (IT) to biomedical and health problems.

I termed that phenomenon "Medical Instamatics" on that late 1990's site.  Unfortunately, the "reductionism" is all too prevalent today.  People whose BMI education and skill levels (which I define as the ability to apply deep knowledge and experience to successfully manage the unexpected, not just manage traditional activities via a book of "process"), are often at the amateur level -- in the same sense that I am a radio amateur, not a telecommunications/engineering professional -- or worse.  This wreaks havoc (as here) in health IT, especially when led by senior management also incognizant of the issues.

Definition: Biomedical informatics (BMI) is the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, driven by efforts to improve human health.

Scope and breadth of discipline: BMI investigates and supports reasoning, modeling, simulation, experimentation, and translation across the spectrum from molecules to individuals and to populations, from biological to social systems, bridging basic and clinical research and practice and the healthcare enterprise.

Theory and methodology: BMI develops, studies, and applies theories, methods, and processes for the generation, storage, retrieval, use, management, and sharing of biomedical data, information, and knowledge.

Technological approach: BMI builds on and contributes to computer, telecommunication, and information sciences and technologies, emphasizing their application in biomedicine.

Human and social context: BMI, recognizing that people are the ultimate users of biomedical information, draws upon the social and behavioral sciences to inform the design and evaluation of technical solutions, policies, and the evolution of economic, ethical, social, educational, and organizational systems.

There is also a call for experts to:

• Acquire professional perspective: Understand and analyze the history and values of the discipline and its relationship to other fields while demonstrating an ability to read, interpret, and critique the core literature.

In effect, health IT amateurs, including those in traditional business computing, have little to no formal education or experience in reasoning, modeling, simulation, experimentation, and translation; developing, studying, and applying theories; building on and contributing to computer, telecommunication, and information sciences and technologies; and drawing upon the social and behavioral sciences to inform design of these complex systems.

BMI is the core scientific discipline that supports applied research and practice in several biomedical disciplines, including health informatics, which is composed of clinical informatics (including subfields such as medical, nursing, and dental informatics) and public health informatics (sometimes referred to more broadly as population informatics to capture its inclusion of global health informatics). There are related notions, such as consumer health informatics, which involves elements of both clinical and public health informatics. BMI in turn draws on the practical experience of the applied subspecialties, and works in the context of clinical and public health systems and organizations to develop experiments, interventions, and approaches that will have scalable impact in solving health informatics problems. However, it is the depth of informatics methods, shared across the spectrum from the molecular to the population levels that defines the core discipline of BMI and provides its coherence and its professional foundation for defining a common set of core competencies.

Here is the diagrammatic represention of the above in the full article:

Biomedical informatics and its areas of application and practice, spanning the range from molecules to populations and society

Finally, excerpts from the meat of the article on Prerequisite knowledge and skills.  This depth and breadth of knowledge does not come from studying business computing, dabbling with systems by nurses or physicians lacking formal domain education at the graduate level or beyond, or by guessing by the seat of one's pants:

• • Fundamental knowledge: Understand the fundamentals of the field in the context of the effective use of biomedical data, information, and knowledge. For example:
    • ... Healthcare: screening, diagnosis (diagnoses, test results), prognosis, treatment (medications, procedures), prevention, billing, healthcare teams, quality assurance, safety, error reduction, comparative effectiveness, medical records, personalized medicine, health economics, information security and privacy.
  • Procedural knowledge and skills: For substantive problems related to scientific inquiry, problem solving, and decision making, apply, analyze, evaluate, and create solutions based on biomedical informatics approaches.
    • Understand and analyze complex biomedical informatics problems in terms of data, information, and knowledge.
    • Apply, analyze, evaluate, and create biomedical informatics methods that solve substantive problems within and across biomedical domains.
    • Relate such knowledge and methods to other problems within and across levels of the biomedical spectrum.
• Theory and methodology: BMI develops, studies, and applies theories, methods, and processes for the generation, storage, retrieval, use, management, and sharing of biomedical data, information, and knowledge. All involve the ability to reason and relate to biomedical information, concepts, and models spanning molecules to individuals to populations:
  • Theories: Understand and apply syntactic, semantic, cognitive, social, and pragmatic theories as they are used in biomedical informatics.
  • Typology: Understand, and analyze the types and nature of biomedical data, information, and knowledge.
  • Frameworks: Understand, and apply the common conceptual frameworks that are used in biomedical informatics.
    • A framework is a modeling approach (eg, belief networks), programming approach (eg, object-oriented programming), representational scheme (eg, problem space models), or an architectural design (eg, web services).
  • Knowledge representation: Understand and apply representations and models that are applicable to biomedical data, information, and knowledge.
    • A knowledge representation is a method of encoding concepts and relationships in a domain using definitions that are computable (eg, first order logics).
  • Methods and processes: Understand and apply existing methods (eg, simulated annealing) and processes (eg, goal-oriented reasoning) used in different contexts of biomedical informatics.
• Technological approach: BMI builds on and contributes to computer, telecommunication, and information sciences and technologies, emphasizing their application in biomedicine.
  • Prerequisite knowledge and skills: Assumes familiarity with data structures, algorithms, programming, mathematics, statistics.
  • Fundamental knowledge: Understand and apply technological approaches in the context of biomedical problems. For example:
    • Imaging and signal analysis.
    • Information documentation, storage, and retrieval.
    • Machine learning, including data mining.
    • Networking, security, databases.
    • Natural language processing, semantic technologies.
    • Representation of logical and probabilistic knowledge and reasoning.
    • Simulation and modeling.
    • Software engineering.
  • Procedural knowledge and skills: For substantive problems, understand and apply methods of inquiry and criteria for selecting and utilizing algorithms, techniques, and methods.
• Human and social context: BMI, recognizing that people are the ultimate users of biomedical information, draws upon the social and behavioral sciences to inform the design and evaluation of technical solutions, policies, and the evolution of economic, ethical, social, educational, and organizational systems.
  • Prerequisite knowledge and skills: Familiarity with fundamentals of social, organizational, cognitive, and decision sciences.
  • Fundamental knowledge: Understand and apply knowledge in the following areas:
    • Design: for example, human-centered design, usability, human factors, cognitive and ergonomic sciences and engineering.
    • Evaluation: for example, study design, controlled trials, observational studies, hypothesis testing, ethnographic methods, field observational methods, qualitative methods, mixed methods.
    • Social, behavioral, communication, and organizational sciences: for example, computer supported cooperative work, social networks, change management, human factors engineering, cognitive task analysis, project management.
    • Ethical, legal, social issues: for example, human subjects, HIPAA, informed consent, secondary use of data, confidentiality, privacy.
    • Economic, social and organizational context of biomedical research, pharmaceutical and biotechnology industries, medical instrumentation, healthcare, and public health.
      
      
      
      

While nobody is an expert in all of these areas, skills in many of them are essential for successful and safety-promoting leadership in the health IT domain.

I repeat, this depth and breadth of knowledge does not come from studying business computing, dabbling with health IT, or by guessing by the seat of one's pants.  It comes about from rigorous education and experience in the appropriate domains at the graduate and (especially) post-doctoral levels.

Amateurs mistakenly put in leadership positions, and their organizations, are going to increasingly find themselves in legal hot water over mistakes in design and implementation that result in patient harm, security breaches, overbilling and other issues.

That is probably what it will take to have hospitals manage health IT talent more appropriately.

Finally, I plead guilty to tooting my own profession's horn.

Somebody needs to when the stakes are so high for patients.

-- SS

A Few Additional Comments on the GE Radiation Debacle

Roy Poses beat me to posting about the General Electric CT over-irradiation debacle (GE: Don't Know Much About Radiation Safety, Don't Know Much About Physics).

I am going to add two points:

Point 1:

The National Research Council in its 2009 report on health IT warned that "current approaches to healthcare IT are insufficient", and one of the major caveats was that:

... greater emphasis should be placed on information technology that provides health care workers and patients with cognitive support, such as assistance in decision-making and problem-solving.

In fact the lack of cognitive support for clinicians was one of the report's major themes.

"Cognitive support" by definition means producing devices (whether physical or virtual) that are intended for busy clinical settings where situations often resemble a madhouse - not calm, solitary office environments (borrowing from Joan Ash's findings on CPOE flaws):

"Many information systems simply don't reflect the health care professional's hectic work environment with its all too frequent interruptions from phone calls, pages, colleagues and patients. Instead these are designed for people who work in calm and solitary environments. This design disconnect is the source of both types of silent errors …Some patient care information systems require data entry that is so elaborate that time spent recording patient data is significantly greater than it was with its paper predecessors," the authors wrote. "What is worse, on several occasions during our studies, overly structured data entry led to a loss of cognitive focus by the clinician."

It does not mean, as reported on July 31, 2010 in the New York Times, that designers and vendors of these medical devices should take the stance of blaming the user:

A GE spokesman, Arvind Gopalratnam, said the way scanners were programmed was “determined by the user and not the manufacturer.” GE, he added, has no record of Glendale seeking its help setting up the new procedure in 2009.

... GE says the hospitals should have known how to safely use the automatic feature. Besides, GE said, the feature had “limited utility” for a perfusion scan because the test targets one specific area of the brain, rather than body parts of varying thickness. In addition, experts say high-clarity images are not needed to track blood flow in the brain.

GE further faulted hospital technologists for failing to notice dosing levels on their treatment screens.

But representatives of both hospitals said GE trainers never fully explained the automatic feature.

Imagine the reaction if Boeing blamed pilots for collisions if those pilots, busy with other matters, were only alerted to an impending collision via a range reading on their cluttered instrument panel - rather than a LOUD AUDIBLE ALARM - such as WARNING, WARNING, COLLISION IMMINENT.

Point 2:

What's really missing here was attention to cognitive support for busy clinicians. This, of course, requires proper senior management - senior management with the appropriate expertise.

Proper senior management then controls talent management down the chain of an organization. If the leaders "don't know nothing 'bout trigonometry", they will be less likely to put mathematicians in appropriate leadership roles.

If management doesn't understand Medical Informatics, they will be less likely to put informaticists into leadership roles as well. One major focus of Medical Informatics is cognitive support of busy clinicians in their all too real-world environments.

Why might the latter point be relevant at GE?

In 1999 after working for a competitor of GE, Comdisco Healthcare Group, who among other business aspects reconditioned and resold capital equipment such as CT scanners, I wrote this in an essay on "what medical informatics is not":

... I've noted a number of large vendors and even national medical organizations whose so-called "Medical Informatics Directors" had neither clinical backgrounds nor training in medical informatics (nor in information science of any kind). MIS managers, social workers, and clinicians with no more experience than some tinkering with a home Macintosh can be found as "Directors of Medical Informatics" in the (unfortunately) unregulated healthcare IT industry.

Sometimes the term is used as a sales slogan. General Electric displayed a huge banner over their booth proclaiming themselves "the world leader in Radiology Informatics" at the 1999 Radiological Society of North America (RSNA) conference in Chicago. Unfortunately, nobody present, including sales, management, and engineering representatives, could explain to me what that term meant. They actually said they did not know. I had only identified myself as a physician at that point, not as a medical informatics professional, and expressed incredulity on nobody being able to explain the banner to me. Under pressure, one GE engineer offered the statement "I think it has something to do with computers attached to our x-ray machines."

It's now 2010. It seems GE still may not know what Medical Informatics is.

Handing busy clinical end users a revolver with a single bullet in the chamber, and asking them to play Russian Roulette under busy circumstances with a warning to "check the chamber carefully each time before you pull the trigger", is simply unacceptable for computerized medical device design.

-- SS

Medical Informatics, Pharma, Health IT, and Golden Advice That Sits Sadly Unused

In recent correspondences with colleagues I was reminded of a letter I wrote seven years ago that was published in Bio-IT World, a journal about biomedicine focusing mainly on pharma, bioinformatics and related fields.

As the sole formally-trained Medical Informatics specialist at Merck, I wrote:

Medical Informatics MIA [Missing in Action - ed.]
Bio-IT World
August 13, 2002

Dear Bio-IT World:

I enjoyed reading the article "Informatics Moves to the Head of the Class" (June Bio·IT World). Thank you for spotlighting the National Library of Medicine (NLM) training programs in medical informatics and bioinformatics, of which I am a graduate (Yale, 1994).

Bioinformatics appears to receive more media attention and offer more status, career opportunities, and compensation than the less-prestigious medical informatics.

This disparity, however, may impede the development of next-generation medicines. Bioinformatics discoveries may be more likely to result in new medicines, for example via pharmacogenomics, when they are coupled with large-scale, concurrent, ongoing clinical data collection. At the same time, applied medical informatics, as a distinct specialty, is essential to the success of extensive clinical data collection efforts, especially at the point of care.

Hospital and provider MIS personnel are best equipped for implementing business-oriented IT, not clinical IT. Implementing clinical IT in patient-care settings constitutes one of the core competencies of applied medical informaticists.

Informatics specialists with a bioinformatics focus — even those coming from the new joint programs — usually are not proficient in hospital business and management issues that impede adoption of clinical IT in patient care settings. Such organizational and territorial issues are in no small way responsible for the low utilization of clinical IT in patient care settings.

It will be important for medical informaticists focused in the clinical domain and bioinformaticists specializing in the molecular domain to collaborate with other specialists in order to best integrate clinical and genomic data.

Further information on these issues can be found in the book Organizational Aspects of Health Informatics: Managing Technological Change, by Nancy M. Lorenzi and Robert T. Riley (Springer-Verlag, 1995). Various publications from the medical informatics community, such as the American Medical Informatics Association (www.amia.org) and the International Medical Informatics Association (www.imia.org), are also useful.

Scot Silverstein, MD
Director, Published Information Resources & The Merck Index
Merck Research Laboratories

I was also responsible for the entry of the term "Medical Informatics" into the controlled vocabulary pool used for various purposes at Merck.

As far as I can tell, the Medical Informatics talent gap still exists in all major pharmas despite writings on the topic from colleagues as well as myself. With the present turmoil including declining pipelines, mergers and mass layoffs pending in many large pharmas, and even despite Medical Informatics on a fast path to being declared a full medical subspecialty, it is likely this gap will persist for years longer. This is a shame. The field offers insights that can help R&D substantially, and I speak from direct experience from my time in that domain.

I am reminded via all this of another industry that seems to hurt itself via ignoring the advice of Medical Informatics professionals, the health IT industry. Healthcare IT is actually the core competence of Medical Informatics professionals, but those people are under-represented in the higher ranks of the health IT industry as well. Many job postings seek such people, but for lower level roles (as I've posted here in the past), and/or conflate formal training with informal experience and with those who qualify for the title of Medical Informaticist like I qualify (being an amateur radio licensee, extra class) as a professional RF engineer.

The irony is this: the wisdom of the Medical Informatics field on health IT goes back not years, but decades. It is advice that could have made the vendors much higher margins, allowed them to produce better products, avoid the government regulation that is now nearly inevitable (in some EU countries, clinical IT has already been determined to be a medical device requiring regulation), and in many cases, enabled corporate longevity.

Yet the teachings and accumulated wisdom of the field were, and largely still are, ignored, making books such as the new "H.I.T. or Miss: Lessons Learned from Health Information Technology Implementations" necessary even in 2009.

Here is just a small sampling of that wisdom:

Dr. Donald A. B. Lindberg (now Director of the U.S. National Library of Medicine at NIH), 1969:
"Computer engineering experts per se have virtually no idea of the real problems of medical or even hospital practice, and furthermore have consistently underestimated the complexity of the problems…in no cases can [building appropriate clinical information systems] be done, simply because they have not been defined with the physician as the continuing major contributor and user of the information."


Dr. Octo Barnett's [Harvard] health IT Ten Commandments, 1970:
1. Thou shall know what you want to do
2. Thou shall construct modular systems - given chaotic nature of hospitals
3. Thou shall build a computer system that can evolve in a graceful fashion
4. Thou shall build a system that allows easy and rapid programming development and modification
5. Thou shall build a system that has consistently rapid response time and is easy for the non-computernik to use
6. Thou shall have duplicate hardware systems
7. Thou shall build and implement your system in a joint effort with real users in a real situation with real problems
8. Thou shall be concerned with realities of the cost and projected benefit of the computer system
9. Innovation in computer technology is not enough; there must be a commitment to the potentials of radical change in other aspects of healthcare delivery, particularly those having to do with organization and manpower utilization
10. Be optimistic about the future, supportive of good work that is being done, passionate in your commitment, but always guided by a fundamental skepticism.

[Dr. Barnett played a key role in the 2009 National Research Council report about current approaches to health IT being inadequate, Press Release at http://www8.nationalacademies.org/onpinews/newsitem.aspx?RecordID=12572, and full report "COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE: IMMEDIATE STEPS AND STRATEGIC DIRECTIONS. - ed.]


Dr. Morris Collen's Five Rules, 1972
Most common causes of health IT failure:

• Suboptimal mix of medical and computer specialists … resulting in communications difficulties and in the computer staff underestimating the vast medical needs
• Gross underestimation of the large amounts of money needed
• Suboptimal systems approach with serious incompatibilities between modules
• Unacceptable terminals
• Inadequate management organization and poor judgment
  

Dr. R. Friedman, Reasons for slow spread of EMR, 1977:

• Poor engineering and unreliability
• Physicians not provided with computer-based applications that exceeded their own capability!
• Inability to prove a positive effect on patient care
• Difficulty transferring one application from one institution to another
  

(All taken from Collen's "A history of Medical Informatics in the United States, 1950-1990".)

I might add that the PC did not even exist in 1977, unless you consider the Altair and Heathkit H8 "personal computers."

Four-decades-old wisdom like this, and much more, sits out there in the ether and in the Medical Informatics field's professionals like a pot of gold, but is apparently considered as valuable as lead by the HIT - and pharma - industries. I find this amazing - and a pity.

-- SS