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:
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.
(Who needs graduate education in Biomedical Informatics when all that seems to be needed is a little on-the-job dabbling?)
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.
There is also a call for experts to:
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.
Here is the diagrammatic represention of the above in the full article:
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:
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
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:
AbstractThe 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.
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- 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.
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- 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).
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- 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).
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- Methods and processes: Understand and apply existing methods (eg, simulated annealing) and processes (eg, goal-oriented reasoning) used in different contexts of biomedical informatics.
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- 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.
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- Procedural knowledge and skills: For substantive problems, understand and apply methods of inquiry and criteria for selecting and utilizing algorithms, techniques, and methods.
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- 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.
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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
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