Challenges for Building Capacity of the Clinical Informatics Subspecialty

The new clinical informatics subspecialty promises to provide professional recognition to the increasing number of physicians who work in the specialty of combining information with their medical expertise to improve quality and safety while lowering the cost of healthcare. The American Board of Preventive Medicine (ABPM), the administrative home for the subspecialty, is currently defining the criteria for those who will be eligible to take the certification exam without formal training (i.e., “grandfathering” by virtue of previous work in the field - whether by the “practice pathway” or prior training - which will be allowed for the first five years of the subspecialty's existence), developing the first board certification exam, and defining criteria for future fellowship training.

The new subspecialty will provide a great opportunity for professional recognition of physicians who work in clinical informatics. One concern, however, is how our field will build capacity to train the critical mass of those who wish to become trained and certified in the subspecialty. There are a number of unique aspects of this discipline that will make this task challenging. In this posting, I will speak to these from my position as a program director of one of the largest clinical informatics educational programs in the United States.

There will be challenges both during the grandfathering era as well as when formal fellowship training is required. For the former, there will likely be exclusion of some who have the knowledge or the experience, but not both, to be deemed clinical informatics subspecialists. For the latter, if this field follows a “traditional” path of requiring all entrants to the field to obtain training only in 1-2 year, on-site fellowships, then we may be unlikely to match the need for these specialists or the aspirations of those who often enter the field in middle of their careers.

Data and Perspectives

Our informatics educational program at Oregon Health & Science University (OHSU) has been an extremely popular approach for all, including physicians, to receive training in clinical informatics. The program is available both on-campus and via distance learning, with the asynchronous nature of courses in the on-line program allowing students to train without having to move or leave their current jobs. A total of 1359 individuals have enrolled in the OHSU informatics program since its inception in 1996. During that time, 441 people have received a total of 12 PhD degrees, 184 master's degrees, and 278 graduate certificates. (The graduate certificate is a subset of the master’s degree program covering the core content of the field. While it has been in existence for over a decade, its numbers increased significantly from funding by the Office of the National Coordinator for Health IT [ONC] University-Based Training [UBT] Program for “short-term training,” especially in the workforce role of “clinician leader.”)

There are currently 291 students actively enrolled in the OHSU informatics program, 95 (32%) of whom are physicians. A similar proportion of our graduates are physicians, many of whom have gone on to leadership roles in clinical informatics, such as that of Chief Medical Informatics Officer (CMIO). A not-insignificant number of them were already CMIOs or other leaders upon entering the program, and some of those negotiated enrollment in the program or at least some courses within it as a condition of employment. Our data and experience clearly show that informatics via distance learning is a credible pathway for physicians and others to become clinical informatics professionals.

Our experience has also shown that essentially all types of informatics experiential learning can take place in a distance learning program. One concern we have always had in our program is the ability to gain experience through a practicum or internship. We have been able to institute such programs that allow students to carry out a mentored experience in “real-world” settings of health care organizations, companies, government agencies, and others. Our process tracks deliverables of the documentation of experiences and includes faculty monitoring of progress. It has even sometimes led to employment in those settings.

Some additional data from our program is relevant to the following discussion of challenges for building clinical informatics capacity of physicians. One is the median age of our students, which is about 41.5 years at matriculation into the program. The following chart shows the average age of physicians currently enrolled in the program. These data clearly show that most physicians in our program pursue informatics training and positions in the middle of their careers, i.e., do not follow the traditional contiguous progression from medical school to residency to subspecialty training and employment.

Another data point concerns the mapping of our curriculum to the core content of the new subspecialty, as laid out by Garnder et al. (2009) and included in the proposal for the subspecialty approved by the American Board of Medical Specialties (ABMS). We recently mapped the core content to our existing curriculum and found the material spread over 23 academic-quarter courses. Clearly the core content of clinical informatics will need to be consolidated into many fewer courses, but it is unlikely that any course of study will require the equivalent of a master's degree or at least a graduate certificate.

But clear unlike most other medical subspecialties, the knowledge base of clinical informatics is not a refinement of what the physician learned in medical school and built upon in residency. Consider, for example, a trainee in the area of critical care medicine. A future intensivist physician will have learned the basics of the diseases, treatments, tests, etc. starting in medical school. In medical school, the student will have started in basic science courses with the fundamentals of the cardiovascular system, the pulmonary system, and other applicable biomedical areas. As a clinical student, he or she will see their first cases of conditions such as sepsis, heart failure, and severe pulmonary disease in critical care units and other areas of the hospital. If interested in a career in critical care medicine, that medical student may then pursue a residency in internal medicine, surgery, anesthesiology, or other areas, but will continue to build upon the foundation of diseases and treatments learned in medical school. He or she will complete their training in a clinical fellowship, where more detailed knowledge emanating from the basics started in medical school will be mastered. Those who aspire to train in clinical informatics, however, will enter a new world of knowledge. While clinical expertise certainly will provide a partial foundation to the knowledge he or she must master, entire new areas of study will be brought into the equation. These include topics such as clinical decision support, organizational behavior and management, health information exchange, and standards and interoperability.

Challenges in the Grandfathering Era

The ABPM will soon be announcing what will qualify as “already working in the field,” which will determine who will be eligible to sit for the certification exam in the first five years of the subspecialty. The proposal submitted to the American Board of Medical Specialties (ABMS) suggested that working in the field be defined as either having worked in the field at 25% or more effort for at least three years or by having completed a “non-accredited fellowship” of at least 24 months duration. What exactly is meant by the latter is unclear, especially since many who have entered the field have done so through graduate-level educational programs, such as the OHSU program described above, that meet or exceed the depth of a fellowship program, even if they are not pursued in a full-time manner.

I have concerns that there will be disappointment with the criteria, both from those who are not eligible and could likely pass the exam as well as those who will be eligible but find the knowledge content of the exam overwhelming despite their substantial experience working in the field. I know this is true of all new medical specialties that become formalized, and that it takes some time for a field to synchronize its training and its practice knowledge base. But as noted above, clinical informatics has some unique differences, especially with regards to a knowledge base that is not just a refinement of what is learned starting in medical school.

There will likely be many in the category of physicians who are deemed not to meet the grandfathering requirements for experience yet could likely pass the test. This may include those who have completed educational programs such as a master’s degree or graduate certificate, either in informatics or a related discipline. Depending on how many of these programs qualify as a “non-accredited fellowship,” there could be many physicians who pursued formal training in the field only to not be eligible under the initial certification process.

By the same token, there will also likely be many physicians who have been working in CMIO or other clinical informatics positions, thus meeting the practice requirements, but whom have little or no formal training in the field and lack mastery of the knowledge base to be able to pass the certification exam. Clearly there must be some bar set for knowledge in the field, but many experienced clinical informaticians will require substantial education to achieve the level of knowledge required to pass the exam. Some challenges will include where to set the bar and how to help those who fall below it achieve the knowledge to move above it.

Challenges in the Clinical Fellowship Era

There will be additional challenges for building capacity after the grandfathering era has ended and formal fellowship training is required. These challenges will likely be more daunting, especially if we want to broadly expand the capacity of the field to meet perceived needs for individuals trained and certificated in clinical informatics. Depending on how stringent the requirements are for full-time, in-residence fellowship training, it could be quite difficult to build the needed capacity.

The first challenge for clinical informatics training will be how new trainees learn the core content. Clearly a subspecialty fellowship in clinical informatics will require a more formal educational program than the usual half-day per week of lectures by local subject experts in a typical clinical fellowship. This point is driven home by an analysis of the core content mapped to courses in the OHSU biomedical informatics graduate program described above, where we found the material to be mapped over 23 academic-quarter courses. Certainly a course of study will need to be consolidated into many fewer courses, but the mastery of this knowledge will not be provided the usual half-day per week of lectures provided in a conventional clinical fellowship. Organizations that offer clinical informatics fellowships will need to provide this educational activity, or at least partner with others who can do so.

A second challenge for building the capacity is that many physicians (and others) enter the field of informatics in the middle of their careers. This is not a negative for the field, as many clinicians come to realization that some of the biggest challenges in healthcare involve managing and making best use of data and information. As such, they decide to pursue careers in informatics that will allow them to do that. This pursuit of informatics in mid-career is one of the major reasons for the popularity of distance learning programs. We have found that despite the large numbers of students in our program, one of our biggest challenges is filling classrooms on our campus. Even “local” students in the Portland area want to take “distance” classes due to convenience and/or daytime working constraints.

A third challenge for developing capacity concerns the ability of organizations to stand up on-site training programs to handle building overall capacity. In order to maintain a clinical informatics fellowship program, according to the training requirements laid out by Safran et al. (2009), organizations will need to provide not only practical, hands-on training under supervised certified clinical informatics subspecialists, but also a robust educational experience. A scan of existing informatics training programs shows that some have strong hands-on components and others have well-developed educational programs but few have both. While the quantity of clinical informatics subspecialists needed is not precisely known, it is clear that only a small number of programs would be able to stand up programs that could meet the requirements spelled out by Safran et al. in contrast to the potentially hundreds if not thousands of hospitals and other clinical settings that could benefit from these specialists. This necessitates a more efficient approach to training, a contribution of which distance learning approaches could provide.

A fourth challenge is who will bear the cost of fellowship training. While most educational programs are funded by tuition, clinical fellowships are usually paid positions where the cost is covered by a combination of graduate medical education subsidy through Medicare as well as patient care services provided by the trainee. While both of these traditional sources of fellowship funding might work in some settings, it is not clear in this era of reduced federal funding for medical training and squeezed hospital budgets that paid fellowships will be viable in many places.

A final challenge could be the accreditation of fellowship sites by the Accreditation Council for Graduate Medical Education (ACGME). This challenge is not limited to the clinical informatics subspecialty. While the ACGME has accredited some programs that allow elements of remote learning, e.g., (Emmett and Green-McKenzie, 2001), its view, like most of medicine, is that subspecialty training is mostly an activity that takes place in a full-time fellowship at one or more physical sites.

Road Ahead

The need for clinical informatics subspecialists is clear, and the aggregate capacity to train adequate numbers is probably available. However, the traditional fellowship where experiential and didactic learning takes place in a single organization is likely impractical, certainly for the numbers that most estimate are needed for the subspecialty. Based on our experience in training physicians and others for careers in informatics, we believe the approach that is most effective and scalable will be to combine the online curricular delivery with practical experience on the ground augmented with additional interactions among trainees, including in-person or virtual approaches.

There are likely creative ways to build the capacity of clinical informatics training programs. One would be to allow institutions that could offer up robust experiential training to partner with those can provide the education, with the latter in a remote manner. Our program is already in discussion with two organizations that are considering melding our educational programs with their on-site training. Not only will we provide “out-sourcing” of coursework to these institutions, but we will also engage with their faculty in faculty development. We also plan to make use of telecommunications modalities to allow interaction among their trainees, our faculty, and even our local trainees.

There are other reasons why clinical informatics fellowship training should be more distributed. The world of clinical informatics is very different in high-resource academic centers compared to community hospitals and other clinical settings. The latter types of organizations are less likely to achieve “meaningful use” of information technology (Desroches, Worzala et al., 2012). A robust training experience should include these types of settings as well. Distributed training experiences will also allow for more interaction among trainees. As a single healthcare organization is likely to only be able to accommodate a few trainees, an integrated multisite program will allow more trainees to interact and share knowledge and experiences.

Clinical subspecialty training has historically been provided at one or a small number of sites, with educational activities also provided at those locations. However, with the growing proliferation of specializations that physicians can undertake today (Cassel and Reuben, 2011), many of which did not exist during their initial training, clinical informatics will not only benefit from novel approaches but could also provide an opportunity for medicine to reconsider how physicians train in many other specialties. Regulatory bodies will need to recognize these problems and authorize training programs that achieve their educational goals, even if in non-traditional ways. Just as the rest of education has adapted to and embraced the use of technology, medicine must do likewise.

References

Cassel, C. and Reuben, D. (2011). Specialization, subspecialization, and subsubspecialization in internal medicine. New England Journal of Medicine, 364: 1169-1173.
Desroches, C., Worzala, C., et al. (2012). Small, nonteaching, and rural hospitals continue to be slow in adopting electronic health record systems. Health Affairs, 31: 1092-1099.
Emmett, E. and Green-McKenzie, J. (2001). External practicum-year residency training in occupational and environmental medicine: the University of Pennsylvania Medical Center Program. Journal of Occupational and Environmental Medicine, 43: 501-511.
Gardner, R., Overhage, J., et al. (2009). Core content for the subspecialty of clinical informatics. Journal of the American Medical Informatics Association, 16: 153-157.
Safran, C., Shabot, M., et al. (2009). ACGME program requirements for fellowship education in the subspecialty of clinical informatics. Journal of the American Medical Informatics Association, 16: 158-166.

Health IT Job Creation Predictions Come True: More Than 60,000 Since 2008

Back in 2008, when searching to find an estimate of the magnitude of health information technology workforce (HIT) needs, I came up empty-handed, which led me to try to answer the question myself. The best source of data I was able to find was the HIMSS Analytics Database. I knew that this was not the ideal information source, i.e., it was self-reported data not really aiming to capture detailed HIT staffing information. While the analysis did make some adjustments to the data that passed muster with peer reviewers, it gave us an estimate of a need for approximately 41,000 additional people needed as electronic health record (EHR) adoption advanced to the level associated with improved clinical outcomes, which coincided with use of clinical decision support and computerized provider order entry. This was based on best research at the time [1] and still holds true today [2]. This corresponded to Stage 4 of the HIMSS Analytics EMR Adoption Model (EMRAM). (This was before the era of "meaningful use," although the following year, HIMSS Analytics noted that EMRAM Stage 4 was approximately the level needed to meet the early conceptions of what meaningful use would be [3].)

I had the opportunity to present the results of my research at a briefing on Capitol Hill in the spring of 2008, with their publication later that year at the AMIA Annual Symposium [4]. I believe I can argue without too much bravado that this was one of a few happenings that put HIT workforce on the map, leading to its inclusion in Section 3016 of the Health Information Technology for Economic and Clinical Health (HITECH) Act of the American Recovery and Reinvestment Act (ARRA) of 2009.

Acting on the Section 3016 statute in the HITECH Act, the Office of the National Coordinator for Health IT (ONC) followed through by combining my data with other sources to come up with an estimate of HIT workforce needs to meet the coming incentives to implement meaningful use. They estimated more than 50,000 new HIT personnel would be required in addition to those already working in the field to achieve the goals for meaningful use [5]. This led to the specific programs created under the ONC Workforce Development Program [6].

While it will take much longer to know how successful the ONC-funded programs will be, or what the long-term HIT job market will look like, the recent release of an ONC Data Brief bore out an estimate of the jobs [7]. Proving early estimates quite prescient, the ONC analysis found that indeed, employment in HIT has increased by over 60,000 between 2008 and 2011, as shown in the figure reproduced from the Data Brief below. The total employment in HIT, according to these government figures, was 362,265 in 2011.

It has been quite rewarding to be part of this national effort to identify, develop, and observe the outcomes of these efforts to achieve one part of the informatics agenda. Although the future is uncertain, as the course of technology, healthcare reform, and government programs is unpredictable, with the interaction among the three of them even more unknowable. However, the need for skilled informatics professionals will continue to be an important part of the HIT landscape [8].

References

1. Chaudhry, B., Wang, J., et al. (2006). Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine, 144: 742-752.
2. Buntin, M., Burke, M., et al. (2011). The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Affairs, 30: 464-471.
3. Davis, M. (2009). The State of U.S. Hospitals Relative to Achieving Meaningful Use Measurements. Chicago, IL, HIMSS Analytics. http://www.himssanalytics.org/docs/HA_ARRA_100509.pdf.
4. Hersh, W. and Wright, A. (2008). What workforce is needed to implement the health information technology agenda? An analysis from the HIMSS Analytics™ Database. AMIA Annual Symposium Proceedings, Washington, DC. American Medical Informatics Association. 303-307. http://skynet.ohsu.edu/~hersh/amia-08-workforce.pdf.
5. Conn, J. (2010). 50,000 new health IT workers might be needed. Modern Healthcare. May 25, 2010. http://www.modernhealthcare.com/apps/pbcs.dll/article?AID=/20100525/NEWS/100529949/.
6. Hersh, W. (2012). Update on the ONC for Health IT Workforce Development Program. HIMSS Clinical Informatics Insights. July, 2012. http://www.himss.org/ASP/ContentRedirector.asp?ContentId=80559&type=HIMSSNewsItem;src=cii20120709.
7. Furukawa, M., Vibbert, D., et al. (2012). HITECH and Health IT Jobs: Evidence from Online Job Postings. Washington, DC, Department of Health and Human Services. Data Brief No. 2, May, 2012, http://www.healthit.gov/sites/default/files/pdf/0512_ONCDataBrief2_JobPostings.pdf.
8. Leviss, J., Gugerty, B., et al. (2010). H.I.T. or Miss: Lessons Learned from Health Information Technology Implementations. Chicago, IL. American Health Information Management Association.

New IOM Report on Implementing the Learning Healthcare System: It's All in the Information

Some of the most important reports for setting the context of the work of informatics have been those from the Institute of Medicine (IOM). These reports have now spanned over 20 years, with many serving to raise awareness of problems and provide a context for informatics solutions. Some of the IOM's seminal reports have covered the topics of electronic health records [1, 2], telemedicine [3], computer networks and the Internet [4], privacy and security [5], medical errors and patient safety [6, 7], healthcare quality [8], health professions education [9], reducing costs while improving outcomes [10], and safety of health information technology [11].

More recently, these reports have coalesced around the notion of the learning health system, a system that learns from its experiences, incorporates the best science, and provides patient-centered care [12]. Additional reports have focused on issues that heavily involve informatics, such as developing the human and organizational [13] as well as digital [14] infrastructures for the learning healthcare system. The former Chief Science Officer of the Office of the National Coordinator for Health Information Technology tied its efforts to the notion of the learning healthcare system [15].

This past week, the IOM provided another "smash hit" in its series of reports. Entitled, Best Care at Lower Cost, this report notes the urgent need to address both the increasing complexity of the healthcare system as well as its continually increasing costs [16]. The report relates that many industries, from banking to manufacturing to transportation, operate with increasing coordination and efficiency in recent times, especially when aided by modern information technology. Yet healthcare is mired in the past, being highly uncoordinated and excessively labor-intensive.

The full report is available for viewing online and as a downloadable PDF. There are some condensed versions as well, including a report brief, the main recommendations, a list of the characteristics of a continuously learning healthcare system, and an infographic that highlights the main points. An article in JAMA also provides an overview of the report's motivations, findings, and recommendations [17].

The report asserts that implementing standard practices from those of other industries could result in:

• Records immediately updated and available for use by patients
• Care delivered the has been proven "reliable at the core and tailored at the margins"
• Patient and family needs and preferences are a central part of the decision process
• All healthcare team members are fully informed about each other’s activities in real time
• Prices and total costs are fully transparent to all participants in the care process
• Incentives for payment are structured to "reward outcomes and value, not volume"
• Errors are promptly identified and corrected
• Outcomes are routinely captured and used for continuous improvement

These results could be possible now because of human and technological changes that have been adopted in most industries, including:

• Substantial computational power that is affordable and widely available
• Network connectivity that allows information to be accessed instantaneously from almost anywhere
• Human and organizational capabilities that improve the reliability and efficiency of care processes
• The recognition that effective care must be delivered collaboratively by teams of clinicians and patients, with each playing a vital role in the process

The report was motivated in part by the conclusions of a previous report that noted annual excess costs of care in the US to be around $750 billion (out of $2.5 trillion expended), resulting in approximately 75,000 annual premature deaths. It grouped the causes of this waste and harm as due to:

• Unnecessary services provided
• Services inefficiently delivered
• Prices too high relative to costs
• Excess administrative costs
• Missed opportunities for prevention
• Fraud

Also identified in the report are four "characteristics of a continuously learning healthcare system." These include:

1. Science and informatics - real-time access to knowledge and digital capture of the entire care experience
2. Patient-clinician partnerships - engaged, empowered patients
3. Incentives - aligned for value with full transparency
4. Culture - instilled by leadership and with supportive system competencies

The report concludes with a series of recommendations for the continuously learning healthcare system group into three categories (verbatim):

I - Foundational Elements
1. The digital infrastructure. Improve the capacity to capture clinical, care delivery process, and financial data for better care, system improvement, and the generation of new knowledge.
2. The data utility. Streamline and revise research regulations to improve care, promote the capture of clinical data, and generate knowledge.
II - Care Improvement Targets
3. Clinical decision support. Accelerate integration of the best clinical knowledge into care decisions.
4. Patient-centered care. Involve patients and families in decisions regarding health and health care, tailored to fit their preferences.
5. Community links. Promote community-clinical partnerships and services aimed at managing and improving health at the community level.
6. Care continuity. Improve coordination and communication within and across organizations.
7. Optimized operations. Continuously improve health care operations to reduce waste, streamline care delivery, and focus on activities that improve patient health.
III - Supportive Policy Environment
8. Financial incentives. Structure payment to reward continuous learning and improvement in the provision of best care at lower cost.
9. Performance transparency. Increase transparency on health care system performance.
10. Broad leadership. Expand commitment to the goals of a continuously learning health care system.

Informatics is of course central to the notion of the learning healthcare system by capturing, analyzing, and acting on data from the entire spectrum of care. There is another figure in the report that provides a "schematic" of the healthcare system that allows all of the critical informatics challenges and opportunities to be enumerated. This figure shows that the overall patient care experience begins from science, moving to evidence of what from the science improves patient care, followed by the delivery of that best care that will ideally result in the optimal patient outcomes and satisfaction. When any of these elements is carried out suboptimally, there are missed opportunities, waste, and harm. The only additions I would make to this figure would be feedback loops among the elements, i.e., the patient experience informs new science, evidence, and care, while the care experience feeds back to science and evidence, and so forth.

Informatics plays a role in each of these elements as well as the transitions between them. Starting with science, informatics increasingly plays a role in both driving and facilitating science. Informatics allows the science to learn from new discoveries in the data and also helps the scientist manage and analyze that data. It helps the clinical researchers select the best science to select and then evaluate for the evidence. Informatics also allows the best evidence to get implemented as care through methods such as clinical decision support. It also optimizes the care experience through quality measurement and improvement. In addition, informatics engages not only the patient and their caregivers but also other providers through health information exchange. Informatics also provides "safety rails" of sorts through maintaining safety, reducing error, facilitating privacy and security, and promoting adherence to standards. There is really no aspect of informatics that cannot be connected to this schematic.

By the same token, there is no aspect of informatics that cannot be related in some way to the continuous learning healthcare system. For this reason, this new IOM report presents a vision and all the grand challenges for the entire healthcare system as well as the role of informatics within it. Of course, vision alone is not enough, and we now must turn our attention to implementing it. Encouraging studies and reports are already coming out, such as the learning healthcare system operationalized at Group Health in Seattle [18], coordinated care projects implemented by Medicare to reduce hospital readmissions [19], the "Choosing Wisely" initiative to reduce unnecessary and potential harmful tests and treatments [20], and new science making the vast findings of genomics clinically "actionable" [21]. As with many other IOM reports, this report presents a robust context for the work of informatics to improve health and the healthcare system and points a way forward for doing so.

References

1. Dick, R., Steen, E., et al., eds. (1991). The Computer-Based Patient Record: An Essential Technology for Health Care. Washington, DC. National Academies Press.
2. Dick, R., Steen, E., et al., eds. (1997). The Computer-Based Patient Record: An Essential Technology for Health Care, Revised Edition. Washington, DC. National Academies Press.
3. Anonymous (1996). Telemedicine: A Guide to Assessing Telecommunications in Health Care. Washington, DC. National Academies Press.
4. Anonymous (2000). Networking Health: Prescriptions for the Internet. Washington, DC. National Academies Press.
5. Anonymous (1997). For the Record: Protecting Electronic Health Information. Washington, DC. National Academies Press.
6. Kohn, L., Corrigan, J., et al., eds. (2000). To Err Is Human: Building a Safer Health System. Washington, DC. National Academies Press.
7. Aspden, P., Corrigan, J., et al., eds. (2004). Patient Safety - A New Standard for Care. Washington, DC. National Academies Press.
8. Anonymous (2001). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC. National Academies Press.
9. Greiner, A. and Knebel, E., eds. (2003). Health Professions Education: A Bridge to Quality. Washington, DC. National Academies Press.
10. Yong, P. and Olsen, L. (2010). The Healthcare Imperative: Lowering Costs and Improving Outcomes - Workshop Series Summary. Washington, DC. National Academies Press. http://iom.edu/Reports/2011/The-Healthcare-Imperative-Lowering-Costs-and-Improving-Outcomes.aspx.
11. Anonymous (2012). Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC. National Academies Press. http://www.iom.edu/Reports/2011/Health-IT-and-Patient-Safety-Building-Safer-Systems-for-Better-Care.aspx.
12. Eden, J., Wheatley, B., et al., eds. (2008). Knowing What Works in Health Care: A Roadmap for the Nation. Washington, DC. National Academies Press. http://www.iom.edu/Reports/2008/Knowing-What-Works-in-Health-Care-A-Roadmap-for-the-Nation.aspx.
13. Olsen, L., Grossman, C., et al. (2011). Learning What Works: Infrastructure Required for Comparative Effectiveness Research. Washington, DC. National Academies Press. http://www.iom.edu/Reports/2011/Learning-What-Works-Infrastructure-Required-for-Comparative-Effectiveness-Research.aspx.
14. Grossman, C. and McGinnis, J. (2010). The Digital Infrastructure for a Learning Health System: Foundation for Continuous Improvement in Health and Health Care - Workshop Summary. Washington, DC. National Academies Press. http://www.iom.edu/Reports/2011/Digital-Infrastructure-for-a-Learning-Health-System.aspx.
15. Friedman, C., Wong, A., et al. (2010). Achieving a nationwide learning health system. Science Translational Medicine, 2(57): 57cm29. http://stm.sciencemag.org/content/2/57/57cm29.full.
16. Smith, M., Saunders, R., et al. (2012). Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC. National Academies Press. http://iom.edu/Reports/2012/Best-Care-at-Lower-Cost-The-Path-to-Continuously-Learning-Health-Care-in-America.aspx.
17. Redberg, R. (2012). Getting to best care at lower cost. Archives of Internal Medicine: Epub ahead of print.
18. Greene, S., Reid, R., et al. (2012). Implementing the learning health system: from concept to action. Annals of Internal Medicine, 157: 207-210.
18. Brown, R., Peikes, D., et al. (2012). Six features of Medicare coordinated care demonstration programs that cut hospital admissions of high-risk patients. Health Affairs, 31: 1156-1166.
20. Cassel, C. and Guest, J. (2012). Choosing wisely: helping physicians and patients make smart decisions about their care. Journal of the American Medical Association, 307: 1801-1802.
21. Feero, W. (2012). Determining actionability of genetic findings in clinical practice. ACP Internist, July/August 2012. http://www.acpinternist.org/archives/2012/07/genomics.htm.

What is "DMICE," a "Track," a "Certificate?" The Jargon of the OHSU Informatics Program

In my paper that gave my definitions of the terminology of the biomedical and health informatics (BMHI) field, I noted the challenge of how confusing all the jargon could be. I realize now that this problem is exacerbated by the additional jargon we add on top of it in our educational program at Oregon Health & Science University (OHSU). Unfortunately, the complexity of our program makes there no simple solution, and the best approach is to try to define the jargon as simply and succinctly as possible.

So let me start with the first sub-question in the title of this post: What is DMICE? DMICE is the Department of Medical Informatics & Clinical Epidemiology, which is one of 26 academic departments in the School of Medicine at OHSU. A public university in the state of Oregon focused mostly on the health sciences, OHSU has Schools of Medicine, Nursing, and Dentistry, along with a School of Pharmacy administered jointly with Oregon State University. DMICE sits among other more traditionally named departments in the OHSU School of Medicine, such as Medicine, Surgery, and Medical & Molecular Genetics. As Chair of DMICE, I report to the Dean of the OHSU School of Medicine.

As in most departments in universities, DMICE has a wide variety of educational and research programs. One of those is the Biomedical Informatics Graduate Program. This is the umbrella term used to describe all of the educational programs dealing with informatics and related disciplines in DMICE. The program is offered at graduate level, i.e., students must have a baccalaureate degree to be admitted.

The Biomedical Informatics Graduate Program features several tracks, each of which represent a focus of study within BMHI. The tracks are partially overlapping, representing our view that our overall program is focused on the larger BMHI, even though students and professionals work within specific areas of the field.

Before there were tracks in the program, the original focus of the program was in clinical informatics, which was originally called medical informatics. We prefer to call this portion of the program the clinical informatics track now, which indicates its broader focus beyond informatics related to the work of medical doctors. Clinical informatics includes other branches of healthcare and even areas beyond the healthcare system, such as consumer health informatics. The clinical informatics track of our program focuses on informatics delivered mainly at the level of individual, whether in the role of a patient or as a consumer outside of the healthcare system.

The second track of our program, the bioinformatics and computational biology (BCB) track, has more of a focus on informatics at cellular and molecular level. The term bioinformatics refers to a focus on genomics and related areas, while the computational biology term indicates a strong emphasis on computational methods.

We recently added a third track to the program, the health information management (HIM) track. There is actually substantial overlap between HIM and clinical informatics, recognizing that HIM is evolving from management of paper records to electronic records. HIM has a different history from informatics as a so-called allied health profession, but it is increasingly moving toward electronic data systems in healthcare, i.e., clinical informatics. Our HIM track is accredited by the Commission on the Accreditation of Health Informatics and Information Management (CAHIIM), and students completing the program are eligible to sit for the Registered Health Information Administrator (RHIA) credential.

One of the advantages of the system of tracks is the ability to add additional tracks. I would like to see us add in the future a track for public health informatics, which would certainly have overlap with the clinical informatics and probably the others as well.

The tracks in our programs offer various degrees and certificates. One degree offered by all of the tracks is the Master of Science (MS). This was the original degree offered by the program and is a traditional research master's degree, which culminates in a thesis. The clinical informatics and HIM tracks also offer a non-thesis master's degree, sometimes referred to as a professional master's degree, the Master of Biomedical Informatics (MBI). The MBI has the same curriculum as the MS but replaces the master's thesis with a less-intensive capstone project. We also allow the capstone project to be an internship experience where the student gains real-world experience in an operational setting, such as a healthcare organization or a company.

Perhaps one of the lesser understood credentials in our program is the Graduate Certificate, which is offered in the clinical informatics and HIM tracks. Unlike master's degrees (which actually vary greatly but represent a generally known quantity), certificates between and even within different fields vary substantially. In many disciplines, the term Graduate Certificate is coming to represent a graduate-level educational experience that is not quite as much as a master's degree. In some universities, a Graduate Certificate is part of a continuing education or professional development program, sometimes even distinct from the graduate program. In our program, the Graduate Certificate is properly viewed as a subset of our master's degree (even though the eight three-credit one-quarter courses are enough to be a master's degree in some institutions). Students in the Graduate Certificate program take the same courses as those in the master's degree, only fewer of them.

The Graduate Certificate was developed when we first started offering distance learning courses in what we then called medical informatics. We thought that many of the students in our program who already had doctoral degrees would not be interested in a whole master's degree. Over time we did find that some were interested in a master's degree, so eventually expanded the distance learning offerings to that level.

Our graduate program also offers a Doctor of Philosophy (PhD) program in the clinical informatics and BCB tracks. (In reality, someone in the HIM track could progress to the PhD level in the clinical informatics track.) Just as the Graduate Certificate is a subset of the master's degree program (for the clinical informatics and HIM tracks), the master's degree programs are in turn a subset of the PhD program. We call the master's curriculum within the PhD the knowledge base, to which students add advanced research courses and a dissertation.

Because the higher-level programs are supersets of the more basic programs, we call this the building-block model of our program, indicating that students can start at the entry level for a given track and potentially progress all the way to the PhD (if their career goals warrant). The following figure depicts the building-block model of the program.

We also offer fellowship programs at both the predoctoral (PhD student) and postdoctoral (those with a doctoral degree already, who usually pursue a master's degree as a fellow) levels. Those who are fellows are mostly funded by training grants and other scholarships. Our main training grant is funded by the National Library of Medicine (NLM), a institute within the National Institutes of Health (NIH). In some ways, the fellowship program is a form of financial aid, as except for the funding provided and a work area, we treat fellows no different from other students.

There is some additional jargon from our program to round out this discussion. One is the 10x10 ("ten by ten") course. The 10x10 course is a program we started in partnership with our professional association, the American Medical Informatics Association (AMIA), in 2005. Its name was based on an estimated need to train 10,000 physicians and nurses (one each in all of the 5000+ US hospitals) in informatics by the year 2010. Of course we encouraged others, even non-healthcare professionals, to enroll in the course. The reason for mentioning the course here is that its curriculum is identical to the introductory course in the OHSU clinical informatics and HIM track, BMI 510 - Introduction to Biomedical & Health Informatics. In fact, those who complete the 10x10 course (which is a continuing education course) can optionally take the BMI 510 final exam and, if they obtain a grade of B or better, receive credit for BMI 510 in our graduate program. About 30% of the 1300+ people who have completed the 10x10 course have done so, and many have gone to further courses in the program. In fact, because of the building-block model, some of have progressed up from the Graduate Certificate to the master's degree programs and in two cases all the way to the PhD program.

Another item of jargon is the clinical informatics subspecialty. This refers to the new subspecialty for physicians that was recognized by the American Board of Medical Specialties in 2011. While plans for the certification process and training programs are still under development, this will represent a career pathway for physicians that gives professional recognition for the work they do in informatics. Unlike any other subspecialty in medicine, it will be available to physicians from all primary specialties, such as Medicine, Surgery, Radiology, and Pathology.

I hope this post clarifies and demystifies some of the confusing jargon of the informatics field and the educational program at OHSU. There is no easy answer to simplifying all this terminology, since there are so many distinct paths and credentials within the field. The approach is to try to understand it all from a comprehensive framework like the one I laid out here.