Saturday, December 8, 2012

Update on the OHSU ONC University-Based Training Grant


It seems like yesterday that the Office of the National Coordinator for Health IT (ONC) HITECH Program was starting up and the funding of the ONC Workforce Development Programs occurred. Now, however, those programs are winding down after nearly three years. In this posting, I will provide an update of OHSU's University-Based Training (UBT) Grant.

As I noted in my last update on the program, we had committed all of our training slots by last summer. For the rest of the grant, we have been and will continue to be leading the students through the program and aiming to launch as many as we can into successful informatics careers.

OHSU was one of nine universities (or consortia of universities) awarded a UBT grant in April, 2010. We were funded to educate 148 students, 135 in the Type 1 (one year) category and 13 in the Type 2 (longer than a year) category. Since we already had existing programs, we ran our grant essentially as a financial aid program for our existing Graduate Certificate (GC, Type 1) and Master of Biomedical Informatics (MBI, Type 2) programs. Because of the time constraints imposed by the ONC funding, we made a condition of admission being completion of the GC program as an accelerated part-time student in one year and of the MBI as a full-time student in one and a half years.

Although both the GC and MBI programs are available online, we chose to require the ONC Type 2 students to be full-time on-campus students. But our GC students have been from all over the US, paralleling the national distribution of all of our online students.

The figure below shows graphically how students have flowed through the program as of this time. A total of 493 students applied for funding, of which we were able to accept 178, meaning a 36% rate of acceptance. It was a challenge to have to turn down so many qualified applicants applying for funding, although all of them had the option of enrolling in the program as self-funded (tuition-paying) students. In fact, 65 (21%) of those who applied but were not funded did enroll in that manner.
Probably the single biggest challenge of the program has been a relatively high rate of student attrition. For most students, this has been due to their underestimating the time commitment of the programs. Even the accelerated part-time commitment to the GC program has been challenging for many students, especially those working full-time or with other (e.g., family) commitments. As a result, 41 students have withdrawn from the program after starting it, although 13 of them have continued as self-funded students on a more part-time basis.

With the fall academic quarter of 2012 winding down, we will likely have a total of 97 graduates (66%) by the end of this term. There are 38 students still progressing through the program. Combined with the 13 who have moved to self-funded status, this should enable us to hit our target of 148 graduates overall for the grant.

Of course, the UBT students have not been the only students in the OHSU informatics program during this time. Even if we were to slightly fall short of our UBT program goals, we have contributed many other people to the informatics workforce. Since the UBT program started, a total of 228 other students have enrolled in the GC or MBI programs, and 96 have graduated.

The UBT grant has also had other benefits to our program. Recognizing that informatics is not a "spectator sport," we developed practicum and internship programs for the GC (one quarter or 11 weeks) and MBI (two quarters or 22 weeks) programs respectively. These experiences have give all of our UBT students real-world experiences in a variety of settings, from healthcare organizations to companies to others. We have even developed a process for our online students to find opportunities and pursue them. Our alumni and other networks have been helpful in identifying these experiences. A second important benefit to the program has been the ability to hire a career development specialist, providing career counseling for the first time in the history of our program.

One of the challenges as the UBT grant winds down is how to sustain these additional benefits. One way we are trying to sustain them is to roll them out to all other (i.e., non-UBT) students in the program. So now, for example, self-funded students can pursue practicum and internship experiences as well as avail themselves to our career development specialist. In the long run, however, we will need the base of enrollment provided by something comparable to the UBT grant to maintain all of these services.

My hope going into the UBT grant was that enrollment in our and other informatics programs would increase initially from the UBT funding and then be sustained by increased interest and career opportunities in the field. As I have noted in another recent posting, there is plenty of data to indicate that the opportunities are there. But as we have always noted, getting out the word about informatics careers has always been a challenge, especially among younger people without much experience in the healthcare system (and knowing why informatics is so important to it). To that end, we are planning to ramp up a marketing campaign to attract more interest in all of our educational programs, which I will detail in a subsequent posting.

As the UBT program winds down, I am confident in its success. I believe it has demonstrated the need for more informatics professionals and provided a foundation for educating them, even if the results might take longer than was hoped to meet the acute needs of the meaningful use program. I am also confident there will continue to be need for professionals working at that interface between healthcare and its information.

Saturday, November 10, 2012

Next Steps for the Clinical Informatics Subspecialty: Initial Certification Exam and Board Review Course

Progress on the clinical informatics subspecialty for physicians continues to be steady but slow. Some additional information recently became available at a late-breaking panel at the American Medical Informatics Association (AMIA) 2012 Annual Symposium this past week, although there are still many unanswered questions, especially the acute question of exactly who will be eligible to take the certification exam during the grandfathering era. In addition to myself, the panelists consisted of two AMIA members who were question-writers, Charles Safran and Justin Starren, and two representatives from the American Board of Preventive Medicine (ABPM), Joshua Lipsman and William Greaves. The latter two described the process of applying for and taking the certification exam, along with maintenance of certification once the exam was passed. The details of these will be posted to the ABPM Web site by the end of 2012. (Disclaimer: What follows in the rest of this posting is my recollection of what I heard during the panel; the "truth" will be what is posted on the ABPM Web site before the end of this year.)

The first certification exam will be offered during a two-week period in October, 2013. It will be a computerized exam offered at Pearson VUE Test Centers. Registration for the exam will open in the spring of 2013, with ABPM reviewing each application to determine if the applicant is eligible to sit for the exam. The discussion that follows assume some knowledge about the subspecialty from pervious postings in this blog, including:
One thing that was clear from the ABPM representatives was that the starting point for their criteria for the certification exam and eligibility will be based literally on the original proposal submitted by AMIA to ABMS in 2009. This document has not been shared publicly yet, though it (or a summary of it) presumably will be made available when the ABPM releases details of the exam at the end of this year. This means that the "grandfathering" era of training requirements will be determined by exactly what was put forth in the proposal. My understanding of what was said on the panel is that there will be two paths to eligibility to take the exam:
  1. Practice pathway - To meet this criteria, someone has to have "practiced" clinical informatics for a minimum of 25% time (i.e., 10 hours per week). The work can be any type of informatics work, including research.
  2. Non-traditional fellowship pathway - To meet this criteria, someone must have completed a formal training program that involves a commitment of time that is double that of the practice pathway, i.e., a minimum of 50% time over three years or full-time over 1.5 years. This amount of commitment must have been made over a 36-month period during the previous five years. The baseline list of programs will consist of 33 programs listed in the ABMS proposal, which was comprised of the 18 programs that were funded under the National Library of Medicine (NLM) training grant program at the time of the ABMS submission, along with 15 others who were members of the AMIA Academic Forum. This list was never intended for this purpose; some of the programs funded by NLM do not have a focus in clinical informatics and furthermore, a new cycle of NLM training grant funding has been awarded, with six programs no longer funded by NLM and two new programs added to the 12 whose funding was continued. Likewise, there are now many more members of the AMIA Academic Forum. The original purpose of the list was to demonstrate that training programs in the field currently exist. The ABMS representatives did state that graduates from other informatics programs would be considered for the non-traditional fellowship pathway in the application process. (Note to Oregon Health & Science University [OHSU] students and alumni: My interpretation of the time commitment required for the non-traditional fellowship is that graduates of our master's degree programs would be eligible if they had completed the program over a three-year span, since these programs take in total the equivalent of 1.5 years of full-time study. Of course, I will also argue to ABPM that at least from a content standpoint, our Graduate Certificate program graduates should also be eligible. But these are ultimately for ABPM to decide.)
The cost of the exam will consist of an exam fee and an additional registration fee that will be lower for those in the non-traditional fellowship pathway than the practice pathway. The registration fee covers the cost of an assessment of whether the applicant meets the qualifications to sit for the exam. The purpose of the fee is to stand up the resources that will be required to  determine whether the applicant is qualified. (If not, he or she will get his or her exam fee returned.)

My role on the panel was to describe a new activity I will be leading for AMIA, which is serving as Course Director of the AMIA Clinical Informatics Board Review course. The course will initially be offered as an in-person course, probably three days in length, but will later be moved to an on-line format. Those taking the exam will also have access to a test bank of practice questions. One question I am sure many potential certification candidates will ask is, should I take the course? One group for whom this will be a complicated question is those in the Practice Pathway track with little or no formal training in informatics. As noted in some of blog posts, the volume of material that will be assessed on the exam is substantial. It is unlikely that someone with no previous exposure to this material will be able to master it with three days of review. A portion of the material is covered in the 10x10 ("ten by ten") course that I teach, but in reality, a full Graduate Certificate or possibly even a master's degree will likely be required. Even more problematic is that most graduate programs have not yet aligned with the core content specified in the ABMS proposal. Based on an analysis of our program, we are exploring how to put a substantial amount of the content in as few courses as possible, which may require some substantial revision of our curriculum. (We do not plan to make our curriculum identical to the core content, since we have students with other aspirations besides clinical informatics certification. We will, however, try to create a pathway through the program as efficiently as possible.)

A final question that always comes up in these discussions is, what about certification for others in the field, including physicians who do not have a board certification and are thus ineligible for the subspecialty. As noted in my previous posts, AMIA has established an advanced interprofessional task force to make recommendations for certification of other doctoral-level (healthcare doctorates such as PharmD, DPM, DPT, and DNP, as well as PhDs) informaticians. This certification will likely have core content that is very similar to the physician subspecialty, which is really not very physician-specific.

It is clear that there will be a number of bumps in the road along the way to development of the clinical informatics subspecialty and other certifications in clinical informatics. I still believe, however, that certification will be important for the recognition of those who work as clinical informatics professionals. I am looking forward to seeing many who are reading this post in the Board Review course or in other informatics training courses in which I am involved. I am actually hoping to be eligible to take the exam myself (I was one of the last cohorts of those trained in internal medicine who received lifetime certification, so I am still board-certified!), so I may be studying with some of you for the test next summer.

Postscript (November 15, 2012): A couple minor errors about required qualifications have been corrected, which are that the practice pathway and non-traditional fellowship must be completed over a three-year period during a five-year span and that for those who are not deemed eligible to take the certification exam, only the exam fee and not the registration fee will be refunded. In addition, two new links of note should be mentioned:

Sunday, November 4, 2012

Selected Again to the Clinical Informatics All-Star Team


For the third time in the three years of the award, I have been selected among the Top 25 Clinical Informaticists by Modern Healthcare magazine. It is quite an honor to be selected among this group, and to be called out for my contributions to the field. The Modern Healthcare web site has a number of pages, some of which require a subscription. The pages include:
It is very fitting that this award be announced right as I am scheduled to kick off the American Medical Informatics Association (AMIA) 2012 Annual Symposium, for which I served as Scientific Program Committee Chair. This is the 27th consecutive fall for me to attend this meeting (sometimes under the guise of SCAMC or MEDINFO). The meeting also has special significance for me this year in addition because it is taking place in my home town of Chicago. A nice interview of me about the conference was prepared at the beginning of the event.

An additional contribution I will be making to the field will be announced at the meeting, as AMIA lets is be known that I will be serving as Director of the Clinical Informatics Subspecialty Board Review course. (More on that in a coming post!)

Wednesday, October 31, 2012

Post-doctoral and Pre-doctoral Training in Biomedical Informatics - Positions Available at OHSU

Although our educational program seems to be getting much of its recognition these days from our online program that focuses on educating informatics practitioners, we are still very committed to more intensive full-time education on our campus, especially in the training of future researchers in the field. I am pleased to report that Oregon Health & Science University (OHSU) had its National Library of Medicine Biomedical Informatics Training Grant renewed this year for another five-year cycle, with additional funding and opportunity provided by another institute of the National Institutes of Health, the National Institute of Dental and Craniofacial Research (NIDCR, which has a focus on oral and cranial health).

We are therefore seeking qualified applicants for our pre-doctoral and post-doctoral research training fellowship program in biomedical informatics for the 2013-2014 academic year. Additional support is available from the U.S. Department of Veterans Affairs and Kaiser-Permanente Northwest Region. By providing a structured research experience, with the option of course work and/or pursuit of a degree, the fellowship program prepares trainees to enter the academic community and undertake programs of independent biomedical informatics research, or to take leadership positions in the growing number of hospital and/or commercial efforts in biomedical informatics.

Our fellowship opportunities including the following:

NLM – Pre- and post-doctoral opportunities are available. The pre-doctoral positions are open to any qualified individual with a bachelor’s degree or higher, with the fellow expected to pursue a doctoral degree. The post-doctoral positions are open to any qualified individual with a doctoral degree. The fellow is expected to pursue a research project and is strongly encouraged to also pursue a master’s degree in biomedical informatics at OHSU.

U.S. Department of Veterans Affairs – The position is open to an MD and has a clinical practice component. The fellow is expected to work on a project with the VA’s advanced clinical information system and is strongly encouraged to also pursue a master’s degree in biomedical informatics at OHSU.

Kaiser-Permanente – This position is open to an MD and has a clinical practice component. The fellow is expected to work on a project with Kaiser-Permanente’s advanced clinical information systems and pursue coursework at OHSU.

NIDCR – Pre- and post-doctoral opportunities are available. Our goal is to prepare the fellow/trainee to enter the academic community and become an independent researcher, or to take leadership positions in the growing number academic and/or commercial efforts in oral health informatics. The pre-doctoral positions are open to any qualified individual with a bachelor’s degree or higher, with the fellow expected to pursue a doctoral degree. The post-doctoral positions are open to any qualified individual with a doctoral degree. The fellow is expected to pursue a research project and is strongly encouraged to also pursue a master’s degree in biomedical informatics at OHSU.

For more information about our biomedical informatics graduate program, visit our Web site. More detailed information about our fellowship programs is also available.

Program faculty and staff from OHSU will be present to meet with prospective students at the Career Expo of the AMIA 2012 Annual Symposium from November 4-7, 2012. Additional information is also available from Lauren Ludwig.

Tuesday, October 23, 2012

Health IT Workforce Policy: Roundtable with Rep. Susanne Bonamici

The First Congressional District of Oregon has a history of leadership in health information technology (HIT) workforce policy. As one who lives in the district, I am delighted that its Congressional representative, Susanne Bonamici, is holding a roundtable discussion on HIT workforce policy this week. I am honored to participate, especially in light of my role on the Workforce Group of the Office of the National Coordinator for HIT (ONC) HIT Policy Committee. Of course I also lead the informatics education program at Oregon Health & Science University (OHSU), an institution of which many employees are constituents of Rep. Bonamici and which is a well-known leader in HIT workforce development.

According to Rep. Bonamici, "As Oregon leads the nation in developing coordinated health delivery systems, the role of technology in managing information flow between patients, providers, and insurance companies will become increasingly important. The results of this roundtable discussion will inform legislative and oversight actions in the health IT field for the 113th Congress." She has further elaborated three topics for the roundtable:
  • What can be done to better prepare practitioners to effectively use health IT and electronic health records?
  • How can technology developers better design their products to fit with workflow in a medical setting?
  • Does current curriculum adequately prepare technology developers and future medical service providers to meet industry and patient needs?
I heartily concur with her statement about Oregon's leadership in coordinated delivery systems and the importance of information in such systems. Regardless of one's political views, or the fate of "Obamacare" after next month's Presidential election, our healthcare system needs to become more coordinated, patient-centered, and focused on rewarding value over quantity of care. This vision is exemplified by the recent report from the Institute of Medicine, Best Care at Lower Cost: The Path to Continuously Learning Health Care in America, which makes a compelling case for a learning health care system infused with all aspects of HIT and informatics.

I also agree with the importance of the topics she raises. Namely, how do we prepare healthcare professionals to work in a data-driven healthcare system that strives for value and coordination of care? Likewise, what sort of professional workforce do we need to optimize information systems and their use? How do we best train that workforce and do so in a cost-effective manner? How do we nurture the industry in which they work? How do we advance the science, practice, and innovation?

What advice do I have for Rep. Bonamici concerning HIT workforce development? Since her first topic mentions the training of clinicians, I will begin there. In this realm, we need to insure that 21st century healthcare professionals are knowledgeable and savvy in their use of HIT to deliver optimal care, not only to individual patients, but entire populations. Current practitioners will need to be brought up to date through continuing education, while students will require their curricula to be updated to reflect what it takes to provide care in a data-driven learning healthcare system. Probably the best way to deliver this care is through the growing push for "interprofessional" education that brings all future healthcare professionals in the same classroom, preparing them to work in the future care system that is increasingly coordinated and team-based.

Delving further, all current and future healthcare professionals need to learn about tools that capture data and transform it into information and knowledge that can be used for both individual and population-based care. As such, these clinicians need to have competency in the following:
  • Understanding the importance of the efficient and accurate collection of data not only for the electronic health record (EHR) of their delivery organization, but also how that data will populate other sources, such as personal health records (PHRs), health information exchange (HIE), and the public health system
  • While not needing the detailed knowledge of informaticians, healthcare practitioners must learn the informatics basics of data standards, interoperability of clinical data systems, and clinical decision support
  • Understanding the critical need to protect patient privacy and confidentiality
  • Have skills in finding pertinent and reputable sources of knowledge to be applied to patient care, i.e., being expert in searching knowledge sources, from scientific literature (e.g., Pubmed) to summary textbooks and related sources
  • Being able to deliver care in teams, using data and information to plan and guide care
  • Mastering the concepts of healthcare quality measurement and improvement, i.e., understanding the rationale but also the limitations of quality measures, and how electronic data systems will facilitate them
  • Being able to look forward and see other changes coming to healthcare in the future, such as personalized medicine, based on genomics and bioinformatics, and the role that it will play in the delivery of healthcare
  • Understanding the ways that care is delivered over telecommunications networks, i.e., telemedicine and telehealth
  • Be competent in the use of a wide variety of information devices, from computers to tablets to smartphones and the networks to which they connect
Implementing this vision and putting the technology and skills to use it will not come without the help of HIT and informatics professionals. The future HIT workforce must be ready to develop, implement, and evaluate HIT systems. These professionals will certainly need to be savvy with technology, but they will also need to be highly knowledgeable in the science and best practice of informatics to make sure these tools serve clinicians, patients, and the healthcare system. They must also be cognizant of the concerns of how HIT impacts workflow and patient safety. We must also adapt the curricula of our educational programs for future HIT professional workforce needs by increasing our experiential learning and preparing for "big data," predictive analytics, and advanced forms of clinical decision support.

Also, in settings like the First Congressional District, with its Silicon Forest, we will need to train additional individuals who will innovate and lead in industry. Our informatics program at OHSU has been working for some time to increase its collaboration with the HIT industry. It must be remembered that not only does HIT have the potential to improve health and healthcare, but in places like the First Congressional District, it can also contribute to economic development.

There must also be a cadre of academic informaticians who perform research and develop the future. In addition to education, a wide variety of innovative informatics research takes place at OHSU. Examples of the areas addressed include:
  • Care coordination - the Integrated Care Coordination Information System (ICCIS) project led by Dr. David Dorr collects data and it makes available for analysis to enhance coordination of care.
  • Secondary use of clinical data - my own research involves the development of data sets to augment the development of systems and algorithms to allow re-use of data in clinical systems.
  • EHR simulation for patient safety - working with intensive care physician Dr. Jeffery Gold, we are developing realistic simulations to improve both users of EHRs and the systems themselves.
As in most scientific fields, government has funded the basic research that industry does not. Exemplary government agencies that support this research include the National Library of Medicine (NLM) and the Agency for Healthcare Research and Quality (AHRQ). I encourage Rep. Bonamici to continue her support for NLM and AHRQ, and encourage her to be in the lead in making sure to maintain and consider increasing their funding.

Another policy challenge going forward will be to maintain the investment made through the HITECH Act. Educational institutions associated with the First District were highly successful in competing for funding provided through the HITECH workforce development programs. OHSU has delivered on the work it was funded to do under the two programs for which grants were awarded to it:
  • Development of national HIT curriculum, which focused initially on community colleges but then generalized to all institutions of higher education. OHSU served as one of five curriculum development centers as well as served as the National Training and Dissemination Center (NTDC) that distributed and supported the curriculum.
  • Educating its share of students in the University-Based Training (UBT) Program. Most of our graduates have been highly successful in fulfilling the roles envisioned for them in the expanding HIT workforce.
Now, of course, the HITECH funding is winding down, ending in early 2013 for the curriculum project and mid-2013 for the UBT program. While student tuition and other sources of funding will fill in some of the declining federal investment, there is still a case for investing in the common good of educating not only the HIT workforce, but also clinicians who use HIT in a more coordinated and learning healthcare system.

I hope that Rep. Bonamici can spur the entire community, from academia to industry to healthcare organizations, to work together to improve healthcare delivery as well as advance the HIT industry. Both are vital to the future health and economic well-being of Oregonians.

Thursday, October 11, 2012

Improving Patient Safety Through Electronic Health Record Simulation


Most tools used in medicine require knowledge and skills of both those who develop them and use them. Even tools that are themselves innocuous can lead to patient harm. For example, while it is difficult to directly harm a patient with a stethoscope, patients can be harmed when improper use of the stethoscope leads to them having tests and/or treatments they do not need (or not having tests and treatments they do need). More directly harmful interventions, such as invasive tests and treatments, can harm patients through their use as well.

To this end, health information technology (HIT) can harm patients. The direct harm from computer use in the care of patients is minimal, but the indirect harm can potentially be extraordinary. HIT usage can, for example, store results in an electronic health record (EHR) incompletely or incorrectly. Clinical decision support may lead clinician astray or may distract them with unnecessary excessive information. Medical imaging may improperly render findings. Search engines may lead clinicians or patients to incorrect information. The informatics professionals who oversee implementation of HIT may not follow best practices to maximize successful use and minimize negative consequences. All of these harms and more were well-documented in the Institute of Medicine (IOM) report published last year on HIT and patient safety [1].

One aspect of HIT safety was brought to our attention when a critical care physician at our medical center, Dr. Jeffery Gold, noted that clinical trainees were increasingly not seeing the big picture of a patient's care due to information being "hidden in plain sight," i.e., behind a myriad of computer screens and not easily aggregated into a single picture. This is especially problematic where he works, in the intensive care unit (ICU), where the generation of data is vast, i.e., found to average about 1300 data points per 24 hours [2]. This led us to perform an experiment where physicians in training were provided a sample case and asked to review an ICU case for sign-out to another physician [3]. Our results found that for 14 clinical issues, only an average of 41% of issues (range 16-68% for individual issues) were uncovered.

While this rate of error is alarmingly high, it must be remembered that the physicians reviewing the case were new to it, i.e., not taking direct care of the patient. It is also important to remember that paper-based information management in the ICU has always had its problems as well. Nonetheless, there clearly needs to be improvement both in the presentation of information as well as the training of users to access it.

As we were completing this work, a new round of funding was announced for a grant program, Improving Patient Safety Through Simulation Research, by the Agency for Healthcare Research and Quality (AHRQ). I am pleased to report that we have been awarded a three-year, $1 million grant to pursue this work. Dr. Gold is Principal Investigator of the project and several faculty in our informatics program, including myself, are Co-Investigators. Our efforts will focus on continuing the development of the simulation through development of new cases, aiming to improve both the user interface as well as user training, and disseminating our results. Of course, we are not the only research group evaluating improved methods to find and use data in the EHR, our simulation approach is novel and will hopefully add additional insights to improving the use of HIT in the clinical setting.

References
1. 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.
2. Manor-Shulman, O., Beyene, J., et al. (2008). Quantifying the volume of documented clinical information in critical illness. Journal of Critical Care, 23: 245-250.
3. Steiger, D., March, C., et al. (2012). Use of simulation to assess and improve electronic medical record usage. American Journal of Respiratory and Critical Care Medicine, 185: A2890. http://ajrccm.atsjournals.org/cgi/reprint/185/1_MeetingAbstracts/A2890.

Sunday, October 7, 2012

Health IT Jobs Growth Outpacing Qualified People to Fill Them?

I recently wrote about the substantial growth in health information technology (HIT) employment that has exceeded all predictions. That is the good news. The less-good news is that healthcare organizations still face substantial challenges in meeting their HIT staffing needs. This was borne out by a recent survey of the College of Healthcare Information Management Executives (CHIME), which assessed HIT workforce staffing issues and found that the shortfalls of needed HIT staff still persist [1]. Perhaps this is not surprising, given the growth in the use of HIT and overall employment as noted in my previous post.

A total of 163 out of CHIME's 1400+ members, mostly chief information officers (CIOs), responded to the survey during July, 2012. A similar survey had been administered in 2010. All sizes (from 0-99 to 1000+) and types (academic, community, and multi-hospital) of healthcare provider organizations responded.

About 67% of respondents reported that their organizations were experiencing shortages. This was compared with 59% in 2010. The highest category having unmet needs was academic centers, reported by 82%. About 12% of organizations reported 15% or more positions being open.

The survey asked about skills most often in demand, which included:
  • Clinical software implementation and support staff (e.g., EHR, CPOE) - 74%
  • Infrastructure staff - 47%
  • Business software implementation and support staff - 45%
About 71% said IT staff shortages could jeopardize an enterprise IT project, while 58% said they would definitely or possibly affect meeting meaningful use criteria for incentive funding. About 85% also expressed concerns about being able to retain current staff.

The survey also assessed awareness of the HIT Workforce Programs of the Office of the National Coordinator for Health IT (ONC) [2]. Only 67% were aware of the ONC workforce programs, with 12% of those respondents reporting that they had hired graduates from them. (Unfortunately the survey did not distinguish knowledge of and hiring from community college versus university-based programs.)

The respondents reported their chief strategies for coping with IT staff shortages, which included:
  • Hiring third-party consultants - 28%
  • Hiring from within the organization and retraining - 20%
  • Other (multiple strategies) - 18%
  • Using recruiters to find and place qualified staff - 15%
  • Depending on HIT vendors to provide implementation staff - 8%
  • Other kinds of outsourcing - 6%
  • Developing a pipeline of students by collaborating with local colleges and universities - 2%
The most important attributes and competencies deemed to be needed by HIT professionals included:
  • Actual experience in a health IT shop
  • Clinical informatics experience
  • Education in IT theory and practice in a real-world setting
The most important attribute or competency that was least likely to be mentioned by respondents was:
  • Coding knowledge
  • Willingness to start “at the bottom” in an IT shop
  • Education in IT theory and practice in a classroom setting
Those surveyed were also asked what competencies or areas of knowledge were generally lacking in candidates being considered for IT staff positions. The most frequently mentioned were:
  • Lack of knowledge of healthcare and related IT applications
  • Lack of practical experience
  • Lack of experience with an organization’s system
  • Inability to interact successfully with front-line users
Overall, the CHIME survey demonstrates that adequate numbers of HIT professionals with appropriate skills are a bottleneck to HIT implementation in healthcare organizations. The highest unmet needs for staffing are in clinical areas, with individuals most sought after being those with healthcare and/or HIT experience, applied education in both theory and practice, and good people skills. Knowledge of ONC workforce programs is by no means universal, and even those with knowledge of the programs are hiring relatively few graduates, although the survey did not distinguish levels of knowledge or hiring from community college versus university-based programs. Clearly while job opportunities in HIT are strong, many organizations are experiencing challenges fulfilling their HIT hiring needs. There is also onus on educational programs to train individuals with adequate skills as quickly and efficiently as possible.

References

1. Anonymous (2012). Demand Persists for Experienced Health IT Staff. Ann Arbor, MI, College of Healthcare Information Management Executives. http://www.cio-chime.org/chime/press/surveys/pdf/CHIME_Workforce%20_survey_report.pdf.

2. 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.

Saturday, September 29, 2012

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.

Thursday, September 13, 2012

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.

Monday, September 10, 2012

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.

Tuesday, September 4, 2012

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.

Sunday, August 26, 2012

Data Entry: An Under-Discussed Grand Challenge for Informatics


Everyone, including this blog writer, has been touting the virtues of the vast troves of data already or soon to be available in the electronic health record (EHR), which will usher in the learning healthcare system [1, 2]. There is sometimes unbridled enthusiasm that the data captured in clinical systems, perhaps combined with research data such as gene sequencing, will effortlessly provide us knowledge of what works in healthcare and how new treatments can be developed [3, 4]. The data is unstructured? No problem, just apply natural language processing [5].

I honestly share in this enthusiasm, but I also realize that it needs to be tempered, or at least given a dose of reality. In particular, we must remember that our great data analytics and algorithms will only get us so far. If we have poor underlying data, the analyses may end up misleading us. We must be careful for problems of data incompleteness and incorrectness.

There are all sorts of reasons for inadequate data in EHR systems. Probably the main one is that those who enter data, i.e., physicians and other clinicians, are usually doing so for reasons other than data analysis. I have often said that clinical documentation can be what stands between a busy clinician and going home for dinner, i.e., he or she has to finish charting before ending the work day.

I also know of many clinicians whose enthusiasm for entering correct and complete data is tempered by their view of the entry of it as a data blackhole. That is, they enter data in but never derive out its benefits. I like to think that most clinicians would relish the opportunity to look at aggregate views of their patients in their practices and/or be able to identify patients who are outliers in one measure or another. Yet a common complaint I hear from clinicians is that data capture priorities are more driven by the hospital or clinic trying to maximize their reimbursement than to aid clinicians in providing better patient care.

Another challenge for clinicians is the time required for electronic data entry. There is no question that the 20th century means of clinical documentation, mostly consisting of scribbling illegible notes on paper, was much easier and faster than typing and/or clicking. While I think that few clinicians want to go back to hand-written notes, there is an appeal of their ease of use, at least for the person doing the entry.

Related to the time for electronic data entry is the "tension" between structured data, which makes aggregation and analysis easier, and "flexible" (or narrative) data, which allows the clinician to tell the story of the patient [6]. Many clinicians report that excess structuring of data (i.e., pointing and clicking) loses the story of the patient, although those who process the data know that structured data is easier to analyze.

An additional challenge for electronic data entry for clinicians is the shift of the focus from the patient to the computer. This was exemplified in a cartoon published earlier this year in JAMA that showed a 7-year-old's sketch of an exam room with the physician hunched over the computer, his back turned away from the patient and her family [7] (the sketch viewable at http://jama.jamanetwork.com/article.aspx?articleid=1187932).

An excellent example of the promise but limitations of current data entry systems was recently documented by Parsons et al. [8], who found in a wide sample of primary care EHRs in New York City that the accuracy of data for measuring breast cancer screening quality measures was highly variable due to differing practices in documentation, workflow, and related factors. While some physicians had the quality of their care measured accurately, for many others it was underestimated due to data limitations and not the care they provided.

I cannot claim to have easy answers to this grand challenge, but two related aspects of it sit in front of us:

  1. We need to find better and faster ways for clinicians to enter data into the EHR that allow data whose quality is good enough to be re-used for other purposes, such as research, quality measurement and improvement, and public health.
  2. We must reward clinicians for their efforts in entering high-quality data. We must allow them to see aggregate views of patients in their practices and be able to identify outliers. We must also engage them in research, quality improvement, and other system uses of their data.

In short, the concept of "garbage in, garbage out" still remains a problem for computers and information technology nearly a half-century after it was coined. In healthcare, we must give clinicians the best tools and incentives for them to participate in the learning healthcare system. For informatics, the problem of data entry is a grand challenge every bit as important as how to make use of its growing quantity, since the knowledge derived from that data will only be as good as the quality of what is input.

References

1. 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.
2. Greene, S., Reid, R., et al. (2012). Implementing the learning health system: from concept to action. Annals of Internal Medicine, 157: 207-210.
3. McCarty, C., Chisholm, R., et al. (2010). The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Genomics, 4(1): 13. http://www.biomedcentral.com/1755-8794/4/13.
4. Rea, S., Pathak, J., et al. (2012). Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project. Journal of Biomedical Informatics, 45: 763-771.
5. Nadkarni, P., Ohno-Machado, L., et al. (2011). Natural language processing: an introduction. Journal of the American Medical Informatics Association, 18: 544-551.
6. Rosenbloom, S., Denny, J., et al. (2011). Data from clinical notes: a perspective on the tension between structure and flexible documentation. Journal of the American Medical Informatics Association, 18: 181-186.
7. Toll, E. (2012). The cost of technology. Journal of the American Medical Association, 307: 2497-2498.
8. Parsons, A., McCullough, C., et al. (2012). Validity of electronic health record-derived quality measurement for performance monitoring. Journal of the American Medical Informatics Association, 19: 604-609.