It is important for all who work in informatics, especially those in academia, to acknowledge both the benefits as well as the harms of health information technology (HIT). As noted in a recent entry in this blog, an updated systematic review of HIT evaluation research shows there is clear value to the use of HIT in improve health and healthcare delivery outcomes.
But we also know that like any clinical intervention, HIT can have adverse effects. If not implemented safely, it can not only lead to harm, but also waste a great deal of money. We must therefore, just like with other interventions, implement HIT, especially the electronic health record (EHR), using best practices and keeping an eye for possible harms. The framework for HIT safety was laid out in a report from the Institute of Medicine in 2011. This report included a chapter on how to avoid "e-iatrogenesis". The lead author on the chapter was Oregon Health & Science University (OHSU) informatics faculty, Dr. Joan Ash, and I was one of her co-authors.
I are now pleased to report that Dr. Ash has co-led (along with Drs. Dean Sittig and Hardeep Singh of the University of Texas Health Science Center at Houston) important further contributions in HIT safety, which are the SAFER (Safety Assurance Factors for EHR Resilience) guides that were released by the Office of the National Coordinator for HIT (ONC) last week. These guides are based on the best known research evidence for optimizing implementation of the electronic health record (EHR) safety. Recent ONC Director Dr. Jacob Reider wrote in the ONC HIT Buzz Blog about the importance of the SAFER guides.
The SAFER guides, along with research about the value of HIT, also drive home the importance of informatics expertise in implementing HIT. Well-trained informaticians bring knowledge and experience to HIT implementation, including being aware of the existence of tools like the SAFER guides. These guides will allow informaticians and others to apply best practices to EHR implementation.
Thursday, January 23, 2014
Tuesday, January 21, 2014
Clinical Informatics Fellowship: OHSU Plans
The complicated world of informatics training will become even more complicated with the advent of clinical informatics fellowships for physicians. As I have stated before, there are serious challenges for these fellowships, especially starting in 2018 when the "grandfathering" option is no longer available and the only way for a physician to become board-certified will be via a formal fellowship accredited by the Accreditation Council for Graduate Medical Education (ACGME).
We are developing plans to offer a clinical informatics subspecialty fellowship at Oregon Health & Science University (OHSU). The administrative home of the program will be the OHSU Department of Medicine (one of nine primary specialties designated by ACGME to be able to offer fellowships and have them accredited through their respective Residency Review Committee [RRC]), although physicians from any specialty can join the fellowship and become board-certified. The OHSU Department of Medical Informatics & Clinical Epidemiology (DMICE) will provide day-to-day administration and leadership of the program. We hope to launch the program in July, 2014.
Clinical informatics fellows will divide their time among three major activities (with approximate time proportions):
In the meantime, we anticipate many physicians (especially those not eligible for the board-certified pathway) as well as non-physicians will still want to train via the graduate education model. We plan no major changes in our graduate programs, other than their evolution to keep up with the field and provide new content options, such as those based on our collaboration with Epic. Of course, as certification for other (non-physician) professions emerges, we and others will need to develop programs that might train using the clinical fellowship model.
As I have written before, the biggest challenge for all who want to start clinical informatics fellowships is funding. Because all medical trainees in residency and fellowship programs are paid, the clinical fellowship model will require that someone pay the stipend and tuition of the fellow. I am hoping to convince some clinical departments as well as our hospital to fund some positions. We have a couple takers so far.
Of note, our hospital administration has a conservative interpretation of CMS rules regarding billing, and will not allow clinical informatics to bill in their primary specialties. This makes the funding model a lot more challenging, in that trainee stipends will not be fundable via clinical practice.
There is good enthusiasm at our institution for the fellowship, but enthusiasm does not always translate to funding. Without funding from the clinical departments or the heath system at our institution and elsewhere, the pipeline of future subspecialists may dry up and impede capacity for development of the subspecialty. In the long run, my hope is that ACGME and others will allow more innovation beyond the bounds of the current model of fellowship training.
We are developing plans to offer a clinical informatics subspecialty fellowship at Oregon Health & Science University (OHSU). The administrative home of the program will be the OHSU Department of Medicine (one of nine primary specialties designated by ACGME to be able to offer fellowships and have them accredited through their respective Residency Review Committee [RRC]), although physicians from any specialty can join the fellowship and become board-certified. The OHSU Department of Medical Informatics & Clinical Epidemiology (DMICE) will provide day-to-day administration and leadership of the program. We hope to launch the program in July, 2014.
Clinical informatics fellows will divide their time among three major activities (with approximate time proportions):
- Clinical informatics projects (40%) – project work under the auspices of program faculty, with a focus on systems for their clinical specialty
- Informatics education (40%) – taking classes in the OHSU informatics graduate program
- Clinical practice (20%) – seeing patients in their primary specialty
In the meantime, we anticipate many physicians (especially those not eligible for the board-certified pathway) as well as non-physicians will still want to train via the graduate education model. We plan no major changes in our graduate programs, other than their evolution to keep up with the field and provide new content options, such as those based on our collaboration with Epic. Of course, as certification for other (non-physician) professions emerges, we and others will need to develop programs that might train using the clinical fellowship model.
As I have written before, the biggest challenge for all who want to start clinical informatics fellowships is funding. Because all medical trainees in residency and fellowship programs are paid, the clinical fellowship model will require that someone pay the stipend and tuition of the fellow. I am hoping to convince some clinical departments as well as our hospital to fund some positions. We have a couple takers so far.
Of note, our hospital administration has a conservative interpretation of CMS rules regarding billing, and will not allow clinical informatics to bill in their primary specialties. This makes the funding model a lot more challenging, in that trainee stipends will not be fundable via clinical practice.
There is good enthusiasm at our institution for the fellowship, but enthusiasm does not always translate to funding. Without funding from the clinical departments or the heath system at our institution and elsewhere, the pipeline of future subspecialists may dry up and impede capacity for development of the subspecialty. In the long run, my hope is that ACGME and others will allow more innovation beyond the bounds of the current model of fellowship training.
Saturday, January 11, 2014
Live Streaming of OHSU Informatics Conference Launched
This past week we launched live-streaming of our weekly informatics conference. I was the speaker for the first conference, and can report that all went well. You can see for yourself by watching the video of event. (We have been recording videos of our conference for several years, which are all available). My slides can also be downloaded.
Our initial attendance was quite good, with about 50 people present at the live conference and the number connected by streaming as follows:
Nonetheless, this new method for widening the reach of our weekly conference, and our academic informatics program, was a great success. I look forward to this continuing in the future, and being able to further disseminate the value of our program.
Our initial attendance was quite good, with about 50 people present at the live conference and the number connected by streaming as follows:
- Start - 34
- 15 minutes - 41
- 30 minutes - 50
- 45 minutes - 49
- End - 45
Nonetheless, this new method for widening the reach of our weekly conference, and our academic informatics program, was a great success. I look forward to this continuing in the future, and being able to further disseminate the value of our program.
Thursday, January 9, 2014
Reaffirming the Value of Health Information Technology
The year 2013 was not a great year for health information technology (HIT), punctated by events happening in the beginning, middle, and end of the year. The year started with a commentary in the journal Health Affairs, noting that the promises that drove the purported benefits for HIT in the US federal HIT investment were still unmet [1]. This came on the heels of the IOM report in late 2012 raising concerns about HIT safety [2]. In the middle of the year, growing concern rose over the ability of healthcare organizations to meet the various government regulatory requirements (typified by a news release from the American College of Physicians), from the meaningful use criteria to ICD-10. Late in the year, while not HIT per se, was the Healthcare.gov debacle, which tarnished the general image of IT for healthcare-related activities, especially those led by government agencies.
This new year has started better, in particular with the publication of an update of a systematic review that reaffirms the value, with some caveats, of what we are trying to accomplish in HIT. Systematic reviews do not report new primary research data, but instead synthesize all of the existing research to catalog and summarize our knowledge (sometimes generating new knowledge in the process through techniques like meta-analysis) as well as identify gaps that may exist. This week saw the publication of a systematic review that was the latest update in a series of systematic reviews about HIT dating back to 2006. The bottom line result was that the proportion of studies reporting beneficial results for HIT continues to be high, varying from 51-73% in the different categories of use that were assessed. When studies reporting mixed or neutral results were factored in, the proportion finding negative results only were small, varying from 0-16%. The functions most commonly assessed in the studies were clinical decision support and computerized provider order entry. A figure from a posting in the ONC Health IT Buzz Blog depicts this all graphically quite well.
There is another important finding reported in this analysis, which is that about half of all studies were done using commercial HIT systems. One of the most important "findings" of the earlier iterations of this systematic review was a preponderance of studies from a small number of "HIT leader" institutions, mostly using home-grown electronic health record systems. This always raised some concerns about generalizability, i.e., could other institutions without the resources of such institutions replicate what the leaders were able to accomplish.
The authors also noted some caveats to the evidence base and provided a set of recommendations for improving it going forward. One caveat is the limited nature by which systems have been studied, usually done as before-after analyses and often not providing adequate contextual description for the results. The authors also note a potential for publication bias, both in studies that may not be reported at all to adverse consequences that are not adequately measured or described in those that are published. I heartily concur with the authors' recommendations that future studies include more robust experimental designs and a focus on those that provide better understanding of the context of the findings and how they provide value for health and healthcare delivery.
Nonetheless, this new systematic review reaffirms the scientific evidence for work in informatics going forward. We still must try to build systems that meet the broadest needs and provide value, but also evaluate them with enlightened skepticism. I am also encouraged by the growing number of studies using commercial HIT systems and not taking place in informatics exemplar institutions. This affirms the value of the approach we are taking in the academic program at OHSU, which is to build a well-trained informatics workforce in our educational program to disseminate and apply the best science and practice. It also demonstrates the value of new partnerships we are establishing, such the one launched between OHSU and Epic that will allow research and education on top of a state-of-the-art commercial platform.
References
1. Kellermann, AL and Jones, SS (2013). What will it take to achieve the as-yet-unfulfilled promises of health information technology? Health Affairs. 32: 63-68.
2. Anonymous (2012). Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC, National Academies Press.
3. Jones, SS, Rudin, RS, et al. (2014). Health information technology: an updated systematic review with a focus on meaningful use. Annals of Internal Medicine. 160: 48-54.
This new year has started better, in particular with the publication of an update of a systematic review that reaffirms the value, with some caveats, of what we are trying to accomplish in HIT. Systematic reviews do not report new primary research data, but instead synthesize all of the existing research to catalog and summarize our knowledge (sometimes generating new knowledge in the process through techniques like meta-analysis) as well as identify gaps that may exist. This week saw the publication of a systematic review that was the latest update in a series of systematic reviews about HIT dating back to 2006. The bottom line result was that the proportion of studies reporting beneficial results for HIT continues to be high, varying from 51-73% in the different categories of use that were assessed. When studies reporting mixed or neutral results were factored in, the proportion finding negative results only were small, varying from 0-16%. The functions most commonly assessed in the studies were clinical decision support and computerized provider order entry. A figure from a posting in the ONC Health IT Buzz Blog depicts this all graphically quite well.
There is another important finding reported in this analysis, which is that about half of all studies were done using commercial HIT systems. One of the most important "findings" of the earlier iterations of this systematic review was a preponderance of studies from a small number of "HIT leader" institutions, mostly using home-grown electronic health record systems. This always raised some concerns about generalizability, i.e., could other institutions without the resources of such institutions replicate what the leaders were able to accomplish.
The authors also noted some caveats to the evidence base and provided a set of recommendations for improving it going forward. One caveat is the limited nature by which systems have been studied, usually done as before-after analyses and often not providing adequate contextual description for the results. The authors also note a potential for publication bias, both in studies that may not be reported at all to adverse consequences that are not adequately measured or described in those that are published. I heartily concur with the authors' recommendations that future studies include more robust experimental designs and a focus on those that provide better understanding of the context of the findings and how they provide value for health and healthcare delivery.
Nonetheless, this new systematic review reaffirms the scientific evidence for work in informatics going forward. We still must try to build systems that meet the broadest needs and provide value, but also evaluate them with enlightened skepticism. I am also encouraged by the growing number of studies using commercial HIT systems and not taking place in informatics exemplar institutions. This affirms the value of the approach we are taking in the academic program at OHSU, which is to build a well-trained informatics workforce in our educational program to disseminate and apply the best science and practice. It also demonstrates the value of new partnerships we are establishing, such the one launched between OHSU and Epic that will allow research and education on top of a state-of-the-art commercial platform.
References
1. Kellermann, AL and Jones, SS (2013). What will it take to achieve the as-yet-unfulfilled promises of health information technology? Health Affairs. 32: 63-68.
2. Anonymous (2012). Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC, National Academies Press.
3. Jones, SS, Rudin, RS, et al. (2014). Health information technology: an updated systematic review with a focus on meaningful use. Annals of Internal Medicine. 160: 48-54.
Monday, January 6, 2014
OHSU Informatics Live-Streaming Weekly Conferences
The Oregon Health & Science University (OHSU) Biomedical Informatics Program is undertaking something new with the beginning of the new year: live streaming of our weekly conferences. These conferences, which take place on Thursdays at 11:30 am Pacific time, will be available live from the following URL:
http://www.ohsu.edu/edcomm/flash/flash_player.php?params=1%60/dmice.flv%60live&width=640&height=480&title=DMICE%20Conference&stream_type=live
Our conferences have been offered to students, faculty, and others for over two decades, and we have been recording them and posting the videos on the Web dating back to 2007 at the URL:
http://oninformatics.com
We will continue to post the recordings 1-2 days after the conferences as we always have.
It should be noted that we sometimes have conferences at different times, due either to visitors speaking on a different day or when we are overloaded with student thesis defenses near the end of the academic term. There are also times when we will not be streaming the conferences, such as talks by job candidates or about student internship projects that had some proprietary nature to them. All of our conferences are posted on our department calendar. We are also in the process of creating a listserv for streamed conference announcements, with more details to appear on our Web site soon.
Of course, we still hope people who are local will come to the actual conference. We will continue to provide one incentive to show up in person, which is serving lunchtime pizza. (Unfortunately we will not be able to deliver pizza to those viewing remotely!)
We also hope to make the conference as interactive to the remote audience as we do to those in attendance, including the ability to ask questions of the speaker. We plan to do this via our Twitter feed, @OHSUInformatics, using the hashtag #q&a. The Twitter feed will be monitored during and after the talk, and in the event of too many questions, it will be up to the discretion of the speaker to choose. We will also want to give those in attendance the opportunity to ask questions as well.
I will be kicking off the first live-streamed conference on January 9, 2014 with a presentation on the state of the informatics field and our program: OHSU Informatics: State of the Field, State of the Program.
We (and others at OHSU) have streamed conferences before, so we do not anticipate any technical glitches. We suspect that our method for capturing speaker questions may need refinement over time, so may adjust our approach using Twitter. In any case, I am delighted that we are now adding live streaming of our conferences to the way we interact with the rest of the world.
http://www.ohsu.edu/edcomm/flash/flash_player.php?params=1%60/dmice.flv%60live&width=640&height=480&title=DMICE%20Conference&stream_type=live
Our conferences have been offered to students, faculty, and others for over two decades, and we have been recording them and posting the videos on the Web dating back to 2007 at the URL:
http://oninformatics.com
We will continue to post the recordings 1-2 days after the conferences as we always have.
It should be noted that we sometimes have conferences at different times, due either to visitors speaking on a different day or when we are overloaded with student thesis defenses near the end of the academic term. There are also times when we will not be streaming the conferences, such as talks by job candidates or about student internship projects that had some proprietary nature to them. All of our conferences are posted on our department calendar. We are also in the process of creating a listserv for streamed conference announcements, with more details to appear on our Web site soon.
Of course, we still hope people who are local will come to the actual conference. We will continue to provide one incentive to show up in person, which is serving lunchtime pizza. (Unfortunately we will not be able to deliver pizza to those viewing remotely!)
We also hope to make the conference as interactive to the remote audience as we do to those in attendance, including the ability to ask questions of the speaker. We plan to do this via our Twitter feed, @OHSUInformatics, using the hashtag #q&a. The Twitter feed will be monitored during and after the talk, and in the event of too many questions, it will be up to the discretion of the speaker to choose. We will also want to give those in attendance the opportunity to ask questions as well.
I will be kicking off the first live-streamed conference on January 9, 2014 with a presentation on the state of the informatics field and our program: OHSU Informatics: State of the Field, State of the Program.
We (and others at OHSU) have streamed conferences before, so we do not anticipate any technical glitches. We suspect that our method for capturing speaker questions may need refinement over time, so may adjust our approach using Twitter. In any case, I am delighted that we are now adding live streaming of our conferences to the way we interact with the rest of the world.
Sunday, December 29, 2013
Annual Reflections at the End of 2013
It has become a tradition for this blog for my last posting of the calendar year to be a message reflecting on the past year and looking ahead to the following one. As such, this marks my fifth annual message, dating back to 2009, 2010, and 2011, and 2012. I continue to enjoy writing this blog, with it serving as a venue to discuss issues of importance to myself and the biomedical and health informatics field.
This past year of 2013 was another gratifying year, as well as a transitional one, as the work and funding under the Health Information Technology for Clinical and Economic Health (HITECH) Act, at least for myself and our program at Oregon Health & Science University (OHSU), drew to a close. Indeed, this blog has paralleled the HITECH Act since the inception of both, getting its start in early 2009 around the time of the passage of the American Recovery and Reinvestment Act (ARRA), the economic stimulus legislation passed in 2009 in the early days of the Obama Administration. HITECH itself is now transitioning, as the most of its grant funding has ended and its incentive payments for EHR adoption are tapering off.
HITECH has certainly been a career-defining era for many of us working in informatics. As with many large initiatives, especially government ones, it has had its successes and failures. It is interesting to read my postings from the early days, after the legislation was passed but prior to it being implemented, followed by the reality that not everything in HITECH, nor the Obama Administration, has gone as we might have hoped. Nonetheless, I do feel comfortable that the government and the taxpayers received their money's worth for the work that our informatics program was funded to do. We created a useful new curricular resource and trained a number of people that resulted in new informatics careers being launched. But going forward, HITECH will increasingly be seen in the rear-view mirror.
In this transitional year, a number of other new initiatives came about, which point the direction of the future for myself and our program. For myself, 2013 is ending with my becoming a "board-certified" clinical informatician. While the new subspecialty is still a work in progress, I was pleased to be part of 450 or so individuals who passed the first offering of new board exam. I was also proud that 40 of those who passed received at least part of their informatics education in our program at OHSU. It was also great to see the press postings from OHSU as well as AMIA, with the former picked up by a local business magazine and the latter described in the health IT press.
For our department, one of the most important new initiatives of the last year was the launch of the Informatics Discovery Lab (IDL). I had the opportunity to give a talk about the IDL in an interesting format of 5 minutes total with exactly 15 seconds per slide at a local forum called IgniteHealth. The IDL was also described in an interview with its leader, faculty member Dr. Aaron Cohen, in Oregon Business, a local business magazine. We also received a good deal of notice about one of the first tangible outcomes of the IDL, which is our partnership with EHR vendor Epic to use their system for research and educational purposes. This initiative too made it into the HIT press: Healthcare IT News, Healthcare Informatics, and HIT Consultant.
Despite the end of the HITECH funding and the modest decline in enrollment expected after it, we are still moving forward and innovating with our educational program. A number of new initiatives are in the works and likely to reach fruition in 2014. Recognizing the need to stay relevant, we are forging ahead in new directions where we believe the field is headed. One of these initiatives is to add coursework in data analytics, with the eventual likelihood of an entire track in this area. In the meantime, we are also developing plans for a clinical informatics fellowship that will complement our other fellowship programs. We are also pleased to be working with other programs developing clinical informatics fellowships, being able to provide coursework and related expertise to them.
Another opportunity for our department has been to become involved in the curriculum transformation process for OHSU medical students. OHSU was also one of 11 medical schools receiving grants from the American Medical Association to accelerate change in education. My role in the grant is to develop competencies and curricula for the data-driven future of medicine that will be forthcoming as care delivery models change. The new OHSU curriculum will also feature more informatics than it ever has before.
Finally, I had the opportunity to weigh in on the year in review for the California Health Care Foundation iHealthBeat year in review.
As for what lies ahead in 2014, I believe it will mainly be built on the foundation of new post-HITECH activities started in 2013. The clinical informatics subspecialty will be important, although I also hope we will see more progress in professional recognition and certification for the larger majority of non-physician (and even non-clinical) informatics professionals. There is a large and important role for all who work in informatics, not only those in clinical (healthcare) areas, but other areas of the field as well. This will be especially so, for example, as advances in clinical research informatics enable other areas, from translational bioinformatics to public health informatics, disseminate their progress into healthcare and individual health spheres. Although each subarea of informatics is distinct, I expect their work to increasingly overlap going forward. For example, as bioinformatics and genomics have more impact in health and healthcare, the underlying informatics will necessarily become more similar.
From a program standpoint, I am equally certain that initiatives such as the IDL will be drivers of our research directions. While government sources of research support will still be important and form the bedrock for advancing the science, it will be equally critical to collaborate with industry and other partners to disseminate the fruits of that research. Academia is unexcelled for making discoveries but industry is just as critical to making them available to a wide audience. The era of "home-grown" informatics systems is receding, with the need to build and study on top of commercial platforms in widespread use becoming more critical.
As for this blog, I plan to continue in the same manner as in the past, with postings only when I have something I believe is interesting to write about, and not serving as my stream of consciousness. I have nothing against the latter types of blogs, but my preferred approach (and time availability!) is the former. I hope to maintain the focus on the issues in informatics that are most core to me, but not hesitating to branch out when appropriate.
This past year of 2013 was another gratifying year, as well as a transitional one, as the work and funding under the Health Information Technology for Clinical and Economic Health (HITECH) Act, at least for myself and our program at Oregon Health & Science University (OHSU), drew to a close. Indeed, this blog has paralleled the HITECH Act since the inception of both, getting its start in early 2009 around the time of the passage of the American Recovery and Reinvestment Act (ARRA), the economic stimulus legislation passed in 2009 in the early days of the Obama Administration. HITECH itself is now transitioning, as the most of its grant funding has ended and its incentive payments for EHR adoption are tapering off.
HITECH has certainly been a career-defining era for many of us working in informatics. As with many large initiatives, especially government ones, it has had its successes and failures. It is interesting to read my postings from the early days, after the legislation was passed but prior to it being implemented, followed by the reality that not everything in HITECH, nor the Obama Administration, has gone as we might have hoped. Nonetheless, I do feel comfortable that the government and the taxpayers received their money's worth for the work that our informatics program was funded to do. We created a useful new curricular resource and trained a number of people that resulted in new informatics careers being launched. But going forward, HITECH will increasingly be seen in the rear-view mirror.
In this transitional year, a number of other new initiatives came about, which point the direction of the future for myself and our program. For myself, 2013 is ending with my becoming a "board-certified" clinical informatician. While the new subspecialty is still a work in progress, I was pleased to be part of 450 or so individuals who passed the first offering of new board exam. I was also proud that 40 of those who passed received at least part of their informatics education in our program at OHSU. It was also great to see the press postings from OHSU as well as AMIA, with the former picked up by a local business magazine and the latter described in the health IT press.
For our department, one of the most important new initiatives of the last year was the launch of the Informatics Discovery Lab (IDL). I had the opportunity to give a talk about the IDL in an interesting format of 5 minutes total with exactly 15 seconds per slide at a local forum called IgniteHealth. The IDL was also described in an interview with its leader, faculty member Dr. Aaron Cohen, in Oregon Business, a local business magazine. We also received a good deal of notice about one of the first tangible outcomes of the IDL, which is our partnership with EHR vendor Epic to use their system for research and educational purposes. This initiative too made it into the HIT press: Healthcare IT News, Healthcare Informatics, and HIT Consultant.
Despite the end of the HITECH funding and the modest decline in enrollment expected after it, we are still moving forward and innovating with our educational program. A number of new initiatives are in the works and likely to reach fruition in 2014. Recognizing the need to stay relevant, we are forging ahead in new directions where we believe the field is headed. One of these initiatives is to add coursework in data analytics, with the eventual likelihood of an entire track in this area. In the meantime, we are also developing plans for a clinical informatics fellowship that will complement our other fellowship programs. We are also pleased to be working with other programs developing clinical informatics fellowships, being able to provide coursework and related expertise to them.
Another opportunity for our department has been to become involved in the curriculum transformation process for OHSU medical students. OHSU was also one of 11 medical schools receiving grants from the American Medical Association to accelerate change in education. My role in the grant is to develop competencies and curricula for the data-driven future of medicine that will be forthcoming as care delivery models change. The new OHSU curriculum will also feature more informatics than it ever has before.
Finally, I had the opportunity to weigh in on the year in review for the California Health Care Foundation iHealthBeat year in review.
As for what lies ahead in 2014, I believe it will mainly be built on the foundation of new post-HITECH activities started in 2013. The clinical informatics subspecialty will be important, although I also hope we will see more progress in professional recognition and certification for the larger majority of non-physician (and even non-clinical) informatics professionals. There is a large and important role for all who work in informatics, not only those in clinical (healthcare) areas, but other areas of the field as well. This will be especially so, for example, as advances in clinical research informatics enable other areas, from translational bioinformatics to public health informatics, disseminate their progress into healthcare and individual health spheres. Although each subarea of informatics is distinct, I expect their work to increasingly overlap going forward. For example, as bioinformatics and genomics have more impact in health and healthcare, the underlying informatics will necessarily become more similar.
From a program standpoint, I am equally certain that initiatives such as the IDL will be drivers of our research directions. While government sources of research support will still be important and form the bedrock for advancing the science, it will be equally critical to collaborate with industry and other partners to disseminate the fruits of that research. Academia is unexcelled for making discoveries but industry is just as critical to making them available to a wide audience. The era of "home-grown" informatics systems is receding, with the need to build and study on top of commercial platforms in widespread use becoming more critical.
As for this blog, I plan to continue in the same manner as in the past, with postings only when I have something I believe is interesting to write about, and not serving as my stream of consciousness. I have nothing against the latter types of blogs, but my preferred approach (and time availability!) is the former. I hope to maintain the focus on the issues in informatics that are most core to me, but not hesitating to branch out when appropriate.
Sunday, December 22, 2013
A Student Who Helped My Educator Aspirations
I believe that one of things that separates a good educator from a great one is that the latter is unafraid to have students who (a) know more about some or many topics than they do, (b) do not hesitate to point out errors in the teacher's content, or (c) are not afraid to speak their minds, including when they disagree with the teacher. I aspire to be a great teacher, and the attributes of trying to be one were reinforced to me this past fall when Keith Boone, aka @motorcycleguy, became a student in my introductory informatics course (and our Master of Biomedical Informatics program at Oregon Health & Science University).
I had been following Keith's health information technology (HIT) standards blog for a number of years when I started to get to know him. I always enjoyed and found value for my teaching in his explanations of HIT standards and related areas. Keith is one of those people who has a wealth of experience, providing knowledge and even wisdom, but without (until now) formal training. He is an excellent writer, not only in his tweets and blog, but also his book on CDA (Clinical Document Architecture). When Keith decided to pursue a formal education in informatics, I was thrilled when he chose our program.
In addition to being a diligent and successful student, Keith blogged and tweeted his way through his first term of courses this past fall. Some of his posts described his decision-making around going back to school and finding tools that worked for him. Others represented his reactions to discussion I try to elicit in the virtual classroom (which are manifested in threaded discussion forums), in particular on payment for physician-patient online communications, consumer health-related access to the Internet, and payment issues around telehealth. I replied to all of his posts in the class and to some of them on his blog.
Naturally some of his postings revolved around his area of expertise, namely standards. I thoroughly enjoyed his posting on noting the difficulty of using Pubmed to find information on standards, which also raised some issues around the academia-industry dichotomy in the standards community. I also got a chuckle out of his tweeting of my mentioning a new standards activity that is generating a great deal of interest and enthusiasm, which is the Fast Healthcare Interoperability Resources (FHIR, pronounced "fire"). I have to admit I felt a little anxiety going into the module on standards with Keith. He was already among my sources for expertise for the lecture, and naturally I wanted to make sure I had everything right. I am pleased to report that he provided some excellent corrections and feedback, mostly on the finer details, and this will benefit future students in having more precise explanations about the nuances of standards. (I also feel a little relief that I did not get anything wrong in a major way!)
I also enjoyed Keith's mid-course comments on what he was getting out of being a student and his wrap-up posting reflecting back on his first term in the program. While I cannot report his grade in the course due to FERPA (Family Educational Rights and Privacy Act, the educational equivalent of HIPAA), I can note that he did extremely well!
One of the most satisfying aspects of my work as an educator is seeing those I have taught go on to achieve great things. While the education I contributed to is never the sole reason for their success, it usually does contribute. Keith has already achieved a tremendous amount in his career, but I am confident I will feel even greater satisfaction when he achieves even more due in part to the education he received in our program.
I had been following Keith's health information technology (HIT) standards blog for a number of years when I started to get to know him. I always enjoyed and found value for my teaching in his explanations of HIT standards and related areas. Keith is one of those people who has a wealth of experience, providing knowledge and even wisdom, but without (until now) formal training. He is an excellent writer, not only in his tweets and blog, but also his book on CDA (Clinical Document Architecture). When Keith decided to pursue a formal education in informatics, I was thrilled when he chose our program.
In addition to being a diligent and successful student, Keith blogged and tweeted his way through his first term of courses this past fall. Some of his posts described his decision-making around going back to school and finding tools that worked for him. Others represented his reactions to discussion I try to elicit in the virtual classroom (which are manifested in threaded discussion forums), in particular on payment for physician-patient online communications, consumer health-related access to the Internet, and payment issues around telehealth. I replied to all of his posts in the class and to some of them on his blog.
Naturally some of his postings revolved around his area of expertise, namely standards. I thoroughly enjoyed his posting on noting the difficulty of using Pubmed to find information on standards, which also raised some issues around the academia-industry dichotomy in the standards community. I also got a chuckle out of his tweeting of my mentioning a new standards activity that is generating a great deal of interest and enthusiasm, which is the Fast Healthcare Interoperability Resources (FHIR, pronounced "fire"). I have to admit I felt a little anxiety going into the module on standards with Keith. He was already among my sources for expertise for the lecture, and naturally I wanted to make sure I had everything right. I am pleased to report that he provided some excellent corrections and feedback, mostly on the finer details, and this will benefit future students in having more precise explanations about the nuances of standards. (I also feel a little relief that I did not get anything wrong in a major way!)
I also enjoyed Keith's mid-course comments on what he was getting out of being a student and his wrap-up posting reflecting back on his first term in the program. While I cannot report his grade in the course due to FERPA (Family Educational Rights and Privacy Act, the educational equivalent of HIPAA), I can note that he did extremely well!
One of the most satisfying aspects of my work as an educator is seeing those I have taught go on to achieve great things. While the education I contributed to is never the sole reason for their success, it usually does contribute. Keith has already achieved a tremendous amount in his career, but I am confident I will feel even greater satisfaction when he achieves even more due in part to the education he received in our program.
Thursday, December 19, 2013
The Informatics Lessons of Healthcare.Gov
The debacle of the Healthcare.gov Web site rollout will serve as a case study in curricula of business, political science, informatics, and other fields of study for years to come. It is unfortunate that the toxic politics of healthcare reform obscure other interesting lessons to be learned about large-scale IT initiatives applied to complex problems, such as trying to match individuals to health insurance plans available in their area and determining who is eligible for federal subsidies.
I count myself among those who have waited years for healthcare reform, seen an imperfect (but better than the status quo) plan signed into law, and then observed its rollout botched from both a technical as well as a communications standpoint. My views on the Affordable Care Act (ACA, aka Obamacare), not the focus of this post, are that it was the best that could be achieved politically at the time, and that it will hopefully be improved over time. The goal of providing healthcare to all Americans, including those who are not insurable by market-based mechanisms, is still a laudable goal. I am also dismayed by those who want to see the ACA fail at all costs, almost as if the fact that real people will be losing real healthcare coverage (or not having it in the first place) did not matter. I also agree with those who note we cannot attribute blame of everything bad happening about health insurance to the ACA, i.e., health insurance costs continue to rise for reasons unrelated the ACA and employers would likely continue scaling back health insurance benefits regardless of whether or not the ACA were repealed. Well, maybe I did want to get some commentary in about the ACA after all, but the bottom line is that the pre-ACA status quo was not sustainable.
Nonetheless, what can we learn from the Heathcare.gov rollout from an informatics standpoint? One problem is clear, which is the federal procurement process for IT, about which even President Obama joked. This is issue is addressed well in context in a blog posting by Dr. David Blumenthal, the former Director of the Office of the National Coordinator for Health IT (ONC) who was appointed shortly after the first election of President Obama. Dr. Blumenthal noted the major differences between a typical large-scale federal IT procurement and the selection of an electronic health record (EHR) system for the large and venerable Partners Health System, which is anchored by two of the large Harvard Medical School teaching hospitals.
For the federal IT procurement, the agency (in this case, ONC) provides the specification and then in essence turns the process over to a separate contracting office in the government. This is in contrast to the Partners EHR decision, which was reached by a process that involved leadership guided by diverse expertise within the organization. This sounds to me like an informatics approach, from gathering the needs of the organization and giving voice to different stakeholders within it, to then seeing the entire selection process through to making a decision. Whether or not we call it "informatics," implementing a large complex IT project "takes a village" within organizations.
Another insightful blog posting comes from Clay Shirky, a well-known Internet commentator. He noted how the Healthcare.gov planning and rollout process defied well-known best practices for undertaking large, complex IT projects. Political necessities cannot bypass the reality of the incremental requirements gathering, setting reasonable timelines, and testing. Part of the problem, of course, is that the ACA needed to roll from a political standpoint in October, 2013. Delaying longer would push implementation into the middle of the 2014 elections, which would make those elections potentially more unpredictable.
But political timelines aside, everyone with knowledge of complex IT projects knows that no amount of political or other wishful thinking can make a project happen faster than is possible. John Halamka, a well-known informatics blogger, rightly pointed out that few people remember a project launching somewhat late, whereas more people remember for a longer time when projects go poorly and caused disruption, as Healthcare.gov has. I myself have always believed that one of the major limitations of the HITECH program was its highly compressed timeline, mostly related to its being funded by a short-term federal economic stimulus. This was certainly true for many of the grant-funded activities under HITECH, such as the regional extension centers (RECs) and the workforce development program. The RECs, which were funded at about the same as the workforce development programs, needed trained personnel immediately. Yet the workforce development training programs needed some lead time to be developed, and even furthermore the curriculum for those programs should have had enough development time before those.
In conclusion, while not everyone uses the word "informatics" in their descriptions of what happened and what should have been properly done with Healthcare.gov, it is clear that the type of approach advocated by most who are trained in informatics would be more likely to achieve the outcome resembling the Partners EHR implementation than the Healthcare.gov debacle. This is not to say that projects led by informatics experts never fail. However, the involvement of stakeholders, glued together by informaticians who understand healthcare, IT, and their interactions, would likely have a probability of greater success. I acknowledge the previous sentence is not evidence-based, since one cannot carry out randomized controlled trials in these sorts of complex interventions. But there is plenty of accumulated knowledge and wisdom on the best practices that emanate when sound informatics principles are applied [1-4], and these should guide any type of complex health IT implementation.
I am sure there will be more lessons that emerge from the Healthcare.gov experience, and hopefully honest scholars will be able to peel back the toxic politics and truly allow learning to take place. I also hope we can achieve sensible answers in our quest to provide basic, high-quality, and affordable healthcare to everyone in the United States.
References
1. Barnett, GO (1979). The use of computers in clinical data management: the ten commandments. Society for Computer Medicine Newsletter. 4: 6-8.
2. Bates, DW, Kuperman, GJ, et al. (2003). Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. Journal of the American Medical Informatics Association. 10: 523-530.
3. McDonald, CJ, Overhage, JM, et al. (2004). Physicians, information technology, and health care systems: a journey, not a destination. Journal of the American Medical Informatics Association. 11: 121-124.
4. Sittig, DF and Singh, H (2012). Rights and responsibilities of users of electronic health records. Canadian Medical Association Journal. 184: 1479-1483.
I count myself among those who have waited years for healthcare reform, seen an imperfect (but better than the status quo) plan signed into law, and then observed its rollout botched from both a technical as well as a communications standpoint. My views on the Affordable Care Act (ACA, aka Obamacare), not the focus of this post, are that it was the best that could be achieved politically at the time, and that it will hopefully be improved over time. The goal of providing healthcare to all Americans, including those who are not insurable by market-based mechanisms, is still a laudable goal. I am also dismayed by those who want to see the ACA fail at all costs, almost as if the fact that real people will be losing real healthcare coverage (or not having it in the first place) did not matter. I also agree with those who note we cannot attribute blame of everything bad happening about health insurance to the ACA, i.e., health insurance costs continue to rise for reasons unrelated the ACA and employers would likely continue scaling back health insurance benefits regardless of whether or not the ACA were repealed. Well, maybe I did want to get some commentary in about the ACA after all, but the bottom line is that the pre-ACA status quo was not sustainable.
Nonetheless, what can we learn from the Heathcare.gov rollout from an informatics standpoint? One problem is clear, which is the federal procurement process for IT, about which even President Obama joked. This is issue is addressed well in context in a blog posting by Dr. David Blumenthal, the former Director of the Office of the National Coordinator for Health IT (ONC) who was appointed shortly after the first election of President Obama. Dr. Blumenthal noted the major differences between a typical large-scale federal IT procurement and the selection of an electronic health record (EHR) system for the large and venerable Partners Health System, which is anchored by two of the large Harvard Medical School teaching hospitals.
For the federal IT procurement, the agency (in this case, ONC) provides the specification and then in essence turns the process over to a separate contracting office in the government. This is in contrast to the Partners EHR decision, which was reached by a process that involved leadership guided by diverse expertise within the organization. This sounds to me like an informatics approach, from gathering the needs of the organization and giving voice to different stakeholders within it, to then seeing the entire selection process through to making a decision. Whether or not we call it "informatics," implementing a large complex IT project "takes a village" within organizations.
Another insightful blog posting comes from Clay Shirky, a well-known Internet commentator. He noted how the Healthcare.gov planning and rollout process defied well-known best practices for undertaking large, complex IT projects. Political necessities cannot bypass the reality of the incremental requirements gathering, setting reasonable timelines, and testing. Part of the problem, of course, is that the ACA needed to roll from a political standpoint in October, 2013. Delaying longer would push implementation into the middle of the 2014 elections, which would make those elections potentially more unpredictable.
But political timelines aside, everyone with knowledge of complex IT projects knows that no amount of political or other wishful thinking can make a project happen faster than is possible. John Halamka, a well-known informatics blogger, rightly pointed out that few people remember a project launching somewhat late, whereas more people remember for a longer time when projects go poorly and caused disruption, as Healthcare.gov has. I myself have always believed that one of the major limitations of the HITECH program was its highly compressed timeline, mostly related to its being funded by a short-term federal economic stimulus. This was certainly true for many of the grant-funded activities under HITECH, such as the regional extension centers (RECs) and the workforce development program. The RECs, which were funded at about the same as the workforce development programs, needed trained personnel immediately. Yet the workforce development training programs needed some lead time to be developed, and even furthermore the curriculum for those programs should have had enough development time before those.
In conclusion, while not everyone uses the word "informatics" in their descriptions of what happened and what should have been properly done with Healthcare.gov, it is clear that the type of approach advocated by most who are trained in informatics would be more likely to achieve the outcome resembling the Partners EHR implementation than the Healthcare.gov debacle. This is not to say that projects led by informatics experts never fail. However, the involvement of stakeholders, glued together by informaticians who understand healthcare, IT, and their interactions, would likely have a probability of greater success. I acknowledge the previous sentence is not evidence-based, since one cannot carry out randomized controlled trials in these sorts of complex interventions. But there is plenty of accumulated knowledge and wisdom on the best practices that emanate when sound informatics principles are applied [1-4], and these should guide any type of complex health IT implementation.
I am sure there will be more lessons that emerge from the Healthcare.gov experience, and hopefully honest scholars will be able to peel back the toxic politics and truly allow learning to take place. I also hope we can achieve sensible answers in our quest to provide basic, high-quality, and affordable healthcare to everyone in the United States.
References
1. Barnett, GO (1979). The use of computers in clinical data management: the ten commandments. Society for Computer Medicine Newsletter. 4: 6-8.
2. Bates, DW, Kuperman, GJ, et al. (2003). Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. Journal of the American Medical Informatics Association. 10: 523-530.
3. McDonald, CJ, Overhage, JM, et al. (2004). Physicians, information technology, and health care systems: a journey, not a destination. Journal of the American Medical Informatics Association. 11: 121-124.
4. Sittig, DF and Singh, H (2012). Rights and responsibilities of users of electronic health records. Canadian Medical Association Journal. 184: 1479-1483.
Monday, December 9, 2013
Consider Giving a Gift to the OHSU Informatics Program
It is time for the annual giving drive of the Oregon Health& Science University (OHSU) Biomedical Informatics Program and I hope that
those of you looking for good causes to support will consider giving a philanthropic
gift to support the program. While our program is as strong and innovative as
ever, philanthropic gifts enable us to accelerate and expand our research as
well as provide support for our students.
This past year has been a transitional year for us, as we
have completed
our work funded by the Office of National
Coordinator for Health IT (ONC) and moved on to new activities. While we
have made this transition successfully, and continue to be known for innovation
and leadership in the field, we still face challenges ahead.
Our ONC funding provided tuition support the enabled about
130 people to launch new careers in clinical informatics and health information
management. We also wrapped up our work on the national health IT curriculum that will be
a resource for years to come. In
addition, our bioinformatics and computational biology program continued to
grow and thrive.
We have also been able to maintain some of the programs
started by the ONC funding and roll them out to all the tracks of the program.
One is our practicum and internship program that enables students to obtain
real-world experience to augment their academic studies. (If you have possible
practicum or internship experiences for our students, please let us know.) Another
program we have maintained is our career
development specialist, whose expertise has likewise been rolled out to all
tracks of the program. Our major challenge for continuing these programs is
finding sources of funding to keep them fully deployed.
As you may have read or seen, we have also undertaken new
initiatives this year. The most visible of these efforts was the launching of our
Informatics
Discovery Lab (IDL). We aim for the IDL to address the important challenges
that are facing healthcare and biomedical research and that require a
combination of informatics innovation and commercial collaboration. The first
fruit of the IDL is our new
partnership with Epic Systems Corp. that will enable us to use the Epic
electronic health record both in our teaching and in our research. We are in
dialogue with a number of other industry partners to also participate in the
IDL.
We have a number of other new initiatives underway. One is
to address the need for informaticians of all backgrounds to acquire more
skills in data analytics. We hope to fulfill this through developing new
courses and other educational activities, and possibly a new track in the
program. We are also recruiting
for new junior faculty to keep the program fresh and vibrant. Finally, we
plan this coming year to begin streaming our Thursday lunchtime research
conferences live, which will augment the recorded videos that we have been
posting after each event for several years. While we will not able to deliver
the pizza we serve locally to remote sites, we do hope those participating
remotely will be able to participate interactively via tweeting questions and
comments.
The ability to carry out these activities will be augmented
and accelerated with additional help that philanthropy can provide. I hope you
will consider providing
a gift that will allow us to reach our new goals more quickly and successfully. I give myself through a weekly deduction from my paycheck, and that is another option as well. Giving in any manner will help our students, faculty, and others associated with the program.
Saturday, December 7, 2013
I'm a Clinical Informatics Subspecialist!
I received notification this weekend that I passed the clinical informatics subspecialty certification exam, which means I can now proudly call myself a subspecialist in clinical informatics. I am delighted that the years of effort initially undertaken by the American Medical Informatics Association (AMIA) have culminated in this outcome.
What does this make me a specialist/expert in? I like the recent definition of the subspecialty by the Accreditation Council for Graduate Medical Education (ACGME): "Clinical informatics is the subspecialty of all medical specialties that transforms health care by analyzing, designing, implementing, and evaluating information and communication systems to improve patient care, enhance access to care, advance individual and population health outcomes, and strengthen the clinician-patient relationship."
I took the exam, administered by the American Board of Preventive Medicine (ABPM), in October. I was no doubt well-prepared by my prolific teaching of informatics, including being the Director of the AMIA Clinical Informatics Board Review Course. The exam covered the material of the core content outline of the field in a representative manner, even if the multiple-choice format somewhat limited the kinds of questions that could be asked.
I am also pleased to report that all OHSU faculty who sat for the clinical informatics subspecialty board exam passed it, which means that we have 6 clinical informatics sub specialists at OHSU. In addition to myself, this includes:
The work of building the specialty will now continue. We are doing our part at OHSU by continuing to develop our fellowship program we hope will be accredited by the ACGME when its rules are finalized next year. OHSU will likely offer continuing medical education (CME). And of course, OHSU will continue to be a leader in educating the rest of the informatics field as well in our graduate educational programs.
What does this make me a specialist/expert in? I like the recent definition of the subspecialty by the Accreditation Council for Graduate Medical Education (ACGME): "Clinical informatics is the subspecialty of all medical specialties that transforms health care by analyzing, designing, implementing, and evaluating information and communication systems to improve patient care, enhance access to care, advance individual and population health outcomes, and strengthen the clinician-patient relationship."
I took the exam, administered by the American Board of Preventive Medicine (ABPM), in October. I was no doubt well-prepared by my prolific teaching of informatics, including being the Director of the AMIA Clinical Informatics Board Review Course. The exam covered the material of the core content outline of the field in a representative manner, even if the multiple-choice format somewhat limited the kinds of questions that could be asked.
I am also pleased to report that all OHSU faculty who sat for the clinical informatics subspecialty board exam passed it, which means that we have 6 clinical informatics sub specialists at OHSU. In addition to myself, this includes:
- Eilis Boudreau, MD, PhD
- Michael Chiang, MD, MA
- Michael Lieberman, MD, MS
- Vishnu Mohan, MD, MBI
- Thomas Yackel, MD, MS
The work of building the specialty will now continue. We are doing our part at OHSU by continuing to develop our fellowship program we hope will be accredited by the ACGME when its rules are finalized next year. OHSU will likely offer continuing medical education (CME). And of course, OHSU will continue to be a leader in educating the rest of the informatics field as well in our graduate educational programs.
Thursday, November 21, 2013
OHSU-Epic Partnership to Advance Informatics Research and Education
This week, our Department of Medical Informatics & Clinical Epidemiology (DMICE) at Oregon Health & Science University (OHSU) announced a new partnership with Epic Systems Corp., developer of the market-leading Epic electronic health record (EHR). The partnership will make the Epic EHR and associated tools available for research and educational purposes to OHSU informatics students and faculty. OHSU is the first academic informatics program to partner with Epic in this manner, in a process that Epic hopes can be exported to its other customers who also have academic informatics programs.
The partnership will entail setting up two laboratory environments at OHSU, one focused on research and the other on education. The research environment will provide access to the source-code level and enable investigation of areas such as usability, data analytics, simulation, interoperability, patient safety, and others. This platform will allow OHSU faculty and students to conduct research with the Epic EHR environment.
The educational environment will provide students in OHSU's biomedical informatics graduate program access to the Epic EHR and associated tools for learning purposes. Students in both OHSU's on-campus and distance-learning programs will be able to pursue coursework based on Epic's state-of-the-art EHR system. Educational activities will include learning to configure screens, implement clinical decision support, generate reports, and perform other front-end and back-end activities.
Every student of informatics who aspires to work professionally in the field should have experience with a state-of-the-art electronic health record system, including back-end functionality. This partnership provides the opportunity for our students, including those in our distance learning program, to obtain such experience. This will not only augment their learning but also make them more competitive for jobs when they graduate.
This project also represents one of the first milestones of OHSU's new Informatics Discovery Lab (IDL), which was described in this blog previously and is led by Aaron Cohen, MD, MS.
Once the laboratory is established, OHSU will provide support to other of Epic's academic customers in establishing a similar laboratory environment for their programs.
The partnership will entail setting up two laboratory environments at OHSU, one focused on research and the other on education. The research environment will provide access to the source-code level and enable investigation of areas such as usability, data analytics, simulation, interoperability, patient safety, and others. This platform will allow OHSU faculty and students to conduct research with the Epic EHR environment.
The educational environment will provide students in OHSU's biomedical informatics graduate program access to the Epic EHR and associated tools for learning purposes. Students in both OHSU's on-campus and distance-learning programs will be able to pursue coursework based on Epic's state-of-the-art EHR system. Educational activities will include learning to configure screens, implement clinical decision support, generate reports, and perform other front-end and back-end activities.
Every student of informatics who aspires to work professionally in the field should have experience with a state-of-the-art electronic health record system, including back-end functionality. This partnership provides the opportunity for our students, including those in our distance learning program, to obtain such experience. This will not only augment their learning but also make them more competitive for jobs when they graduate.
This project also represents one of the first milestones of OHSU's new Informatics Discovery Lab (IDL), which was described in this blog previously and is led by Aaron Cohen, MD, MS.
Once the laboratory is established, OHSU will provide support to other of Epic's academic customers in establishing a similar laboratory environment for their programs.
Monday, November 11, 2013
Continued Concerns for Building the Capacity of the Clinical Informatics Subspecialty
About a year ago, I wrote a posting expressing concerns about how we will build capacity for the clinical informatics subspecialty. A year later, the subspecialty has moved forward, with the first offering of the certification exam this past October as well as release of draft rules for fellowships accredited by the Accreditation Council for Graduate Medical Education (ACGME).
I still, however, have many of the concerns raised last year, especially now that some of the challenges for standing up ACGME-accredited fellowships have come into sharper focus with the release of the draft rules, and some of us actually trying to figure out how we will develop these fellowships within our own institutions. While the current "grandfathering" period is likely to result in a good starting number of certified individuals, the subspecialty will only succeed in the long run if there are enough training programs to meet the workforce needs of the subspecialty going forward. If only a small number of training programs develop, or they are very difficult for individuals to enter for reasons having nothing to do with their qualifications, then the subspecialty may not be sustianable in the long run.
I have had many of these concerns since the very idea of a subspecialty was first developed by the American Medical Informatics Association (AMIA) around 2006. While I still greatly support the notion of professional recognition of those who work in informatics, I nonetheless have concerns about trying to do so using the mechanism of the medical board subspecialty. In this posting, I will update my thinking on these concerns and also present an alternative for addressing them, even though I am well aware that it may be incompatible with the rules, or at least the traditions, of ACGME.
Background
For those not familiar with the conventional approach to medical subspecialty training, it is important to remember that clinical fellowships differ somewhat from the graduate education model under which most informatics education programs have historically operated. In graduate education, progress is made in units of courses. While most courses are based on specific subjects and/or competencies, there can also be courses for internship/practicum work, theses/dissertations, and integrative learning experiences. With the world's growing number of adult learners in all fields, graduate education can be pursued on a part-time basis, at the appropriate pace for the learner.
Medical training (including clinical fellowships), however, has historically progressed in time-based units, typically in units of years. As such, internal medicine or family medicine residencies are three years long, with other specialties having similar or longer training periods. Those who pursue subspecialty training do so in fellowships lasting one to several years. Sometimes a training program will be extended for research or other work, but almost always in one-year increments.
It is therefore no surprise that the draft ACGME rules propose a time-based approach to training. According to the draft rules, the clinical informatics fellowship will be two years long. While ACGME is currently reviewing public comments, including ours, it is unlikely they will change from that two-year length.
Challenges
Whereas my concerns about clinical informatics fellowships last year were conceptual, we now have real (draft) rules to figure out how to develop fellowships. My concerns are not lessened, and I see potential problems. These include the funding model of fellowships, the requirement to align in a given institution with a single medical specialty, and the arbitrary two-year time frame. I do believe, however, that there are means to accomplish the same ends by a different approach, which I will explain after elaborating more on my concerns.
The funding problem for clinical informatics fellowships will stem from the fact that these fellowships will be different from most other subspecialty fellowships. This derives from the regulations of the Centers for Medicare and Medical Services (CMS) that when one is a clinical fellow, he or she is viewed as a trainee and not allowed to perform any clinical practice without supervision, even if fully board-certificated in the area of practice.
In a typical clinical fellowship, this mostly makes sense, as the clinical work that a fellow is doing is related to the subspecialty in which he or she is training. A clinical informatics fellow will be functioning somewhat differently, as his or her fellowship work will be done in informatics, whereas he or she will be expected to maintain their clinical skills in the practice of the specialty in which he or she was originally board-certified. I am not aware of any fellowship that asks a clinical fellow to maintain practice in their original specialty. Cardiology or rheumatology fellows are not asked, for example, to maintain their practice of general internal medicine, which is the specialty in which they trained before entering their subspecialty fellowship.
Continuing this example, a person entering a cardiology or rheumatology fellowship after completing an internal medicine residency will perform his or her clinical work in their fellowship in cardiology or rheumatology. A clinical informatics fellow training after an internal medicine residency, on the other hand, will be doing his or her "clinical" work in informatics and practicing general internal medicine, mostly to maintain his or her clinical skills. A challenge is that this practice will need to be supervised as if the fellow were a trainee, even if he or she is already board-certified. This will incur a cost to institutions in that attending physicians are limited in the number of residents and fellows they can supervise at any one time. By requiring the clinical informatics fellow to have supervision, it is one less other resident or fellow that an attending physician can supervise under CMS rules.
This underscores the bigger challenge of figuring out how to fund the fellowship. Unlike a graduate education program, where someone (the student, a training grant, a scholarship, etc.) pays the tuition, which in turn supports the program, a clinical fellow earns a salary, which is funded by either CMS graduate medical education (GME) money or the institution itself. Most clinical departments in academic medical centers can fund such fellowships out of other revenues, including the practice revenues of the attending physicians. The challenge for clinical informatics fellows will be how to fund the fellows, not only their stipends, but also the other costs, such as faculty supervision time, tuition for the courses offered, scholarship expenses (e.g., travel to meetings), and administrative costs.
One option suggested by some is to allow the fellows to "moonlight," a tried and true way for medical trainees to augment their income. A problem with moonlighting for clinical experience and revenue is that it will disconnect the clinical informatics fellow clinically from the setting where he or she is doing their informatics training, thus making them less integrated with the environment whose informatics systems they are learning to improve.
A second major problem is the ACGME requirement for clinical informatics fellowships to be aligned with one of six (perhaps increasing to nine) residency programs. This is mainly being done for efficiency reasons, so that new residency review committees (RRCs) that review residencies and fellowships for accreditation will not need to be created. It is stressed that programs can accept anyone from any specialty, but in reality programs will likely resemble the specialties to whose programs they are administratively aligned, if for no other reason due to the normal give and take of academic medical center negotiations and politics.
A third major problem is the two-year time frame. While I do believe that most clinical informatics subspecialists are likely to require two years to complete their training, the arbitrary two-year time frame is at odds with the growing change in medical education from time-based tp competency-based education. Learning informatics is no more like "steeping tea" than medical education [1].
One of the outcomes from these problems is that it may be too difficult for there to be an adequate number of fellowship programs for qualified individuals from all medical specialties to make the subspecialty truly viable. If each fellowship has a one-off situation (i.e., only able or willing to take trainees from one or a small number of specialties) or must be funded, to use the words of my institution's chief medical officer, "creatively," this may undermine the long-term viability of the subspecialty.
Alternative Approach
I cannot criticize the proposed approach without offering an alternative, and I believe there are approaches that could be rigorous enough to ensure an equally if not more robust educational and training experience than the proposed fellowship model. It would no doubt test the boundaries of a tradition-bound organization like ACGME but could also show innovation reflective (and indeed required) of modern education.
We must remember that there will be three basic activities of clinical informatics subspecialty trainees:
Second, how would trainees get their practical hands-on project work? Again, many informatics programs, certainly ours, have developed mechanisms by which students can do internships or practicums in remote location through a combination of affiliation agreements, local mentoring, and remote supervision. While our program currently has students performing 3-6 months at a time of these, I see no reason why the practical experience could not be expanded to a year or longer. Strict guidelines for experience and both local and remote mentoring could be put in place to insure quality.
Finally, what about clinical practice? This may be easiest of all. Requiring a trainee to perform a certain volume of clinical practice, while adhering to all appropriate requirements for licensure and maintenance of certification, should be more than adequate to insure practice in their primary specialty. Many informatics distance learning students are already maintaining their clinical practices to maintain their livelihood. Making clinical practice explicit, instead of as something requiring supervision, will also allow training to be more financially viable for the fellow. Any costs of tuition and practical work could easily be offset by clinical practice revenue.
There would obviously need to be some sort of national infrastructure to set standards and monitor progress of clinical informatics trainees. There are any number of organizations that could perform this task, such as AMIA, and it could perhaps be a requirement of accreditation.
In fact, ACGME and the larger medical education community may learn from alternative approaches like this for training in other specialties. One major national concern these days is that number of residency positions for medical school graduates is not keeping up with the increases of medical school enrollment or, for that matter, the national need for physicians [3]. It is possible that alternative approaches like this could expand the capacity of all medical specialties and subspecialties, and not just clinical informatics.
References
1. Hodges BD, A tea-steeping or i-Doc model for medical education? Academic Medicine, 2010. 85: S34-S44.
2. Hersh WR, The full spectrum of biomedical informatics education at Oregon Health & Science University. Methods of Information in Medicine, 2007. 46: 80-83.
3. Iglehart JK, The residency mismatch. New England Journal of Medicine, 2013. 369: 297-299.
I still, however, have many of the concerns raised last year, especially now that some of the challenges for standing up ACGME-accredited fellowships have come into sharper focus with the release of the draft rules, and some of us actually trying to figure out how we will develop these fellowships within our own institutions. While the current "grandfathering" period is likely to result in a good starting number of certified individuals, the subspecialty will only succeed in the long run if there are enough training programs to meet the workforce needs of the subspecialty going forward. If only a small number of training programs develop, or they are very difficult for individuals to enter for reasons having nothing to do with their qualifications, then the subspecialty may not be sustianable in the long run.
I have had many of these concerns since the very idea of a subspecialty was first developed by the American Medical Informatics Association (AMIA) around 2006. While I still greatly support the notion of professional recognition of those who work in informatics, I nonetheless have concerns about trying to do so using the mechanism of the medical board subspecialty. In this posting, I will update my thinking on these concerns and also present an alternative for addressing them, even though I am well aware that it may be incompatible with the rules, or at least the traditions, of ACGME.
Background
For those not familiar with the conventional approach to medical subspecialty training, it is important to remember that clinical fellowships differ somewhat from the graduate education model under which most informatics education programs have historically operated. In graduate education, progress is made in units of courses. While most courses are based on specific subjects and/or competencies, there can also be courses for internship/practicum work, theses/dissertations, and integrative learning experiences. With the world's growing number of adult learners in all fields, graduate education can be pursued on a part-time basis, at the appropriate pace for the learner.
Medical training (including clinical fellowships), however, has historically progressed in time-based units, typically in units of years. As such, internal medicine or family medicine residencies are three years long, with other specialties having similar or longer training periods. Those who pursue subspecialty training do so in fellowships lasting one to several years. Sometimes a training program will be extended for research or other work, but almost always in one-year increments.
It is therefore no surprise that the draft ACGME rules propose a time-based approach to training. According to the draft rules, the clinical informatics fellowship will be two years long. While ACGME is currently reviewing public comments, including ours, it is unlikely they will change from that two-year length.
Challenges
Whereas my concerns about clinical informatics fellowships last year were conceptual, we now have real (draft) rules to figure out how to develop fellowships. My concerns are not lessened, and I see potential problems. These include the funding model of fellowships, the requirement to align in a given institution with a single medical specialty, and the arbitrary two-year time frame. I do believe, however, that there are means to accomplish the same ends by a different approach, which I will explain after elaborating more on my concerns.
The funding problem for clinical informatics fellowships will stem from the fact that these fellowships will be different from most other subspecialty fellowships. This derives from the regulations of the Centers for Medicare and Medical Services (CMS) that when one is a clinical fellow, he or she is viewed as a trainee and not allowed to perform any clinical practice without supervision, even if fully board-certificated in the area of practice.
In a typical clinical fellowship, this mostly makes sense, as the clinical work that a fellow is doing is related to the subspecialty in which he or she is training. A clinical informatics fellow will be functioning somewhat differently, as his or her fellowship work will be done in informatics, whereas he or she will be expected to maintain their clinical skills in the practice of the specialty in which he or she was originally board-certified. I am not aware of any fellowship that asks a clinical fellow to maintain practice in their original specialty. Cardiology or rheumatology fellows are not asked, for example, to maintain their practice of general internal medicine, which is the specialty in which they trained before entering their subspecialty fellowship.
Continuing this example, a person entering a cardiology or rheumatology fellowship after completing an internal medicine residency will perform his or her clinical work in their fellowship in cardiology or rheumatology. A clinical informatics fellow training after an internal medicine residency, on the other hand, will be doing his or her "clinical" work in informatics and practicing general internal medicine, mostly to maintain his or her clinical skills. A challenge is that this practice will need to be supervised as if the fellow were a trainee, even if he or she is already board-certified. This will incur a cost to institutions in that attending physicians are limited in the number of residents and fellows they can supervise at any one time. By requiring the clinical informatics fellow to have supervision, it is one less other resident or fellow that an attending physician can supervise under CMS rules.
This underscores the bigger challenge of figuring out how to fund the fellowship. Unlike a graduate education program, where someone (the student, a training grant, a scholarship, etc.) pays the tuition, which in turn supports the program, a clinical fellow earns a salary, which is funded by either CMS graduate medical education (GME) money or the institution itself. Most clinical departments in academic medical centers can fund such fellowships out of other revenues, including the practice revenues of the attending physicians. The challenge for clinical informatics fellows will be how to fund the fellows, not only their stipends, but also the other costs, such as faculty supervision time, tuition for the courses offered, scholarship expenses (e.g., travel to meetings), and administrative costs.
One option suggested by some is to allow the fellows to "moonlight," a tried and true way for medical trainees to augment their income. A problem with moonlighting for clinical experience and revenue is that it will disconnect the clinical informatics fellow clinically from the setting where he or she is doing their informatics training, thus making them less integrated with the environment whose informatics systems they are learning to improve.
A second major problem is the ACGME requirement for clinical informatics fellowships to be aligned with one of six (perhaps increasing to nine) residency programs. This is mainly being done for efficiency reasons, so that new residency review committees (RRCs) that review residencies and fellowships for accreditation will not need to be created. It is stressed that programs can accept anyone from any specialty, but in reality programs will likely resemble the specialties to whose programs they are administratively aligned, if for no other reason due to the normal give and take of academic medical center negotiations and politics.
A third major problem is the two-year time frame. While I do believe that most clinical informatics subspecialists are likely to require two years to complete their training, the arbitrary two-year time frame is at odds with the growing change in medical education from time-based tp competency-based education. Learning informatics is no more like "steeping tea" than medical education [1].
One of the outcomes from these problems is that it may be too difficult for there to be an adequate number of fellowship programs for qualified individuals from all medical specialties to make the subspecialty truly viable. If each fellowship has a one-off situation (i.e., only able or willing to take trainees from one or a small number of specialties) or must be funded, to use the words of my institution's chief medical officer, "creatively," this may undermine the long-term viability of the subspecialty.
Alternative Approach
I cannot criticize the proposed approach without offering an alternative, and I believe there are approaches that could be rigorous enough to ensure an equally if not more robust educational and training experience than the proposed fellowship model. It would no doubt test the boundaries of a tradition-bound organization like ACGME but could also show innovation reflective (and indeed required) of modern education.
We must remember that there will be three basic activities of clinical informatics subspecialty trainees:
- Clinical informatics education to master the core knowledge of the field
- Clinical informatics project work to gain skills and practical experience
- Clinical practice to maintain their skills in their primary medical specialty
Second, how would trainees get their practical hands-on project work? Again, many informatics programs, certainly ours, have developed mechanisms by which students can do internships or practicums in remote location through a combination of affiliation agreements, local mentoring, and remote supervision. While our program currently has students performing 3-6 months at a time of these, I see no reason why the practical experience could not be expanded to a year or longer. Strict guidelines for experience and both local and remote mentoring could be put in place to insure quality.
Finally, what about clinical practice? This may be easiest of all. Requiring a trainee to perform a certain volume of clinical practice, while adhering to all appropriate requirements for licensure and maintenance of certification, should be more than adequate to insure practice in their primary specialty. Many informatics distance learning students are already maintaining their clinical practices to maintain their livelihood. Making clinical practice explicit, instead of as something requiring supervision, will also allow training to be more financially viable for the fellow. Any costs of tuition and practical work could easily be offset by clinical practice revenue.
There would obviously need to be some sort of national infrastructure to set standards and monitor progress of clinical informatics trainees. There are any number of organizations that could perform this task, such as AMIA, and it could perhaps be a requirement of accreditation.
In fact, ACGME and the larger medical education community may learn from alternative approaches like this for training in other specialties. One major national concern these days is that number of residency positions for medical school graduates is not keeping up with the increases of medical school enrollment or, for that matter, the national need for physicians [3]. It is possible that alternative approaches like this could expand the capacity of all medical specialties and subspecialties, and not just clinical informatics.
References
1. Hodges BD, A tea-steeping or i-Doc model for medical education? Academic Medicine, 2010. 85: S34-S44.
2. Hersh WR, The full spectrum of biomedical informatics education at Oregon Health & Science University. Methods of Information in Medicine, 2007. 46: 80-83.
3. Iglehart JK, The residency mismatch. New England Journal of Medicine, 2013. 369: 297-299.
Wednesday, November 6, 2013
Evolution of Medical Education to Competency-Based Approaches: Can or Should Informatics Education Adopt Them?
There are major changes taking place in the education of physicians and other healthcare professionals. I have had the opportunity to be involved up close as OHSU undertakes a major transformation of its medical school curriculum.
As I work with our medical education leaders, and become familiar with the latest methods, jargon, and research, I ask myself whether informatics can or should adopt these approaches in our educational programs. In this posting I will review the methods and techniques being used in medical education, and in a subsequent posting will explore how these may be adapted by informatics education.
One of the major thrusts in medical education has been the move to competency-based medical education (CBME). A great deal of new jargon has emerged, the understanding of which is critical to read its literature [1].
Probably the biggest change being advocated is a transition from time-based to competency-based education [2]. This is based on the notion that our traditional time-based approach of students being "steeped" (like tea) in an educational program for a fixed period of time will result in their somehow emerging as competent practitioners. More recent thinking is that students acquire competencies at different rates from their fellow students, and there is no reason why all students should be forced into the same time period to attain them. This also has practical relevance in the need to increase the number of physicians in the US as the baby boomer population ages and more patients are brought into the healthcare system through healthcare reform.
Some aspects of these concepts are not new. For example, in the 1960s Bloom advanced the concept of "mastery learning" [3]. The idea behind mastery learning is that the variable changes from the traditional fixed time and measuring learning on a test to insuring that learning is complete and the variable being time, namely different time to master a subject for different students.
Another development in medical education in recent years has been the acceptance of a common taxonomy of competency domains [4]. This started with the six competency domains developed by the ACGME and ABMS, with 36 competencies delineated within them. More recently, the AAMC has added two additional domains and both added and refined the competencies to increase the total to 58.
An additional important development is the Next Accreditation System for graduate (post-medical school) medical education [5]. Central to this effort is the development of milestones (developmentally based, specialty-specific) that are achieved at five levels throughout medical school, residency, and advanced training or clinical practice. Seven specialties are launching milestones in 2013, with the remainder following in 2014 [6]. Training programs will be assessed based on the achievement of milestones by their trainees.
Another related concept to emerge is that of the entrustable professional activity (EPA), which is the notion that there are tasks or responsibilities that can be entrusted to a trainee once competence is achieved so that he or she can execute them without supervision [7]. EPA activities have been proposed for family medicine physicians [8], with others likely to follow.
These changes in medical education are also augmented by changes brought about by new technology. Two have been noted in particular: massive open online courses (MOOCs), which allow "flipped classrooms" and digital badges, both of which can create electronic means to validate milestones, EPAs, and other achievements [9]. Many other roles have been discerned as well [10-11].
Technology also foments other change in medical education. For example, the wedding of individuals to smartphones and tablets may have unintended consequences [12]. In addition, just as EHRs profoundly impact the workflow of physicians, they likewise impact the workflow of students [13]. Additional instruction may be required in reading and writing to the EHR [14] as well as its proper use in the patient-physician encounter [15-16]. Technology also impacts the role of the "master diagnostician," a teacher who also plays an outsized role in many academic medical centers [17].
The approach to educating physicians is clearly changing. The bulk of medical education leaders advocate an approach that changes the focus from one that is time-based to a more competency-based design. As such, there is no reason why medical education should last four years for everyone, and many aspects can be more individualized based on prior knowledge before medical school, learning systems, and other attributes of students.
It turns out that I have been using some of the above innovations in my own teaching, although I did not realize there were names for what I was doing. For example, in my introductory informatics course, taught in various venues as OHSU BMI 510 or AMIA 10x10, I sometimes teach the course "on campus." When I do this, it does not make sense to stand up and give lectures in the traditional sense. Instead, I use the "flipped classroom" approach of having students view the lectures before coming to class and then spending class time reviewing the material, answering questions, and discussing issues more deeply. (In online offerings of the class, these activities are carried out in discussion forums on our learning management system.)
In addition, for at least one class I teach, I use a variant of mastery learning. Another course I teach in our program is an elective course, BMI 536 - Evidence-Based Medicine. I believe that the best way to teach this topic is for students to repeatedly carry out the techniques of asking appropriate clinical questions and critically appraising the evidence until they get them right, i.e., have mastered them. They must do this before receiving a passing grade in the course.
In a future posting, I will explore whether this larger competency-based approach is appropriate to the education of informaticians, and if so, how we might implement it. I will also look at this from the standpoint of fellowships for the new physician subspecialty of clinical informatics.
References
1. Carraccio CL and Englander R, From Flexner to competencies: reflections on a decade and the journey ahead. Academic Medicine, 2013. 88: 1067-1073.
2. Hodges BD, A tea-steeping or i-Doc model for medical education? Academic Medicine, 2010. 85: S34-S44.
3. Kulik CC and Kulik JA, Effectivenss of mastery learning programs: a meta-analysis. Review of Educational Research, 1990. 60: 265-299.
4. Englander R, Cameron T, Ballard AJ, Dodge J, Bull J, and Aschenbrener CA, Toward a common taxonomy of competency domains for the health professions and competencies for physicians. Academic Medicine, 2013. 88: 1088-1094.
5. Nasca TJ, Philibert I, Brigham T, and Flynn TC, The next GME accreditation system--rationale and benefits. New England Journal of Medicine, 2012. 366: 1051-1056.
6. Swing SR, Beeson MS, Carraccio C, Coburn M, Iobst W, Selden NR, et al., Educational milestone development in the first 7 specialties to enter the next accreditation system. Journal of Graduate Medical Education, 2013. 5: 98-106.
7. Ten Cate O, Nuts and bolts of enstrustable professional activities. Journal of Graduate Medical Education, 2013. 5: 157-158.
8. Shaughnessy AF, Sparks J, Cohen-Osher M, Goodell KH, Sawin GL, and Gravel J, Entrustable professional activities in family medicine. Journal of Graduate Medical Education, 2013. 5: 112-118.
9. Mehta NB, Hull AL, Young JB, and Stoller JK, Just imagine: new paradigms for medical education. Academic Medicine, 2013. 88: 1418-1423.
10. Triola MM, Friedman E, Cimino C, Geyer EM, Wiederhorn J, and Mainiero C, Health information technology and the medical school curriculum. American Journal of Managed Care, 2010. 16(12 Suppl HIT): SP54-SP56.
11. Anonymous, Health Professions Education: Accelerating Innovation Through Technology. 2013, The Blue Ridge Academic Health Group: Atlanta GA, http://whsc.emory.edu/blueridge/publications/archive/blue-ridge-2013.pdf.
12. Wu R, Rise of the cyborgs: residents with smartphones, iPads, and Androids. Journal of Graduate Medical Education, 2013. 5: 161-162.
13. Ellaway RH, Graves L, and Greene PS, Medical education in an electronic health record-mediated world. Medical Teacher, 2013. 35: 282-286.
14. Han H and Lopp L, Writing and reading EHR documentation: an entirely new world. Medical Education Online, 2013. 18: 18634. http://med-ed-online.net/index.php/meo/article/view/18634.
15. Pearce C, Dwan K, Arnold M, Phillips C, and Trumble S, Doctor, patient and computer--a framework for the new consultation. International Journal of Medical Informatics, 2009. 78: 32-38.
16. Pearce C, Arnold M, Phillips C, Trumble S, and Dwan K, The patient and the computer in the primary care consultation. Journal of the American Medical Informatics Association, 2011. 18: 138-142.
17. Dhaliwal G and Detsky AS, The evolution of the master diagnostician. Journal of the American Medical Association, 2013. 310: 579-580.
As I work with our medical education leaders, and become familiar with the latest methods, jargon, and research, I ask myself whether informatics can or should adopt these approaches in our educational programs. In this posting I will review the methods and techniques being used in medical education, and in a subsequent posting will explore how these may be adapted by informatics education.
One of the major thrusts in medical education has been the move to competency-based medical education (CBME). A great deal of new jargon has emerged, the understanding of which is critical to read its literature [1].
Probably the biggest change being advocated is a transition from time-based to competency-based education [2]. This is based on the notion that our traditional time-based approach of students being "steeped" (like tea) in an educational program for a fixed period of time will result in their somehow emerging as competent practitioners. More recent thinking is that students acquire competencies at different rates from their fellow students, and there is no reason why all students should be forced into the same time period to attain them. This also has practical relevance in the need to increase the number of physicians in the US as the baby boomer population ages and more patients are brought into the healthcare system through healthcare reform.
Some aspects of these concepts are not new. For example, in the 1960s Bloom advanced the concept of "mastery learning" [3]. The idea behind mastery learning is that the variable changes from the traditional fixed time and measuring learning on a test to insuring that learning is complete and the variable being time, namely different time to master a subject for different students.
Another development in medical education in recent years has been the acceptance of a common taxonomy of competency domains [4]. This started with the six competency domains developed by the ACGME and ABMS, with 36 competencies delineated within them. More recently, the AAMC has added two additional domains and both added and refined the competencies to increase the total to 58.
An additional important development is the Next Accreditation System for graduate (post-medical school) medical education [5]. Central to this effort is the development of milestones (developmentally based, specialty-specific) that are achieved at five levels throughout medical school, residency, and advanced training or clinical practice. Seven specialties are launching milestones in 2013, with the remainder following in 2014 [6]. Training programs will be assessed based on the achievement of milestones by their trainees.
Another related concept to emerge is that of the entrustable professional activity (EPA), which is the notion that there are tasks or responsibilities that can be entrusted to a trainee once competence is achieved so that he or she can execute them without supervision [7]. EPA activities have been proposed for family medicine physicians [8], with others likely to follow.
These changes in medical education are also augmented by changes brought about by new technology. Two have been noted in particular: massive open online courses (MOOCs), which allow "flipped classrooms" and digital badges, both of which can create electronic means to validate milestones, EPAs, and other achievements [9]. Many other roles have been discerned as well [10-11].
Technology also foments other change in medical education. For example, the wedding of individuals to smartphones and tablets may have unintended consequences [12]. In addition, just as EHRs profoundly impact the workflow of physicians, they likewise impact the workflow of students [13]. Additional instruction may be required in reading and writing to the EHR [14] as well as its proper use in the patient-physician encounter [15-16]. Technology also impacts the role of the "master diagnostician," a teacher who also plays an outsized role in many academic medical centers [17].
The approach to educating physicians is clearly changing. The bulk of medical education leaders advocate an approach that changes the focus from one that is time-based to a more competency-based design. As such, there is no reason why medical education should last four years for everyone, and many aspects can be more individualized based on prior knowledge before medical school, learning systems, and other attributes of students.
It turns out that I have been using some of the above innovations in my own teaching, although I did not realize there were names for what I was doing. For example, in my introductory informatics course, taught in various venues as OHSU BMI 510 or AMIA 10x10, I sometimes teach the course "on campus." When I do this, it does not make sense to stand up and give lectures in the traditional sense. Instead, I use the "flipped classroom" approach of having students view the lectures before coming to class and then spending class time reviewing the material, answering questions, and discussing issues more deeply. (In online offerings of the class, these activities are carried out in discussion forums on our learning management system.)
In addition, for at least one class I teach, I use a variant of mastery learning. Another course I teach in our program is an elective course, BMI 536 - Evidence-Based Medicine. I believe that the best way to teach this topic is for students to repeatedly carry out the techniques of asking appropriate clinical questions and critically appraising the evidence until they get them right, i.e., have mastered them. They must do this before receiving a passing grade in the course.
In a future posting, I will explore whether this larger competency-based approach is appropriate to the education of informaticians, and if so, how we might implement it. I will also look at this from the standpoint of fellowships for the new physician subspecialty of clinical informatics.
References
1. Carraccio CL and Englander R, From Flexner to competencies: reflections on a decade and the journey ahead. Academic Medicine, 2013. 88: 1067-1073.
2. Hodges BD, A tea-steeping or i-Doc model for medical education? Academic Medicine, 2010. 85: S34-S44.
3. Kulik CC and Kulik JA, Effectivenss of mastery learning programs: a meta-analysis. Review of Educational Research, 1990. 60: 265-299.
4. Englander R, Cameron T, Ballard AJ, Dodge J, Bull J, and Aschenbrener CA, Toward a common taxonomy of competency domains for the health professions and competencies for physicians. Academic Medicine, 2013. 88: 1088-1094.
5. Nasca TJ, Philibert I, Brigham T, and Flynn TC, The next GME accreditation system--rationale and benefits. New England Journal of Medicine, 2012. 366: 1051-1056.
6. Swing SR, Beeson MS, Carraccio C, Coburn M, Iobst W, Selden NR, et al., Educational milestone development in the first 7 specialties to enter the next accreditation system. Journal of Graduate Medical Education, 2013. 5: 98-106.
7. Ten Cate O, Nuts and bolts of enstrustable professional activities. Journal of Graduate Medical Education, 2013. 5: 157-158.
8. Shaughnessy AF, Sparks J, Cohen-Osher M, Goodell KH, Sawin GL, and Gravel J, Entrustable professional activities in family medicine. Journal of Graduate Medical Education, 2013. 5: 112-118.
9. Mehta NB, Hull AL, Young JB, and Stoller JK, Just imagine: new paradigms for medical education. Academic Medicine, 2013. 88: 1418-1423.
10. Triola MM, Friedman E, Cimino C, Geyer EM, Wiederhorn J, and Mainiero C, Health information technology and the medical school curriculum. American Journal of Managed Care, 2010. 16(12 Suppl HIT): SP54-SP56.
11. Anonymous, Health Professions Education: Accelerating Innovation Through Technology. 2013, The Blue Ridge Academic Health Group: Atlanta GA, http://whsc.emory.edu/blueridge/publications/archive/blue-ridge-2013.pdf.
12. Wu R, Rise of the cyborgs: residents with smartphones, iPads, and Androids. Journal of Graduate Medical Education, 2013. 5: 161-162.
13. Ellaway RH, Graves L, and Greene PS, Medical education in an electronic health record-mediated world. Medical Teacher, 2013. 35: 282-286.
14. Han H and Lopp L, Writing and reading EHR documentation: an entirely new world. Medical Education Online, 2013. 18: 18634. http://med-ed-online.net/index.php/meo/article/view/18634.
15. Pearce C, Dwan K, Arnold M, Phillips C, and Trumble S, Doctor, patient and computer--a framework for the new consultation. International Journal of Medical Informatics, 2009. 78: 32-38.
16. Pearce C, Arnold M, Phillips C, Trumble S, and Dwan K, The patient and the computer in the primary care consultation. Journal of the American Medical Informatics Association, 2011. 18: 138-142.
17. Dhaliwal G and Detsky AS, The evolution of the master diagnostician. Journal of the American Medical Association, 2013. 310: 579-580.
Monday, November 4, 2013
Will MOOCs Cause Disruptive Innovation in Higher Education or Just Be a Valuable Resource?
As one who has been involved in online learning for nearly a decade and a half, I have been following the development of massive open online courses (MOOCs) with great interest. As someone who struggles daily with managing an educational program that is mandated by my university to be financially self-sufficient, I take great interest in financial models for educational programs and the new challenges these disruptive technologies may bring.
MOOCs have been around for a couple years now, and they have even started to permeate into the biomedical and health informatics world. I am aware of at least two informatics-related MOOCs (one from Georgia Institute of Technology and the other from University of Minnesota) as well as another that focuses on healthcare analytics. In addition, a colleague from Australia has been fashioning the ONC health IT curriculum into one giant MOOC, although it is not a course in the sense of one registering for it and having criteria for successfully completing it.
We are also starting to see some research findings about MOOCs. A first analysis was recently published about a MOOC from Massachusetts Institute of Technology (MIT) [1]. The course studied was 6.002x, an introductory course in circuits and electronics offered through edX, one of the two large US-based consortia offering MOOCs. Typical of MOOCs, about 155,000 people registered for the course and about 7000 completed it. This is still an impressive number completing the course, and the research noted factors that kept students engaged and successful. One of the most prominent factors was interaction with fellow students in the non-required online discussion forums. The researchers also determined that students spent the most time in online lectures when learning materials but most often referred to the online textbook when completing exams and browsed the discussion forums for help in completing homework assignments.
Other research in the same issue of the journal where this paper was published looked at handling some of the challenges of large-scale online education, such as grading of essay materials [2] and preventing cheating in these types of courses [3]. One approach to essay grading, highly controversial, is automated grading that uses machine learning approaches, while another approach uses "calibrated peer review" among participating students.
At this point in time, MOOCs have not yet led to true disruptive innovation in an industry (higher education) that has maintained resistance to such innovation, although the originator of the concept of disruptive innovation believes this will happen soon [4]. Perhaps the development that comes closest to disrupting higher education is the launching of a $6600 master's degree in computer science by Georgia Institute of Technology [5]. This program does not replace the institution's residential $40,000 master's program. But if successful, it will demonstrate a possible pathway to high-quality higher education that is significantly less expensive than conventionally delivered education.
In our OHSU graduate program in biomedical informatics, we have found that distance learning's attributes are more about the flexibility and the reach of our educational program across the planet than lowered cost. We consider it important for us to still provide the value of a comprehensive higher education program, which includes:
There is no question that online education delivery will continue to grow, and soon start to permeate highly resistant fields, such as medicine [6]. As with many technology-related endeavors, I believe that the most likely models to emerge will be hybrid models, i.e., those that make use of resources like MOOCs but still offer comprehensive educational experiences. I can easily see institutions of higher education licensing or otherwise using MOOCs in their educational offerings, with the institution filling in the additional value required for a complete education.
These benefits of MOOCs will have the potential to lower costs and introduce efficiencies, but probably not to the extent of widespread sub-$10,000 master's degrees. There will be other value, however, such as the reach and flexibility of online learning. Perhaps the best of all worlds will allow higher education to focus on other activities it can perform well, such as personal and career development as well as exposure to "real world" work environments.
References
1. Breslow L, Pritchard DE, DeBoer J, Stump GS, Ho AD, and Seaton DT, Studying learning in the worldwide classroom: research into edX’s first MOOC. Research and Practice in Assessment, 2013. 8. http://www.rpajournal.com/studying-learning-in-the-worldwide-classroom-research-into-edxs-first-mooc/.
2. Balfour SP, Assessing writing in MOOCs: automated essay scoring and calibrated peer review. Research and Practice in Assessment, 2013. 8. http://www.rpajournal.com/assessing-writing-in-moocs-automated-essay-scoring-and-calibrated-peer-review/.
3. Meyer JP and Zhu S, Fair and equitable measurement of student learning in MOOCs: an introduction to item response theory, scale linking, and score equating. Research and Practice in Assessment, 2013. 8. http://www.rpajournal.com/fair-and-equitable-measurement-of-student-learning-in-moocs-an-introduction-to-item-response-theory-scale-linking-and-score-equating/.
4. Christensen CM and Horn MB, Innovation Imperative: Change Everything, New York Times. November 1, 2013. http://www.nytimes.com/2013/11/03/education/edlife/online-education-as-an-agent-of-transformation.html.
5. Lewin T, Master’s Degree Is New Frontier of Study Online, New York Times. August 17, 2013. http://www.nytimes.com/2013/08/18/education/masters-degree-is-new-frontier-of-study-online.html.
6. Mehta NB, Hull AL, Young JB, and Stoller JK, Just imagine: new paradigms for medical education. Academic Medicine, 2013. 88: 1418-1423.
MOOCs have been around for a couple years now, and they have even started to permeate into the biomedical and health informatics world. I am aware of at least two informatics-related MOOCs (one from Georgia Institute of Technology and the other from University of Minnesota) as well as another that focuses on healthcare analytics. In addition, a colleague from Australia has been fashioning the ONC health IT curriculum into one giant MOOC, although it is not a course in the sense of one registering for it and having criteria for successfully completing it.
We are also starting to see some research findings about MOOCs. A first analysis was recently published about a MOOC from Massachusetts Institute of Technology (MIT) [1]. The course studied was 6.002x, an introductory course in circuits and electronics offered through edX, one of the two large US-based consortia offering MOOCs. Typical of MOOCs, about 155,000 people registered for the course and about 7000 completed it. This is still an impressive number completing the course, and the research noted factors that kept students engaged and successful. One of the most prominent factors was interaction with fellow students in the non-required online discussion forums. The researchers also determined that students spent the most time in online lectures when learning materials but most often referred to the online textbook when completing exams and browsed the discussion forums for help in completing homework assignments.
Other research in the same issue of the journal where this paper was published looked at handling some of the challenges of large-scale online education, such as grading of essay materials [2] and preventing cheating in these types of courses [3]. One approach to essay grading, highly controversial, is automated grading that uses machine learning approaches, while another approach uses "calibrated peer review" among participating students.
At this point in time, MOOCs have not yet led to true disruptive innovation in an industry (higher education) that has maintained resistance to such innovation, although the originator of the concept of disruptive innovation believes this will happen soon [4]. Perhaps the development that comes closest to disrupting higher education is the launching of a $6600 master's degree in computer science by Georgia Institute of Technology [5]. This program does not replace the institution's residential $40,000 master's program. But if successful, it will demonstrate a possible pathway to high-quality higher education that is significantly less expensive than conventionally delivered education.
In our OHSU graduate program in biomedical informatics, we have found that distance learning's attributes are more about the flexibility and the reach of our educational program across the planet than lowered cost. We consider it important for us to still provide the value of a comprehensive higher education program, which includes:
- An up-to-date curriculum based on a solid foundation
- Faculty who are international leaders in research and practice
- Ability to find and carry out an internship or practicum experience
- Career development and advising
- Connections to industry and others in the field
There is no question that online education delivery will continue to grow, and soon start to permeate highly resistant fields, such as medicine [6]. As with many technology-related endeavors, I believe that the most likely models to emerge will be hybrid models, i.e., those that make use of resources like MOOCs but still offer comprehensive educational experiences. I can easily see institutions of higher education licensing or otherwise using MOOCs in their educational offerings, with the institution filling in the additional value required for a complete education.
These benefits of MOOCs will have the potential to lower costs and introduce efficiencies, but probably not to the extent of widespread sub-$10,000 master's degrees. There will be other value, however, such as the reach and flexibility of online learning. Perhaps the best of all worlds will allow higher education to focus on other activities it can perform well, such as personal and career development as well as exposure to "real world" work environments.
References
1. Breslow L, Pritchard DE, DeBoer J, Stump GS, Ho AD, and Seaton DT, Studying learning in the worldwide classroom: research into edX’s first MOOC. Research and Practice in Assessment, 2013. 8. http://www.rpajournal.com/studying-learning-in-the-worldwide-classroom-research-into-edxs-first-mooc/.
2. Balfour SP, Assessing writing in MOOCs: automated essay scoring and calibrated peer review. Research and Practice in Assessment, 2013. 8. http://www.rpajournal.com/assessing-writing-in-moocs-automated-essay-scoring-and-calibrated-peer-review/.
3. Meyer JP and Zhu S, Fair and equitable measurement of student learning in MOOCs: an introduction to item response theory, scale linking, and score equating. Research and Practice in Assessment, 2013. 8. http://www.rpajournal.com/fair-and-equitable-measurement-of-student-learning-in-moocs-an-introduction-to-item-response-theory-scale-linking-and-score-equating/.
4. Christensen CM and Horn MB, Innovation Imperative: Change Everything, New York Times. November 1, 2013. http://www.nytimes.com/2013/11/03/education/edlife/online-education-as-an-agent-of-transformation.html.
5. Lewin T, Master’s Degree Is New Frontier of Study Online, New York Times. August 17, 2013. http://www.nytimes.com/2013/08/18/education/masters-degree-is-new-frontier-of-study-online.html.
6. Mehta NB, Hull AL, Young JB, and Stoller JK, Just imagine: new paradigms for medical education. Academic Medicine, 2013. 88: 1418-1423.
Wednesday, October 23, 2013
Discerning the Evidence Base for Health Information Exchange (HIE)
Some of the most important projects in my career have come out of nowhere, almost by coincidence, but turning into major commitments as well as important endeavors for the field. I am about to begin a new project that originated in a similarly unexpected manner.
This project came about because our Department of Medical Informatics & Clinical Epidemiology (DMICE) is one of the 12 Evidence-Based Practice Centers (EPCs) funded by the Agency for Healthcare Research and Quality (AHRQ). Being an EPC gives these centers the opportunity to bid to perform "task orders," which mainly consist of "evidence reports" that usually consist of one or more systematic reviews and sometimes additional analysis. DMICE houses the Pacific Northwest EPC, which is a major activity of the "CE" part of DMICE.
Most of the AHRQ evidence reports focus on clinical topics, such as traumatic brain injury or myocardial infarction. AHRQ has commissioned a few reports on informatics-related topics over the years, such as telemedicine and health information technology. (I was principal investigator [PI] of the telemedicine reports it commissioned, which included an initial analysis focused on Medicare patients [1-2], a supplementary analysis on all patients, and an update [3-4].)
A couple months ago, the Director of our EPC informed me that AHRQ had released a "request for task order" (RFTO) seeking bids to prepare an evidence report on health information exchange (HIE). Needless to say, I jumped at the chance to serve as PI and prepare a proposal. I was then delighted to receive word in mid-September that our proposal had been selected for funding. We have a great team that combines informatics and evidence-based medicine expertise.
This 18-month project will become one of my major projects going forward. The timing could actually not be better, with the winding down of my big Office of the National Coordinator for Health IT (ONC) projects.
I am looking forward to undertaking this project. HIE is one of the most important activities of informatics now. The benefit of a standards-based, interoperable health IT ecosystem will not be realized unless, to quote former AHRQ Director Dr. Carolyn Clancy, "data follows the patient" wherever they go in the healthcare system. Or, as started by Dr. William Yasnoff, former Senior Federal Advisor for the National Health Information Infrastructure, there must be "anytime, anywhere access to data for patient care." Or even, as noted more recently by Dr. John Halamka, ACO = HIE + analytics. HIE has a growing accumulation of evidence concerning its effectiveness, obstacles, and sustainability.
We will follow the usual EPC approach in producing the evidence report. Our first activity will be to develop a set of key questions that the report will answer, embedded into a conceptual framework that shows the interrelationships among the questions. This process will be aided by engaging a set of "key informants" who represent various stakeholder groups, such as patients, providers, healthcare organizations, health policymakers, and vendors. They will help us in a process of "topic refinement" to specify the key questions [5].
Once our key questions are finalized, we will then perform a systematic review on each one. Systematic reviews differ from other kinds of topic reviews in that the questions are focused and amenable to being answered with scientific evidence [6]. They not only provide an inventory of existing scientific knowledge on a given topic but also inform us of the gaps in our knowledge.
Systematic reviews always begin with a comprehensive literature search, which casts a wide net for all possible evidence. The retrieved titles and abstracts are read and analyzed to determine which papers might meet inclusion criteria and should be pulled for reading of their full text. Those that meet the inclusion criteria are abstracted for the evidence, which is then is entered into evidence tables. If enough evidence from similar studies is retrieved, then a meta-analysis might be performed, where the data from each study is aggregated as if it were a single study. Meta-analysis summarizes the evidence as well as increases its statistical power.
This report will not focus solely on the evidence for the benefit of HIE. While that will clearly be an important part, we will also look at other aspects of HIE.
The first key question will likely concern the effectiveness of HIE. This will be assessed based on a variety of outcomes of its use, including healthcare process outcomes, intermediate outcomes, economic outcomes, clinical outcomes, and population-level outcomes. Other key questions will likely focus on:
One of the challenges for defining evidence for HIE is that it is used as a means to other ends, usually improved clinical care. Since many other factors may influence care, it may be hard to tease out the true value of HIE. But we will aggregate all the evidence we can find to best discern the scientific picture of its value and limitations.
References
1. Hersh, WR, Helfand, M, et al. (2001). Clinical outcomes resulting from telemedicine interventions: a systematic review. BMC Medical Informatics and Decision Making. 1: 5. http://www.biomedcentral.com/1472-6947/1/5.
2. Hersh, W, Helfand, M, et al. (2002). A systematic review of the efficacy of telemedicine for making diagnostic and management decisions. Journal of Telemedicine and Telecare. 8: 197-209.
3. Hersh, WR, Hickam, DH, et al. (2006). The evidence base of telemedicine: overview of the supplement. Journal of Telemedicine & Telecare. 12(Supp 2): 1-2.
4. Hersh, WR, Hickam, DH, et al. (2006). Diagnosis, access, and outcomes: update of a systematic review on telemedicine services. Journal of Telemedicine & Telecare. 12(Supp 2): 3-31.
This project came about because our Department of Medical Informatics & Clinical Epidemiology (DMICE) is one of the 12 Evidence-Based Practice Centers (EPCs) funded by the Agency for Healthcare Research and Quality (AHRQ). Being an EPC gives these centers the opportunity to bid to perform "task orders," which mainly consist of "evidence reports" that usually consist of one or more systematic reviews and sometimes additional analysis. DMICE houses the Pacific Northwest EPC, which is a major activity of the "CE" part of DMICE.
Most of the AHRQ evidence reports focus on clinical topics, such as traumatic brain injury or myocardial infarction. AHRQ has commissioned a few reports on informatics-related topics over the years, such as telemedicine and health information technology. (I was principal investigator [PI] of the telemedicine reports it commissioned, which included an initial analysis focused on Medicare patients [1-2], a supplementary analysis on all patients, and an update [3-4].)
A couple months ago, the Director of our EPC informed me that AHRQ had released a "request for task order" (RFTO) seeking bids to prepare an evidence report on health information exchange (HIE). Needless to say, I jumped at the chance to serve as PI and prepare a proposal. I was then delighted to receive word in mid-September that our proposal had been selected for funding. We have a great team that combines informatics and evidence-based medicine expertise.
This 18-month project will become one of my major projects going forward. The timing could actually not be better, with the winding down of my big Office of the National Coordinator for Health IT (ONC) projects.
I am looking forward to undertaking this project. HIE is one of the most important activities of informatics now. The benefit of a standards-based, interoperable health IT ecosystem will not be realized unless, to quote former AHRQ Director Dr. Carolyn Clancy, "data follows the patient" wherever they go in the healthcare system. Or, as started by Dr. William Yasnoff, former Senior Federal Advisor for the National Health Information Infrastructure, there must be "anytime, anywhere access to data for patient care." Or even, as noted more recently by Dr. John Halamka, ACO = HIE + analytics. HIE has a growing accumulation of evidence concerning its effectiveness, obstacles, and sustainability.
We will follow the usual EPC approach in producing the evidence report. Our first activity will be to develop a set of key questions that the report will answer, embedded into a conceptual framework that shows the interrelationships among the questions. This process will be aided by engaging a set of "key informants" who represent various stakeholder groups, such as patients, providers, healthcare organizations, health policymakers, and vendors. They will help us in a process of "topic refinement" to specify the key questions [5].
Once our key questions are finalized, we will then perform a systematic review on each one. Systematic reviews differ from other kinds of topic reviews in that the questions are focused and amenable to being answered with scientific evidence [6]. They not only provide an inventory of existing scientific knowledge on a given topic but also inform us of the gaps in our knowledge.
Systematic reviews always begin with a comprehensive literature search, which casts a wide net for all possible evidence. The retrieved titles and abstracts are read and analyzed to determine which papers might meet inclusion criteria and should be pulled for reading of their full text. Those that meet the inclusion criteria are abstracted for the evidence, which is then is entered into evidence tables. If enough evidence from similar studies is retrieved, then a meta-analysis might be performed, where the data from each study is aggregated as if it were a single study. Meta-analysis summarizes the evidence as well as increases its statistical power.
This report will not focus solely on the evidence for the benefit of HIE. While that will clearly be an important part, we will also look at other aspects of HIE.
The first key question will likely concern the effectiveness of HIE. This will be assessed based on a variety of outcomes of its use, including healthcare process outcomes, intermediate outcomes, economic outcomes, clinical outcomes, and population-level outcomes. Other key questions will likely focus on:
- Usage
- Usability
- Harms
- Facilitators and barriers to successful use
- Sustainability
One of the challenges for defining evidence for HIE is that it is used as a means to other ends, usually improved clinical care. Since many other factors may influence care, it may be hard to tease out the true value of HIE. But we will aggregate all the evidence we can find to best discern the scientific picture of its value and limitations.
References
1. Hersh, WR, Helfand, M, et al. (2001). Clinical outcomes resulting from telemedicine interventions: a systematic review. BMC Medical Informatics and Decision Making. 1: 5. http://www.biomedcentral.com/1472-6947/1/5.
2. Hersh, W, Helfand, M, et al. (2002). A systematic review of the efficacy of telemedicine for making diagnostic and management decisions. Journal of Telemedicine and Telecare. 8: 197-209.
3. Hersh, WR, Hickam, DH, et al. (2006). The evidence base of telemedicine: overview of the supplement. Journal of Telemedicine & Telecare. 12(Supp 2): 1-2.
4. Hersh, WR, Hickam, DH, et al. (2006). Diagnosis, access, and outcomes: update of a systematic review on telemedicine services. Journal of Telemedicine & Telecare. 12(Supp 2): 3-31.
5. Buckley, DI, Ansari, M, et al. (2013). The Refinement of Topics for Systematic Reviews: Lessons and Recommendations From the Effective Health Care Program. Rockville, MD, Agency for Healthcare Quality & Research.
6. Khan, K, Kunz, R, et al. (2011). Systematic Reviews to Support Evidence-Based Medicine, 2nd Edition. Boca Raton, FL, CRC Press.
Friday, October 11, 2013
Gimme Some Analytics (We Already Have It!)
One of the most common requests we hear for new curricular content in our clinical informatics graduate program curriculum is for a course on "analytics." Indeed, this word has officially achieved buzzword status in informatics and maybe even all of healthcare. This was evidenced recently by the OHSU School of Medicine Senior Associate Dean for Education speaking with a hospital CEO in our state about what skills were most important in training our future physicians. The CEO replied with one: "predictive analytics." I am not sure I would value that skill highest in a physician I was seeing for a medical problem, although I would appreciate that physician having been trained in a data-driven, system-oriented approach.
I wrote in this blog last year that the focus of work of clinical informatics itself will likely change from electronic health record implementation to analytics, i.e., optimizing the systems we now have so all of the data accumulating in them can be used to improve health and healthcare delivery. I have also taken, like many other informatics researchers, a great interest in data-related issues [1, 2].
By the same token, I believe we need to be careful about the hype around analytics, something I alluded to in a recent posting reminding data scientists that they also needed to be research methodology scientists. Since that time, a colleague provided some additional examples of this, noting a recent talk she attended by a CEO of a data analytics company who proudly declared that he did not need to know anything about the underlying domain of medicine; he was only a data scientist. He proceeded to describe some correlations that his product had recently uncovered, such as a diagnosis of chronic kidney disease being associated with an ateriovenous (AV) fistula and septicemia being associated with hospitalization. (To which the medically knowledgeable people replied, "Duh!") In other words, finding correlations among things we already know clinically is hardly an advance for analytics. This is also borne out in another recent paper on "Why big won't cure us," with the author pointing out a myriad of technical, financial, and ethical issues that must be addressed before we will be able to make use of big data routinely in clinical practice [3].
Another issue is that while the use of data analytics is important and will grow, we cannot say with certainty what proportion of informatics professionals will actually be working in this area. There is no question that most informatics professionals will be working with data and information in some way, as these are the "lifeblood" of healthcare [4]. We also know that for every person who explicitly "does" analytics, there are some number of people, probably a larger proportion, who are either supporting analytics or doing other health IT work, whether with EHRs or other technologies. This is borne out by a recent report from the McKinsey consulting firm, which forecasts a demand for 140,000 to 190,000 positions for all data scientists in all fields (not limited to healthcare), and also another need for an addition 1.5 million managers and analysts who "can ask the right questions and consume the results of the analysis of big data effectively" [4].
What our informatics educational program needs to teach was borne out in a recent conversation with Brian Sikora, who is Director of Data & Information Management Enhancement in the Kaiser-Permanente Northwest Region. His service currently employs 110 analysts and plans to add more. Also good news for our students was our discussion about possible internship opportunities.
Mr. Sikora described four types of analysts in his department to our career development specialist, Ms. Virginia Lankes:
The jury is clearly out on who and how many will be doing what in analytics in the future work of informatics and larger healthcare. Just as I called for research delineating the informatics workforce years ago [5], we need a similar analysis with regards to analytics. We need to answer questions such as, how many people do we need to do the work of analytics, how many do we need to support it, what fraction of the informatics field will be working explicitly in analytics, and what training is needed for those who work directly in it and those who need to know it as part of a well-rounded informatics education.
References
1. Hersh, WR, Weiner, MG, et al. (2013). Caveats for the use of operational electronic health record data in comparative effectiveness research. Medical Care. 51(Suppl 3): S30-S37.
2. Hersh, WR, Cimino, JJ, et al. (2013). Recommendations for the use of operational electronic health record data in comparative effectiveness research. eGEMs (Generating Evidence & Methods to improve patient outcomes). 1: 14. http://repository.academyhealth.org/egems/vol1/iss1/14/.
3. Neff, G (2013). Why big data won't cure us. Big Data. 1: 117-123.
4. Manyika, J, Chui, M, et al. (2011). Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation.
I wrote in this blog last year that the focus of work of clinical informatics itself will likely change from electronic health record implementation to analytics, i.e., optimizing the systems we now have so all of the data accumulating in them can be used to improve health and healthcare delivery. I have also taken, like many other informatics researchers, a great interest in data-related issues [1, 2].
By the same token, I believe we need to be careful about the hype around analytics, something I alluded to in a recent posting reminding data scientists that they also needed to be research methodology scientists. Since that time, a colleague provided some additional examples of this, noting a recent talk she attended by a CEO of a data analytics company who proudly declared that he did not need to know anything about the underlying domain of medicine; he was only a data scientist. He proceeded to describe some correlations that his product had recently uncovered, such as a diagnosis of chronic kidney disease being associated with an ateriovenous (AV) fistula and septicemia being associated with hospitalization. (To which the medically knowledgeable people replied, "Duh!") In other words, finding correlations among things we already know clinically is hardly an advance for analytics. This is also borne out in another recent paper on "Why big won't cure us," with the author pointing out a myriad of technical, financial, and ethical issues that must be addressed before we will be able to make use of big data routinely in clinical practice [3].
Another issue is that while the use of data analytics is important and will grow, we cannot say with certainty what proportion of informatics professionals will actually be working in this area. There is no question that most informatics professionals will be working with data and information in some way, as these are the "lifeblood" of healthcare [4]. We also know that for every person who explicitly "does" analytics, there are some number of people, probably a larger proportion, who are either supporting analytics or doing other health IT work, whether with EHRs or other technologies. This is borne out by a recent report from the McKinsey consulting firm, which forecasts a demand for 140,000 to 190,000 positions for all data scientists in all fields (not limited to healthcare), and also another need for an addition 1.5 million managers and analysts who "can ask the right questions and consume the results of the analysis of big data effectively" [4].
What our informatics educational program needs to teach was borne out in a recent conversation with Brian Sikora, who is Director of Data & Information Management Enhancement in the Kaiser-Permanente Northwest Region. His service currently employs 110 analysts and plans to add more. Also good news for our students was our discussion about possible internship opportunities.
Mr. Sikora described four types of analysts in his department to our career development specialist, Ms. Virginia Lankes:
- Data analysts - the most technical group who do the architecture
- Report analysts – design reports, dashboards, key performance indicators; wider background, business intelligence domain.
- Business systems analysts – understand system, workflow and the people they are working with to process and know how data is produced in a specific area. “Here’s the problem - help us solve it.” Deep expertise in operational areas.
- Informatics analysts – (the largest group) which includes statisticians; they help operations leaders interpret and analyze data. Data mining, text mining.
- Programming skills - analytics professionals must have programming skills, especially in data-related areas for locating and extracting data, using tools such as SQL and SAS.
- Statistics
- Understanding the healthcare environment
- Communication skills - ability to work with clinical, administrative, and financial staff to understand their programs and present solutions in written and oral form
- Critical thinking - including the ability to understand a business problem, identify the appropriate data elements, extract and aggregate the data, and use it to solve the practical problem
The jury is clearly out on who and how many will be doing what in analytics in the future work of informatics and larger healthcare. Just as I called for research delineating the informatics workforce years ago [5], we need a similar analysis with regards to analytics. We need to answer questions such as, how many people do we need to do the work of analytics, how many do we need to support it, what fraction of the informatics field will be working explicitly in analytics, and what training is needed for those who work directly in it and those who need to know it as part of a well-rounded informatics education.
References
1. Hersh, WR, Weiner, MG, et al. (2013). Caveats for the use of operational electronic health record data in comparative effectiveness research. Medical Care. 51(Suppl 3): S30-S37.
2. Hersh, WR, Cimino, JJ, et al. (2013). Recommendations for the use of operational electronic health record data in comparative effectiveness research. eGEMs (Generating Evidence & Methods to improve patient outcomes). 1: 14. http://repository.academyhealth.org/egems/vol1/iss1/14/.
3. Neff, G (2013). Why big data won't cure us. Big Data. 1: 117-123.
4. Manyika, J, Chui, M, et al. (2011). Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation.
5. Hersh, WR (2006). Who are the informaticians? What we know and should know. Journal of the American Medical Informatics Association. 13: 166-170.
Wednesday, October 9, 2013
Further Evidence That Health IT Job Growth Has Been Underestimated, and Some Ramifications
New data from an analysis of online job postings confirms that employment growth in health information technology (HIT) has even further exceeded projections, driven by funding from the Health Information Technology for Economic and Clinical Health (HITECH) Act. In addition, two other reports show that the job market for those working with electronic health record (EHR) and related systems continues to be strong for employees and challenging for employers.
In the new analysis, Schwartz and colleagues used a comprehensive database of 84 million online job postings, extracted out those related to HIT, and built a model aiming to determine the influence of HITECH [1]. The authors limited their focus to jobs that would be defined in the realm of clinical informatics. Although this was important for their goal of assessing the influence of HITECH, from the perspective of a biomedical informatics graduate program director like myself, this excluded other important informatics jobs, such as those in imaging informatics, bioinformatics, clinical research informatics, and other areas where a graduates of our program are employed.
The analysis classified jobs into two broad categories, HIT core jobs and HIT clinical user jobs. The former category included those developing, implementing, supporting, and selling EHRs, while the latter included clinicians, receptionists, technicians, and other personnel making heavy use of EHRs in their jobs. Wearing my informatics educational program director's hat, I was most interested in the authors' results for the HIT core job listings, as these individuals would be most likely to be employed in (and to seek education preparing them for) informatics careers.
Schwartz et al. counted a total of HIT-related 434,282 job postings between 2007-2011, with 226,356 HIT core jobs and 207,926 HIT-related clinical user jobs. Yes, not 41,000 [2], not even 51,000 [3], but over 226,000!
For both categories of HIT jobs combined, the authors categorized employer type and provided a percentage of all HIT jobs for each. The largest employer category was IT vendor, which included IT service providers, consulting firms, sales firms, and IT staffing firms hiring developers and posted 42% of all HIT-related listings. Another 39% were posted by healthcare provider organizations. The remaining 19% were either ambiguous as to the employer type (15%) or another type of employer (4%).
For all HIT jobs, they also categorized and tallied job responsibilities, which could be assigned to more than one category for a given posting. The most frequent job responsibility was implementation support, with 43% of jobs including responsibilities such as system installation, customization, building, debugging, purchasing, or workflow redesign. The next highest category was user training (27%), followed by system development (22%). This was followed by technical support, with 21% of jobs including the maintaining of continued technical functionality or providing customer support. Other responsibilities included IT strategy (long-term IT planning and system optimization in the clinical setting - 13%), sales (11%), and research (quantitative hypothesis testing using health IT systems - 6%).
Their model estimated that about 48% of the job growth was due to HITECH, with the remainder due to growth that would have continued at historical trends prior to HITECH. Some other interesting findings for the time period between 2007-2011 included HIT jobs growing from 0.75% to nearly 2.5% of all healthcare job postings (consistent with prior findings of healthcare organizations hiring one IT employee per 48-60 non-IT employees [2]) and an approximately four-fold increase in the number of jobs posted.
It will remain to be seen how strong the job market remains as the HITECH incentives wind down, although HIT will continue to be a cost of doing business in healthcare, especially as the system moves to payment models that require better management and use of data, such as accountable care organizations and primary care/patient-centered medical homes [4].
Two other reports show that healthcare organizations are responding to the need for hiring and maintaining more HIT talent. A report by Towers Watson that surveyed 100 healthcare organizations [5] found that 67% of reported problems in attracting experienced IT employees, with an even higher proportion (73%) finding problems hiring Epic-certified employees. These organizations also reported problems in retaining experienced IT employees (38%) and Epic-certified employees (52%) as well. Many fewer organizations reported problems in attracting (14%) or retaining (9%) new graduates with IT skills, indicating a strong incentive for those early in their careers to get up to speed quickly.
The Towers Watson report also discerned differences between employees and employers in the drivers of attracting and retaining IT personnel. For attracting personnel, employees listed job security and salary as their primary concerns, whereas employers believed that challenging work and organizational reputation were key drivers. There was more concordance on drivers of IT employee retention, with both employees and employers ranking salary and opportunities for career advancement highest.
The second report was the first of what will be an annual survey of the HIT workforce by HIMSS Analytics [6]. The survey was completed by 224 individuals who were HIMSS corporate members as well as hospital and health system IT executives. About three-quarters of respondents were employed by healthcare provider organizations, including standalone hospitals (45%), hospitals as part of an integrated delivery system (17%), and corporate offices of a delivery system (13%). The remaining respondents worked vendor organizations, including companies (20%) and consulting firms (4%).
Over 80% of healthcare provider organizations reported adding IT FTE in the past year, with half hiring 1-5 FTE and the rest hiring more. Only 8% reporting laying off staff. The most common areas for hiring were clinical application support (51% of all healthcare provider organizations hiring), help desk (51%), IT management (29%), financial application support (28%), system design and implementation (24%), IT security (22%), project management (21%), clinical informatics/clinical champion (19%), system integration (19%), user training (15%). About three-quarters of provider organizations outsourced rather than hired some of the above types of personnel. Essentially similar percentages were seen in hiring plans for the coming year.
A similar picture was seen for vendors and consultants for the past year, with over 90% adding FTE, and nearly 60% hiring more than 20 FTE. Most common areas for hiring included sale and marketing (88%), field support staff (84%), support staff (68%), and executive team (58%). Nearly a third reported laying off some staff. Similar to provider organizations, comparable percentages were noted in hiring plans for the coming year.
Various types of certification were deemed important, more so vendors than healthcare provider organizations. The most highly rated certifications were security professional, network/architecture support, database administrator, project manager, and informatics professional.
Strategies for retaining qualified staff were also important for providers and vendors, including offering professional development activities (60% for providers, 64% for vendors), paid tuition (48%, 41%), membership payment in professional associations (35%, 41%), and telecommuting (29%, 52%).
About 80% of providers and 57% of vendors reported lack of fully qualified staff as a barrier to achieving organizational IT goals. The most common reason for lack of staff was lack of qualified staff in their local region (43%, 56%). Both types of organizations reported hires being attracted to other organizations by more lucrative offers (25%, 19%). About 31% of provider organizations reported putting an IT initiative on hold due to inadequate staffing, with another 19% contemplating doing so.
Overall, these new data show that the HIT job market is robust, and provides great opportunities for a career. Of course, like essentially all professional areas these days, the job market will change. This will be driven in HIT by the tapering of the incentive funding from HITECH and the shift from implementing to optimizing (i.e,, making use of the data) systems. This will require that the skill sets of professionals, and the curricular content of educational programs, adapt to (or ideally anticipate) the likely changes.
References
1. Schwartz, A, Magoulas, R, et al. (2013). Tracking labor demand with online job postings: the case of health IT workers and the HITECH Act. Industrial Relations: A Journal of Economy and Society. 52: 941–968.
2. Hersh, W (2010). The health information technology workforce: estimations of demands and a framework for requirements. Applied Clinical Informatics. 1: 197-212.
3. 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/.
4. Anonymous (2011). Support for Accountable Care: Recommended Health IT Infrastructure. Washington, DC, eHealth Initiative. http://www.ehidc.org/resource-center/reports/view_document/103-reports-support-for-accountable-care-recommended-health-it-infrastructure-accountable-care.
5. Anonymous (2013). Closing the IT Talent Gap in Health Care - The Towers Watson 2013 Health Care IT Survey Report, Towers Watson. http://www.towerswatson.com/en/Insights/IC-Types/Survey-Research-Results/2013/03/Closing-the-IT-Talent-Gap-in-Health-Care.
6. Anonymous (2013). 2013 HIMSS Workforce Survey. Chicago, IL, HIMSS Analytics. http://www.himssanalytics.com/research/AssetDetail.aspx?pubid=82097&tid=128.
In the new analysis, Schwartz and colleagues used a comprehensive database of 84 million online job postings, extracted out those related to HIT, and built a model aiming to determine the influence of HITECH [1]. The authors limited their focus to jobs that would be defined in the realm of clinical informatics. Although this was important for their goal of assessing the influence of HITECH, from the perspective of a biomedical informatics graduate program director like myself, this excluded other important informatics jobs, such as those in imaging informatics, bioinformatics, clinical research informatics, and other areas where a graduates of our program are employed.
The analysis classified jobs into two broad categories, HIT core jobs and HIT clinical user jobs. The former category included those developing, implementing, supporting, and selling EHRs, while the latter included clinicians, receptionists, technicians, and other personnel making heavy use of EHRs in their jobs. Wearing my informatics educational program director's hat, I was most interested in the authors' results for the HIT core job listings, as these individuals would be most likely to be employed in (and to seek education preparing them for) informatics careers.
Schwartz et al. counted a total of HIT-related 434,282 job postings between 2007-2011, with 226,356 HIT core jobs and 207,926 HIT-related clinical user jobs. Yes, not 41,000 [2], not even 51,000 [3], but over 226,000!
For both categories of HIT jobs combined, the authors categorized employer type and provided a percentage of all HIT jobs for each. The largest employer category was IT vendor, which included IT service providers, consulting firms, sales firms, and IT staffing firms hiring developers and posted 42% of all HIT-related listings. Another 39% were posted by healthcare provider organizations. The remaining 19% were either ambiguous as to the employer type (15%) or another type of employer (4%).
For all HIT jobs, they also categorized and tallied job responsibilities, which could be assigned to more than one category for a given posting. The most frequent job responsibility was implementation support, with 43% of jobs including responsibilities such as system installation, customization, building, debugging, purchasing, or workflow redesign. The next highest category was user training (27%), followed by system development (22%). This was followed by technical support, with 21% of jobs including the maintaining of continued technical functionality or providing customer support. Other responsibilities included IT strategy (long-term IT planning and system optimization in the clinical setting - 13%), sales (11%), and research (quantitative hypothesis testing using health IT systems - 6%).
Their model estimated that about 48% of the job growth was due to HITECH, with the remainder due to growth that would have continued at historical trends prior to HITECH. Some other interesting findings for the time period between 2007-2011 included HIT jobs growing from 0.75% to nearly 2.5% of all healthcare job postings (consistent with prior findings of healthcare organizations hiring one IT employee per 48-60 non-IT employees [2]) and an approximately four-fold increase in the number of jobs posted.
It will remain to be seen how strong the job market remains as the HITECH incentives wind down, although HIT will continue to be a cost of doing business in healthcare, especially as the system moves to payment models that require better management and use of data, such as accountable care organizations and primary care/patient-centered medical homes [4].
Two other reports show that healthcare organizations are responding to the need for hiring and maintaining more HIT talent. A report by Towers Watson that surveyed 100 healthcare organizations [5] found that 67% of reported problems in attracting experienced IT employees, with an even higher proportion (73%) finding problems hiring Epic-certified employees. These organizations also reported problems in retaining experienced IT employees (38%) and Epic-certified employees (52%) as well. Many fewer organizations reported problems in attracting (14%) or retaining (9%) new graduates with IT skills, indicating a strong incentive for those early in their careers to get up to speed quickly.
The Towers Watson report also discerned differences between employees and employers in the drivers of attracting and retaining IT personnel. For attracting personnel, employees listed job security and salary as their primary concerns, whereas employers believed that challenging work and organizational reputation were key drivers. There was more concordance on drivers of IT employee retention, with both employees and employers ranking salary and opportunities for career advancement highest.
The second report was the first of what will be an annual survey of the HIT workforce by HIMSS Analytics [6]. The survey was completed by 224 individuals who were HIMSS corporate members as well as hospital and health system IT executives. About three-quarters of respondents were employed by healthcare provider organizations, including standalone hospitals (45%), hospitals as part of an integrated delivery system (17%), and corporate offices of a delivery system (13%). The remaining respondents worked vendor organizations, including companies (20%) and consulting firms (4%).
Over 80% of healthcare provider organizations reported adding IT FTE in the past year, with half hiring 1-5 FTE and the rest hiring more. Only 8% reporting laying off staff. The most common areas for hiring were clinical application support (51% of all healthcare provider organizations hiring), help desk (51%), IT management (29%), financial application support (28%), system design and implementation (24%), IT security (22%), project management (21%), clinical informatics/clinical champion (19%), system integration (19%), user training (15%). About three-quarters of provider organizations outsourced rather than hired some of the above types of personnel. Essentially similar percentages were seen in hiring plans for the coming year.
A similar picture was seen for vendors and consultants for the past year, with over 90% adding FTE, and nearly 60% hiring more than 20 FTE. Most common areas for hiring included sale and marketing (88%), field support staff (84%), support staff (68%), and executive team (58%). Nearly a third reported laying off some staff. Similar to provider organizations, comparable percentages were noted in hiring plans for the coming year.
Various types of certification were deemed important, more so vendors than healthcare provider organizations. The most highly rated certifications were security professional, network/architecture support, database administrator, project manager, and informatics professional.
Strategies for retaining qualified staff were also important for providers and vendors, including offering professional development activities (60% for providers, 64% for vendors), paid tuition (48%, 41%), membership payment in professional associations (35%, 41%), and telecommuting (29%, 52%).
About 80% of providers and 57% of vendors reported lack of fully qualified staff as a barrier to achieving organizational IT goals. The most common reason for lack of staff was lack of qualified staff in their local region (43%, 56%). Both types of organizations reported hires being attracted to other organizations by more lucrative offers (25%, 19%). About 31% of provider organizations reported putting an IT initiative on hold due to inadequate staffing, with another 19% contemplating doing so.
Overall, these new data show that the HIT job market is robust, and provides great opportunities for a career. Of course, like essentially all professional areas these days, the job market will change. This will be driven in HIT by the tapering of the incentive funding from HITECH and the shift from implementing to optimizing (i.e,, making use of the data) systems. This will require that the skill sets of professionals, and the curricular content of educational programs, adapt to (or ideally anticipate) the likely changes.
References
1. Schwartz, A, Magoulas, R, et al. (2013). Tracking labor demand with online job postings: the case of health IT workers and the HITECH Act. Industrial Relations: A Journal of Economy and Society. 52: 941–968.
2. Hersh, W (2010). The health information technology workforce: estimations of demands and a framework for requirements. Applied Clinical Informatics. 1: 197-212.
3. 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/.
4. Anonymous (2011). Support for Accountable Care: Recommended Health IT Infrastructure. Washington, DC, eHealth Initiative. http://www.ehidc.org/resource-center/reports/view_document/103-reports-support-for-accountable-care-recommended-health-it-infrastructure-accountable-care.
5. Anonymous (2013). Closing the IT Talent Gap in Health Care - The Towers Watson 2013 Health Care IT Survey Report, Towers Watson. http://www.towerswatson.com/en/Insights/IC-Types/Survey-Research-Results/2013/03/Closing-the-IT-Talent-Gap-in-Health-Care.
6. Anonymous (2013). 2013 HIMSS Workforce Survey. Chicago, IL, HIMSS Analytics. http://www.himssanalytics.com/research/AssetDetail.aspx?pubid=82097&tid=128.
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