This week has been a milestone for this blog, with the number of page views surpassing the 100,000 mark since its inception in March, 2009. This achievement gives me a chance to reflect on my use of social media, which seems to be different than for others. Maybe social media is just like anything else in life, with different people preferring differing aspects and uses of it.
Clearly I have enjoyed being a blogger. This blog has provided me a nice platform from which to share my thoughts and views with a worldwide audience. As I have noted before, I am not a stream of consciousness blogger, feeling compelled to post things continuously, such as every day. Rather, I prefer that my postings carefully reflect thoughts and ideas on specific topics.
Another social media activity I enjoy is Facebook. I have three main networks on Facebook, and I enjoy seeing them interact with my personal and professional life. These networks include my professional colleagues, my family and friends, and my high school classmates. Facebook is also a great medium for sharing and annotating photos and other digital artifacts.
Two social media activities I personally find less valuable to myself are Twitter and LinkedIn. I know this is at odds with some dear friends and colleagues. However, tweets just seem too short (I often have more to say than can be expressed in 140 characters!) and fleeting (it seems you either catch something in the Twitter stream or never see it again) to sustain my interest. Sometimes I try to get involved in the Twitter dialogue at conferences, but soon find it distracting to try to otherwise participate in the meeting (whose primary value is usually the direct personal interaction). I have the most fun with it when I use it to editorialize about presentations at those meetings, but I find it difficult to sustain any sort of dialogue when doing that.
As for LinkedIn, while I am sure it is highly valuable for some people, I find that my major interaction with it is to receive requests for connections and endorsements. I am happy to connect with anyone on LinkedIn, but I have yet to find value in the hundreds of connections I have made. I also do not like to make generic LinkedIn endorsements, instead preferring to serve as real references for colleagues and current or former students when they need it for specific opportunities.
I know that other people have different preferences for social media, and perhaps my own preferences will change over time. And of course, it is likely that the social media tools and sites will change over time, or that new ones will emerge. For now, however, I will keep blogging and Facebooking while still trying to determine the value of other social media.
Tuesday, February 26, 2013
Sunday, February 24, 2013
Data Mining Systems Improve Cost and Quality of Healthcare - Or Do They?
Several email lists I am on were abuzz last week about the publication of a paper that was described in a press release from Indiana University to demonstrate that "machine learning -- the same computer science discipline that helped create voice recognition systems, self-driving cars and credit card fraud detection systems -- can drastically improve both the cost and quality of health care in the United States." The press release referred to a study published by an Indiana faculty member in the journal, Artificial Intelligence in Medicine [1].
While I am a proponent of computer applications that aim to improve the quality and cost of healthcare, I also believe we must be careful about the claims being made for them, especially those derived from results from scientific research.
After reading and analyzing the paper, I am skeptical of the claims made not only by the press release but also by the authors themselves. My concern is less about their research methods, although I have some serious qualms about them I will describe below, but more so with the press release that was issued by their university public relations office. Furthermore, as always seems to happen when technology is hyped, the press release was picked up and echoed across the Internet, followed by the inevitable conflation of its findings. Sure enough, one high-profile blogger wrote, "physicians who used an AI framework to make patient care decisions had patient outcomes that were 50 percent better than physicians who did not use AI." It is clear from the paper that physicians did not actually use such a framework, which was only applied retrospectively to clinical data.
What exactly did the study show? Basically, the researchers obtained a small data set for one clinical condition in one institution's electronic health record and applied some complex data mining techniques to show that lower cost and better outcomes could be achieved by following the options suggested by the machine learning algorithm instead of what the clinicians actually did. The claim, therefore, is that if the data mining were followed by the clinicians instead of their own decision-making, then better and cheaper care would ensue.
As done in many scientific papers about technology, the paper goes into exquisite detail about the data mining algorithms and the experiments comparing them. But the paper unfortunately provides very little description about the clinical data itself. There is a reference to another paper from a conference that appears to describe the data set [2], but it is still not clear how the data was applied to evaluate the algorithms.
I have a number of methodological problems with the paper. First is the paucity of clinical details about the data. The authors refer to a metric called the "outcomes rating scale" of the "client-directed outcome informed (CDOI) assessment." No details are provided as to exactly what this scale measures or how differences in measurement correlate with improved clinical outcome. Furthermore, the variables of the details of care for the patient that the data mining algorithm supposedly outperforms are not described either. Therefore anyone hoping to understand the clinical value that this approach is claimed to have improved is not able to do so.
A second problem is that there is no discussion about the cost data or what cost perspective (e.g., system, clinician, societal, etc.) is taken. This is a common problem that plagues many studies in healthcare that attempt to measure costs [3]. Given the relatively modest amounts of money spent on the care that is reported in their results, amounting only to a few hundred dollars per patient, it is unlikely that the data includes the full amount of the costs of treatment for each patient, or over an appropriate time period. If my interpretation of the low value of the cost data is correct (which is difficult to discern from reading the paper due, again due to lack of details), the data do not include the cost of clinician time, facilities, or longer-term costs beyond the time frame of the data set. If that is indeed the case, then it would be particularly problematic for a machine learning system, since such systems make inferences limited only to the data that is provided to the model. Therefore if poor data is provided to the model, its "conclusions" are suspect. (This raises a side issue as to whether there is truly "artificial intelligence" here, since the only intelligence applied by the system is the models developed by their human creators.)
A third concern is that this is a modeling study. As every evaluation methodologist knows, modeling studies are limited in their ability to assign cause and effect. There is certainly a role in informatics science for modeling studies, although we saw recently that such studies have their limits, especially when revisited over the long run. In this study, there may have been reasons for the clinicians following the more expensive path or confounding reasons why such patients had worse outcomes, but they cannot be captured by the approach used in this study.
This is related to the final and most serious problem of the work, which is that the modeling evaluation is a very weak form of evidence to demonstrate the value of an intervention. If the authors truly wanted to show the benefits of the system and approach they developed, they should have performed a randomized controlled trial that compared their intervention with an appropriate control group. This would have led to the type of study that the blogger mentioned above erroneously described this to be. Such a study design would assess some of the more vexing problems we face in informatics, such as whether the advice coming from a computer will change clinician behavior. Or, when such systems are introduced into the "real world," whether the "advice" provided will prospectively lead to better outcomes.
I do believe that the kind of work addressed by this paper is important, especially as we move into the area of personalized medicine. As eloquently described by Stead and colleagues, healthcare will soon be reaching the point where the number of data points required for clinical decisions will exceed the bounds of human cognition [4]. (It probably already has.) Therefore clinicians will require aids to their cognition provided by information systems, perhaps one like that described in the study.
But such aids require, like everything else in medicine, robust evaluative research to demonstrate their value. The methods used in this paper may indeed be the methods to provide this value, but the implementation and evaluation described miss the mark. That miss is further exacerbated by the hype and conflation the ensued after the paper was published.
What can we learn from this paper and its ensuing hype? First, bold claims require bold evidence to back them up. In the case of showing value for an approach in healthcare - be it test, treatment, or informatics application - we must use evaluation methods that provide best evidence for the claim. That is not always a randomized controlled trial, but in this situation, it would be, and the modeling techniques used are really just preliminary data that (might) justify an actual clinical trial. Second, when we perform technology evaluation, we need to describe, and ideally release, all of the clinical data so that others can analyze and even replicate the results. Finally, while we all want to disseminate the results of our research to the widest possible audience, we need to be realistic in explaining what we accomplished and what are its larger implications.
References
[1] Bennett, C. and K. Hauser (2013). Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach. Artificial Intelligence in Medicine. Epub ahead of print.
[2] Bennett, C., T. Doub, A. Bragg, J. Luellen, C. VanRegenmorter, J. Lockman and R. Reiserer (2011). Data mining session-based patient reported outcomes (PROs) in a mental health setting: toward data-driven clinical decision support and personalized treatment. 2011 First IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB 2011), San Jose, CA. 229-236.
[3] Drummond, M. and M. Sculpher (2005). Common methodological flaws in economic evaluations. Medical Care. 43(7 Suppl): 5-14.
[4] Stead, W., J. Searle, H. Fessler, J. Smith and E. Shortliffe (2011). Biomedical informatics: changing what physicians need to know and how they learn. Academic Medicine. 86: 429-434.
While I am a proponent of computer applications that aim to improve the quality and cost of healthcare, I also believe we must be careful about the claims being made for them, especially those derived from results from scientific research.
After reading and analyzing the paper, I am skeptical of the claims made not only by the press release but also by the authors themselves. My concern is less about their research methods, although I have some serious qualms about them I will describe below, but more so with the press release that was issued by their university public relations office. Furthermore, as always seems to happen when technology is hyped, the press release was picked up and echoed across the Internet, followed by the inevitable conflation of its findings. Sure enough, one high-profile blogger wrote, "physicians who used an AI framework to make patient care decisions had patient outcomes that were 50 percent better than physicians who did not use AI." It is clear from the paper that physicians did not actually use such a framework, which was only applied retrospectively to clinical data.
What exactly did the study show? Basically, the researchers obtained a small data set for one clinical condition in one institution's electronic health record and applied some complex data mining techniques to show that lower cost and better outcomes could be achieved by following the options suggested by the machine learning algorithm instead of what the clinicians actually did. The claim, therefore, is that if the data mining were followed by the clinicians instead of their own decision-making, then better and cheaper care would ensue.
As done in many scientific papers about technology, the paper goes into exquisite detail about the data mining algorithms and the experiments comparing them. But the paper unfortunately provides very little description about the clinical data itself. There is a reference to another paper from a conference that appears to describe the data set [2], but it is still not clear how the data was applied to evaluate the algorithms.
I have a number of methodological problems with the paper. First is the paucity of clinical details about the data. The authors refer to a metric called the "outcomes rating scale" of the "client-directed outcome informed (CDOI) assessment." No details are provided as to exactly what this scale measures or how differences in measurement correlate with improved clinical outcome. Furthermore, the variables of the details of care for the patient that the data mining algorithm supposedly outperforms are not described either. Therefore anyone hoping to understand the clinical value that this approach is claimed to have improved is not able to do so.
A second problem is that there is no discussion about the cost data or what cost perspective (e.g., system, clinician, societal, etc.) is taken. This is a common problem that plagues many studies in healthcare that attempt to measure costs [3]. Given the relatively modest amounts of money spent on the care that is reported in their results, amounting only to a few hundred dollars per patient, it is unlikely that the data includes the full amount of the costs of treatment for each patient, or over an appropriate time period. If my interpretation of the low value of the cost data is correct (which is difficult to discern from reading the paper due, again due to lack of details), the data do not include the cost of clinician time, facilities, or longer-term costs beyond the time frame of the data set. If that is indeed the case, then it would be particularly problematic for a machine learning system, since such systems make inferences limited only to the data that is provided to the model. Therefore if poor data is provided to the model, its "conclusions" are suspect. (This raises a side issue as to whether there is truly "artificial intelligence" here, since the only intelligence applied by the system is the models developed by their human creators.)
A third concern is that this is a modeling study. As every evaluation methodologist knows, modeling studies are limited in their ability to assign cause and effect. There is certainly a role in informatics science for modeling studies, although we saw recently that such studies have their limits, especially when revisited over the long run. In this study, there may have been reasons for the clinicians following the more expensive path or confounding reasons why such patients had worse outcomes, but they cannot be captured by the approach used in this study.
This is related to the final and most serious problem of the work, which is that the modeling evaluation is a very weak form of evidence to demonstrate the value of an intervention. If the authors truly wanted to show the benefits of the system and approach they developed, they should have performed a randomized controlled trial that compared their intervention with an appropriate control group. This would have led to the type of study that the blogger mentioned above erroneously described this to be. Such a study design would assess some of the more vexing problems we face in informatics, such as whether the advice coming from a computer will change clinician behavior. Or, when such systems are introduced into the "real world," whether the "advice" provided will prospectively lead to better outcomes.
I do believe that the kind of work addressed by this paper is important, especially as we move into the area of personalized medicine. As eloquently described by Stead and colleagues, healthcare will soon be reaching the point where the number of data points required for clinical decisions will exceed the bounds of human cognition [4]. (It probably already has.) Therefore clinicians will require aids to their cognition provided by information systems, perhaps one like that described in the study.
But such aids require, like everything else in medicine, robust evaluative research to demonstrate their value. The methods used in this paper may indeed be the methods to provide this value, but the implementation and evaluation described miss the mark. That miss is further exacerbated by the hype and conflation the ensued after the paper was published.
What can we learn from this paper and its ensuing hype? First, bold claims require bold evidence to back them up. In the case of showing value for an approach in healthcare - be it test, treatment, or informatics application - we must use evaluation methods that provide best evidence for the claim. That is not always a randomized controlled trial, but in this situation, it would be, and the modeling techniques used are really just preliminary data that (might) justify an actual clinical trial. Second, when we perform technology evaluation, we need to describe, and ideally release, all of the clinical data so that others can analyze and even replicate the results. Finally, while we all want to disseminate the results of our research to the widest possible audience, we need to be realistic in explaining what we accomplished and what are its larger implications.
References
[1] Bennett, C. and K. Hauser (2013). Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach. Artificial Intelligence in Medicine. Epub ahead of print.
[2] Bennett, C., T. Doub, A. Bragg, J. Luellen, C. VanRegenmorter, J. Lockman and R. Reiserer (2011). Data mining session-based patient reported outcomes (PROs) in a mental health setting: toward data-driven clinical decision support and personalized treatment. 2011 First IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB 2011), San Jose, CA. 229-236.
[3] Drummond, M. and M. Sculpher (2005). Common methodological flaws in economic evaluations. Medical Care. 43(7 Suppl): 5-14.
[4] Stead, W., J. Searle, H. Fessler, J. Smith and E. Shortliffe (2011). Biomedical informatics: changing what physicians need to know and how they learn. Academic Medicine. 86: 429-434.
Friday, February 22, 2013
AMIA Clinical Informatics Board Review Course Announced
This week, the American Medical Informatics Association (AMIA) released the details of the Clinical Informatics Board Review Course (CIBRC), for which I will serving as Course Director. I am excited to see this come to fruition, and look forward not only to these course offerings but also the expansion of the program as other certifications in informatics for non-physicians become reality in the years ahead.
The course will be offered four times in person before the first offering of the certification exam in October. There will also be an online version of the course that can be accessed in tandem with the live courses or taken alone. A question bank of practice questions will also be made available. The four offerings of the course will be:
For those with less knowledge of the field, a better approach might be to start with a course that more fundamentally builds their knowledge base. The 10x10 ("ten by ten") course is option, although even it is only a single course and may not be enough for those with little or no formal training in the field. The next Oregon Health & Science University (OHSU) offering of its 10x10 course runs from April through July; registration is available on the AMIA Web site. For those individuals desiring more training, a more comprehensive course of study leading to a Graduate Certificate or master's degree may be a better option. AMIA has a database of educational programs, which includes our program at OHSU.
The rollout of the course is an exciting event. For convenience, here is an index to some of my important previous postings on the clinical informatics subspecialty:
The course will be offered four times in person before the first offering of the certification exam in October. There will also be an online version of the course that can be accessed in tandem with the live courses or taken alone. A question bank of practice questions will also be made available. The four offerings of the course will be:
- April 12-14 - Bethesda, MD (registration has opened for this course and will soon be available for the others)
- June 7-9 - Philadelphia, PA
- August 9-11 - Portland, OR (I am thrilled to have one offering of the course in my own city!)
- September 7-9 - Rosemont (Chicago), IL
- Overview of the course
- Course Design
- Faculty
- Frequently asked questions
- History of the course and subspecialty
For those with less knowledge of the field, a better approach might be to start with a course that more fundamentally builds their knowledge base. The 10x10 ("ten by ten") course is option, although even it is only a single course and may not be enough for those with little or no formal training in the field. The next Oregon Health & Science University (OHSU) offering of its 10x10 course runs from April through July; registration is available on the AMIA Web site. For those individuals desiring more training, a more comprehensive course of study leading to a Graduate Certificate or master's degree may be a better option. AMIA has a database of educational programs, which includes our program at OHSU.
The rollout of the course is an exciting event. For convenience, here is an index to some of my important previous postings on the clinical informatics subspecialty:
- Eligibility for the subspecialty (January, 2013)
- Some challenges for building capacity of the subspecialty (September, 2012)
- Mapping the OHSU informatics program content to the core content (July, 2012)
Wednesday, February 13, 2013
From HITECH to Accountable Care: A Student and Workforce Development Program Success Story
This year's State of the Union address by President Barack Obama noted that controlling the costs of healthcare, particularly of Medicare and Medicaid, is a critical element of addressing the government's debt problem, especially in the long run. A key element of this approach, as the President noted, will be new models of care delivery. As he stated, "medical bills shouldn’t be based on the number of tests ordered or days spent in the hospital; they should be based on the quality of care that our seniors receive." One of the key elements for operationalizing this approach is the development of accountable care organizations (ACOs) [1], whose goals are to achieve Dr. Donald Berwick's "triple aim" of improved health, improved healthcare delivery, and reduced cost [2].
To this end, President Obama invited Oregon's Governor, Dr. John Kitzhaber, to be present at his address. Dr. Kitzhaber's presence acknowledged his leadership and innovation in healthcare reform that is taking place in Oregon. With help from the Obama Administration, Oregon is revamping its entire Medicaid program under a new brand of ACOs known as Coordinated Care Organizations (CCOs). The Oregon CCOs will provide value-driven coordinated care for Oregon Medicaid patients in pursuit of the "triple aim." As with all ACOs, managing information, including that beyond the electronic health record (EHR), will be critical for the success of Oregon's CCOs. Indeed, a recent post by Dr. John Halamka posited that "ACO = HIE + analytics," a shorthand way of stating that ACOs (and CCOs) will require robust health information exchange (HIE) and data analytics.
The importance of health information technology (HIT) to accountable care was recognized in the Health Information Technology for Clinical and Economic Health (HITECH) Act when it was passed in 2009 [3]. Indeed, one of the original roles for HITECH was to serve as a "down payment" for healthcare reform [4].
I am pleased to report of one recent instance, small but significant, where HITECH funding has indeed resulted in an outcome that is helping healthcare reform and accountable care. It is the story of two individuals who pursued our University-Based Training (UBT) program and have both been hired by Health Share of Oregon, the CCO for the Portland, Oregon tri-county region.
Isolde Knaap worked in the State of Oregon's Department of Human Services for over 20 years in various positions as a data analyst/system developer/research analyst. During that time, she built a wealth of experience managing data and systems in public health and child welfare. Aiming to advance her skills and career more into the HIT realm, she applied and was accepted into the Oregon Health & Science University (OHSU) Graduate Certificate Program, funded by the UBT training grant. Her previous educational background include a Bachelor of Arts in Modern Languages with a secondary teaching certificate.
After graduation from the program, Isolde was hired by Health Share of Oregon as a Senior IT Project Manager. Like all CCOs, Health Share Oregon is tasked to improve health outcomes and reduce health cost through collaboration with previously disparate health care providers. Isolde's position will help align IT processes so that the care delivery transformation activities of Health Share Oregon achieve the triple-aim goals while also achieving administrative simplification.
One of the courses in her program of study Isolde found most useful was Introduction to Standards and Interoperability. This gave her an appreciation for the challenges of health information exchange, a critical function for CCOs who share financial accountability for providing care for their patients. She stated that she was asked in various interviews what she knew about EHRs, and replied that she could confidently state that she had taken the courses, Clinical Information Systems and Clinical Information Systems Laboratory, and later served as a teaching assistant in the latter. She also purposefully chose a practicum with a local Veteran's Administration hospital to get exposure to their VistA EHR (which was used in the Clinical Information Systems Laboratory course).
Charles Sorgie is another UBT graduate who has been hired by Health Share of Oregon, serving as a Senior IT Business Analyst. Charles has a long history of work in the IT field as a software developer, designer, and architect. He had no experience in the healthcare domain but had developed a documentation management system to support electronic design automation that was subsequently adopted by the aerospace industry to manage their maintenance documentation. In addition, he was involved more recently in the process modeling and enterprise modeling fields, which led him to become interested in ways to organize and model the processes, resources, and work products involved in complex business interactions. His educational experience was a Master of Science in Computer Science.
In his position with Health Share of Oregon, Charles is involved in the gathering of stakeholder requirements and acting as a coordinator and liaison between those stakeholders and the IT teams tasked with the deployment of solutions to satisfy those requirements. He notes that the OHSU UBT program proved him a basic understanding of the challenges in the support of clinical processes, their privacy and security requirements, their workflow, and the standards used to support that workflow (e.g., HL7). It also gave him a background to the basic terminology used (e.g., personal health information, or PHI) as well as the challenges surrounding the business processes that support those clinical processes. All of this helps him be more productive in his current position.
Both Isolde and Charles report to Daniel Dean, the Chief Information Officer (CIO) of Health Share of Oregon. Their connection to Mr. Dean was made possible by another resource provided by our UBT grant, which is Virginia Lankes, who is the Career Development Specialist that the grant enabled us to hire. The connections Ms. Lankes had nurtured with Mr. Dean made him aware of our two students as they were completing their studies.
Isolde and Charles are thus examples of how the UBT program has developed the HIT workforce and how the HITECH program is contributing to healthcare reform. The two of them also provide a refreshing counterexample to the common adage that one must have a clinical background to succeed in biomedical and health informatics. As I have written, the work of informatics is shifting from implementation to data, and their experience and expertise in using data seems to have played an important role in their new positions.
The story of Isolde and Charles provide more examples of how the UBT funding of our educational program has helped individuals advance their careers and added jobs to the economy. Their experiences build on the successful outcomes that other students in the program have had, as I documented in 2011 and 2012.
Postscript: Sure enough, the day after this posting, an article appeared in the New England Journal of Medicine, describing the Oregon CCO program [5]. It is a well-written overview and freely available, so I will add this postscript to provide this additional information.
References
1. Berwick, D. (2011). Making good on ACOs' promise--the final rule for the Medicare shared savings program. New England Journal of Medicine 365: 1753-1756.
2. Berwick, D., T. Nolan and J. Whittington (2008). The triple aim: care, health, and cost. Health Affairs 27: 759-769.
3. Blumenthal, D. (2010). Launching HITECH. New England Journal of Medicine 362: 382-385.
4. Blumenthal, D. (2009). Stimulating the adoption of health information technology. New England Journal of Medicine 360: 1477-1479.
5. Stecker, E. (2013). The Oregon ACO experiment — bold design, challenging execution. New England Journal of Medicine Epub ahead of print.
Monday, February 11, 2013
The Informatics Professor on Video
Some video interviews of me have appeared on the Web. They are part of some interesting and worthy sites.
The first video was an appearance on the Web-based show, Health IT Live!, which features a host of informatics experts from around the world. I took part in Healthcare IT Live! Episode #6. The interview discussed a variety of topics, mostly related to my research and educational work in biomedical and health informatics.
I was also recently interviewed for a site called AskimoTV, which purports to provide access to experts, initially via video interviews and later to those wanting one-on-one consultations. My interview was done in three topical segments:
1. Biomedical and Health informatics: Improving healthcare through information
2. Barriers to Retrieving Patient Information from Electronic Health Record
3. Building A Health Informatics Workforce In Developing Countries
These interviews demonstrate the simple power of the Web and its ability to disseminate information in ways we could not have imagined a couple decades ago.
Saturday, February 2, 2013
Marketing the OHSU Informatics Program
The Oregon Health & Science University (OHSU) Biomedical Informatics Graduate Program is undertaking a new marketing campaign. This campaign is, of course, a business decision, but I also find it a great opportunity to spread the word about our program as well as careers in the profession of informatics.
We are especially interested in touting the benefits of education and careers in informatics for younger people. The mid-career individuals who enter our program mostly know well the problems in healthcare and how informatics can address them. But we find that younger people, probably because of their less experience with the healthcare system (and its dysfunction), are not aware of how informatics can not only lead to a rewarding career but also benefit health, improve healthcare delivery, and advance basic and clinical research. There are also opportunities in bioinformatics and computational biology of which they may actually be more aware. The graphic below shows our message.
As such, the focus of this marketing campaign aims at younger people. Not that we do not want to continue to attract the more common mid-career healthcare or other professional who enters the clinical informatics track of our program, but our marketing focus is aimed at those who are younger and probably have little knowledge of healthcare, biomedical research, or genomics. The campaign aims at both of the major tracks in our program, clinical informatics and bioinformatics.
Another reason for the challenge in marketing our program is the complexity of its tracks, degrees and certificates, and other aspects.
An additional message we are promoting concerns the value of our program. We note that while our tuition is comparable with most other programs, we feature a large and world-renowned faculty, great longevity of over 20 years, and nearly 500 alumni who have successfully obtained employment in a wide variety of settings.
The components of the marketing campaign include:
We will see how this translates to new student enrollment, especially among the younger people who will someday be users, implementers, and leaders of the new data-rich, information-driven healthcare system that guides our vision now.
We are especially interested in touting the benefits of education and careers in informatics for younger people. The mid-career individuals who enter our program mostly know well the problems in healthcare and how informatics can address them. But we find that younger people, probably because of their less experience with the healthcare system (and its dysfunction), are not aware of how informatics can not only lead to a rewarding career but also benefit health, improve healthcare delivery, and advance basic and clinical research. There are also opportunities in bioinformatics and computational biology of which they may actually be more aware. The graphic below shows our message.
Another reason for the challenge in marketing our program is the complexity of its tracks, degrees and certificates, and other aspects.
An additional message we are promoting concerns the value of our program. We note that while our tuition is comparable with most other programs, we feature a large and world-renowned faculty, great longevity of over 20 years, and nearly 500 alumni who have successfully obtained employment in a wide variety of settings.
The components of the marketing campaign include:
- A banner ad on Radiolab.org - This Web site, run by New York Public Radio, features podcasts on a variety of topics around science and arts. In addition to having the banner add to click-through to our Web site, our program is also acknowledged at the beginning and in the middle of each podcast (here is an example).
- Print ads in west coast college newspapers - Print ads are a challenge these days, as readership of college newspapers has declined as with all print media. But such ads do still reach our target audience, and will be supplemented by our usual attendance of graduate school fairs and other events at these colleges. The ad we are running is shown above.
- Google Adwords campaign
- Ad placement on Web pages via Quantcast
- Ongoing Webinars - I gave our first Webinar on January 31, 2013, which is available for viewing, along with the slide deck.
We will see how this translates to new student enrollment, especially among the younger people who will someday be users, implementers, and leaders of the new data-rich, information-driven healthcare system that guides our vision now.
Tuesday, January 29, 2013
Implementing the Learning Healthcare System Can Be Facilitated Using the Principles of Evidence-Based Medicine
The enthusiasm for big data and for the use of analytics and business intelligence with that data is reaching a fevered pitch. I share that enthusiasm, but also know from both my clinical and my informatics experience that knowledge will not emanate just by turning on the data spigot from the growing number of electronic health record (EHR) systems now in operational use. However, if we approach the problem properly, I believe we can achieve the goals of the learning healthcare system as eloquently laid out in various reports from the Institute of Medicine (IOM) [1, 2].
One sensible approach was published recently in Annals of Internal Medicine [3]. The authors were from Group Health Cooperative in Seattle, a leader in the use of data and information systems to improve the quality and outcomes of care. The paper is summarized well by a figure that shows a continuous cycle of design-implementation-evaluation-adjustment of improved care, with interaction with the external environment through scanning for identification of problems and solutions and dissemination to share what has been learned in their setting.
A complementary approach to learning from EHR and other clinical data can be to apply the basic approach of evidence-based medicine (EBM) [4]. In some ways, EBM is antagonistic to EHR data analytics, with the former giving the most value to evidence from controlled experiments, especially randomized controlled trials (RCTs), while the latter makes use of real-world observational data that may be incomplete, incorrect, and inconsistent.
But I maintain that we can look to the process of EBM to guide us in how to best approach the "evidence" of EHR data analytics and the learning health system. EBM is not just about finding RCTs. Rather, it uses a principled approach to find and apply the best evidence to make clinical decisions. In particular, EBM done most effectively uses four steps:
In the second step, the principle from EBM is very much the same, even if the techniques of obtaining evidence are very different. The "evidence" in the case of the learning health system is the data in EHR and other systems that, as noted above, may be incomplete, incorrect, and inconsistent. We therefore need to determine if we have the proper data and, if so, whether it can applied to answer our question.
For the third step, just as with EBM, we must critically appraise our evidence. Can we trust the inferences and conclusions from the data? Are there confounding variables of which we may not be aware? This may be critical with EHR data where assignment of cause and effect could be difficult, if not impossible. The solution likely comes back to asking the right question, i.e., one we can have confidence in the correct answer.
Finally, we have to ask, can the data be applied in our setting? Just as some RCTs answer questions in patient populations very different from those of the clinician making decisions, it must be ascertained if the results obtained from this approach can be applied to a specific patient or setting.
The growing quantity of clinical data in operational clinical systems provides a foundation for the learning healthcare system. However, we must approach the questions we ask and how we answer them with caution and a sound methodology. The approach of EBM offers a framework for carrying out this very different but complementary work.
References
1. Eden J, Wheatley B, McNeil B, and Sox H, eds. Knowing What Works in Health Care: A Roadmap for the Nation. 2008, National Academies Press: Washington, DC.
2. Smith M, Saunders R, Stuckhardt L, and McGinnis JM, Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. 2012, Washington, DC: National Academies Press.
3. Greene SM, Reid RJ, and Larson EB, Implementing the learning health system: from concept to action. Annals of Internal Medicine, 2012. 157: 207-210.
4. Straus SE, Glasziou P, Richardson WS, and Haynes RB, Evidence-Based Medicine: How to Practice and Teach It, 4e. 2010, New York, NY: Churchill Livingstone.
One sensible approach was published recently in Annals of Internal Medicine [3]. The authors were from Group Health Cooperative in Seattle, a leader in the use of data and information systems to improve the quality and outcomes of care. The paper is summarized well by a figure that shows a continuous cycle of design-implementation-evaluation-adjustment of improved care, with interaction with the external environment through scanning for identification of problems and solutions and dissemination to share what has been learned in their setting.
A complementary approach to learning from EHR and other clinical data can be to apply the basic approach of evidence-based medicine (EBM) [4]. In some ways, EBM is antagonistic to EHR data analytics, with the former giving the most value to evidence from controlled experiments, especially randomized controlled trials (RCTs), while the latter makes use of real-world observational data that may be incomplete, incorrect, and inconsistent.
But I maintain that we can look to the process of EBM to guide us in how to best approach the "evidence" of EHR data analytics and the learning health system. EBM is not just about finding RCTs. Rather, it uses a principled approach to find and apply the best evidence to make clinical decisions. In particular, EBM done most effectively uses four steps:
- Ask an answerable question
- Find the best evidence
- Critically appraise the evidence
- Apply it to the patient situation
In the second step, the principle from EBM is very much the same, even if the techniques of obtaining evidence are very different. The "evidence" in the case of the learning health system is the data in EHR and other systems that, as noted above, may be incomplete, incorrect, and inconsistent. We therefore need to determine if we have the proper data and, if so, whether it can applied to answer our question.
For the third step, just as with EBM, we must critically appraise our evidence. Can we trust the inferences and conclusions from the data? Are there confounding variables of which we may not be aware? This may be critical with EHR data where assignment of cause and effect could be difficult, if not impossible. The solution likely comes back to asking the right question, i.e., one we can have confidence in the correct answer.
Finally, we have to ask, can the data be applied in our setting? Just as some RCTs answer questions in patient populations very different from those of the clinician making decisions, it must be ascertained if the results obtained from this approach can be applied to a specific patient or setting.
The growing quantity of clinical data in operational clinical systems provides a foundation for the learning healthcare system. However, we must approach the questions we ask and how we answer them with caution and a sound methodology. The approach of EBM offers a framework for carrying out this very different but complementary work.
References
1. Eden J, Wheatley B, McNeil B, and Sox H, eds. Knowing What Works in Health Care: A Roadmap for the Nation. 2008, National Academies Press: Washington, DC.
2. Smith M, Saunders R, Stuckhardt L, and McGinnis JM, Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. 2012, Washington, DC: National Academies Press.
3. Greene SM, Reid RJ, and Larson EB, Implementing the learning health system: from concept to action. Annals of Internal Medicine, 2012. 157: 207-210.
4. Straus SE, Glasziou P, Richardson WS, and Haynes RB, Evidence-Based Medicine: How to Practice and Teach It, 4e. 2010, New York, NY: Churchill Livingstone.
Sunday, January 27, 2013
The Health IT "Grand Experiment": Mid-Study Check-Up
It seems that whenever there is something negative about health information technology (HIT) in the popular press, I get emails from people inside and outside the field, asking "what's wrong?" A case in point is a recent article in the New York Times [1], reporting on a "negative" point of view from two researchers from the RAND Corp. that was published in the journal Health Affairs [2]. One of the interesting twists of the Health Affairs piece was that it was written by researchers from RAND, the same organization that published a modeling study in 2005 that concluded that investment in HIT could provide potential annual savings to the healthcare system of $142-$371 billion [3]. About the same time, another model-based study from the Center for Information Technology Leadership (CITL) found similar potential savings [4]. This data in part led to the inclusion of HIT in the Health Information Technology for Economic and Clinical Health (HITECH) Act, the program from the American Recovery and Reinvestment Act (ARRA) of 2009, also known as the economic stimulus bill, that invested up to $29 billion in the adoption and "meaningful use" of the electronic health record (EHR) and other HIT [5].
What can we conclude from this recent publication and reporting about it in the popular press? As always, it is best to look at exactly what has been claimed, what evidence supports it, and where it fits in the larger picture of this topic.
The 2005 RAND study modeled savings that could occur from HIT adoption [3]:
The CITL study used a somewhat similar modeling approach and drew similar conclusions. The CITL model focused on different types of health information exchange (HIE), from simple transmission of documents to full semantic interoperability of EHR systems. The latter approach was shown to achieve the most benefit, up to $77 billion per year.
Can we assess the correctness of these modeling studies, now that we have substantially increased EHR adoption through HITECH? The recent paper from RAND noted that the question is not simple to answer, but that HIT probably has fallen short of its promises, especially in terms of reducing costs [2]. Of course, one of the challenges in answering the question of cost-reduction is that it is difficult to attribute avoidable cost in the healthcare system. We do know that healthcare costs have reduced their rate of growth in the last few years, probably mainly due to the economic recession [10]. But we cannot know for sure how much of that reduction might be due to HIT adoption.
But an even bigger reason why we cannot know if the modeling studies are true is that we have achieved the kind of HIT environment that these studies assumed in the development of their models. The original RAND study assumed, as noted above, interconnected and interoperable systems that were adopted widely and used effectively. The authors of the new RAND paper note that HIT failure has come in large part because of failure to reach those assumption. In particular,
Therefore my view echoes that of the RAND researchers in the new Health Affairs piece, which is that yes, HIT has not yet delivered on its promise to improve efficiency and reduce cost in the healthcare system. But the proposition that it inherently is not able to do so is also not known. As such, if we hope for that improvement, the grand experiment should go on. There is no question that the required time will be longer, the resources required will be larger, and the cultural change will be more difficult. There is also quite valid concern that there are some untended consequences of the staged approach in HITECH, which may be locking clinicians and hospitals into monolithic systems that are difficult to use and expand. I sympathize with the notion of current market-leader systems locking us into an "EHR trap," where the EHR should not be a monolithic application but instead a platform on top of which we can build apps that provide innovative functions and/or make new use of the data [11].
Over the last few years, I have ended many a talk on informatics noting that a "grand experiment" in our field was taking place, with the complete results unlikely to be years away. This study can be viewed as a mid-study assessment, and we can conclude that the benefits have not yet accrued, but that it may be too early to conclude that they will not occur. Although I agree that we probably need some mid-course correction in our approach, I also argue that we cannot go back nor should we end the experiment prematurely. We also must remember the motivations for implementing HIT and reforming healthcare in the first place, which is the error-prone and financially dysfunctional existing system, which both undermines competitiveness of US companies globally due to high employee healthcare costs as well as threatening to bankrupt the US government through unsustainable Medicare cost increases.
References
1. Abelson R and Creswell J, In Second Look, Few Savings From Digital Health Records, New York Times. January 10, 2013. http://www.nytimes.com/2013/01/11/business/electronic-records-systems-have-not-reduced-health-costs-report-says.html.
2. Kellermann AL and Jones SS, What will it take to achieve the as-yet-unfulfilled promises of health information technology? Health Affairs, 2013. 32: 63-68.
3. Hillestad R, Bigelow J, Bower A, Girosi F, Meili R, Scoville R, et al., Can electronic medical record systems transform health care? Health Affairs, 2005. 24: 1103-1117.
4. Pan E, Johnston D, Walker J, Adler-Milstein J, Bates DW, and Middleton B, The Value of Healthcare Information Exchange and Interoperability. 2004, Center for Information Technology Leadership: Boston, MA.
5. Blumenthal D, Launching HITECH. New England Journal of Medicine, 2010. 362: 382-385.
6. Himmelstein DU and Woolhandler S, Hope and hype: predicting the impact of electronic medical records. Health Affairs, 2005. 24: 1121-1123.
7. Goodman C, Savings in electronic medical record systems? Do it for the quality. Health Affairs, 2005. 24: 1124-1126.
8. Walker JM, Electronic medical records and health care transformation. Health Affairs, 2005. 24: 1118-1120.
9. Orszag P, Evidence on the Costs and Benefits of Health Information Technology. 2008, Congressional Budget Office: Washington, DC, http://www.cbo.gov/ftpdocs/91xx/doc9168/05-20-HealthIT.pdf.
10. Hartman M, Martin AB, Benson J, and Catlin A, National health spending in 2011: overall growth remains low, but some payers and services show signs of acceleration. Health Affairs, 2013. 32: 87-99.
11. Mandl KD and Kohane IS, Escaping the EHR trap--the future of health IT. New England Journal of Medicine, 2012. 366: 2240-2242.
What can we conclude from this recent publication and reporting about it in the popular press? As always, it is best to look at exactly what has been claimed, what evidence supports it, and where it fits in the larger picture of this topic.
The 2005 RAND study modeled savings that could occur from HIT adoption [3]:
- Reduced adverse drug events that extend hospital length of stay in the inpatient setting and avoid hospitalization in the outpatient setting
- Increased used of cost-effective immunizations and screening interventions
- Improved efficiency of chronic disease management
The CITL study used a somewhat similar modeling approach and drew similar conclusions. The CITL model focused on different types of health information exchange (HIE), from simple transmission of documents to full semantic interoperability of EHR systems. The latter approach was shown to achieve the most benefit, up to $77 billion per year.
Can we assess the correctness of these modeling studies, now that we have substantially increased EHR adoption through HITECH? The recent paper from RAND noted that the question is not simple to answer, but that HIT probably has fallen short of its promises, especially in terms of reducing costs [2]. Of course, one of the challenges in answering the question of cost-reduction is that it is difficult to attribute avoidable cost in the healthcare system. We do know that healthcare costs have reduced their rate of growth in the last few years, probably mainly due to the economic recession [10]. But we cannot know for sure how much of that reduction might be due to HIT adoption.
But an even bigger reason why we cannot know if the modeling studies are true is that we have achieved the kind of HIT environment that these studies assumed in the development of their models. The original RAND study assumed, as noted above, interconnected and interoperable systems that were adopted widely and used effectively. The authors of the new RAND paper note that HIT failure has come in large part because of failure to reach those assumption. In particular,
- We do not have interconnected and interoperable systems. In part, this is because many EHR systems are still closed and proprietary. In addition, HIE efforts are still early and nascent.
- We also do not have wide adoption yet of systems, especially advanced systems. While HITECH has led to increased adoption, there is still a long ways to go.
- And probably the biggest shortcoming has been lack of EHRs being used effectively. The adoption incentives in Stage 1 of meaningful use focus (by design) on building the data foundation. More effective use will come based on that foundation in Stage 2 and beyond.
Therefore my view echoes that of the RAND researchers in the new Health Affairs piece, which is that yes, HIT has not yet delivered on its promise to improve efficiency and reduce cost in the healthcare system. But the proposition that it inherently is not able to do so is also not known. As such, if we hope for that improvement, the grand experiment should go on. There is no question that the required time will be longer, the resources required will be larger, and the cultural change will be more difficult. There is also quite valid concern that there are some untended consequences of the staged approach in HITECH, which may be locking clinicians and hospitals into monolithic systems that are difficult to use and expand. I sympathize with the notion of current market-leader systems locking us into an "EHR trap," where the EHR should not be a monolithic application but instead a platform on top of which we can build apps that provide innovative functions and/or make new use of the data [11].
Over the last few years, I have ended many a talk on informatics noting that a "grand experiment" in our field was taking place, with the complete results unlikely to be years away. This study can be viewed as a mid-study assessment, and we can conclude that the benefits have not yet accrued, but that it may be too early to conclude that they will not occur. Although I agree that we probably need some mid-course correction in our approach, I also argue that we cannot go back nor should we end the experiment prematurely. We also must remember the motivations for implementing HIT and reforming healthcare in the first place, which is the error-prone and financially dysfunctional existing system, which both undermines competitiveness of US companies globally due to high employee healthcare costs as well as threatening to bankrupt the US government through unsustainable Medicare cost increases.
References
1. Abelson R and Creswell J, In Second Look, Few Savings From Digital Health Records, New York Times. January 10, 2013. http://www.nytimes.com/2013/01/11/business/electronic-records-systems-have-not-reduced-health-costs-report-says.html.
2. Kellermann AL and Jones SS, What will it take to achieve the as-yet-unfulfilled promises of health information technology? Health Affairs, 2013. 32: 63-68.
3. Hillestad R, Bigelow J, Bower A, Girosi F, Meili R, Scoville R, et al., Can electronic medical record systems transform health care? Health Affairs, 2005. 24: 1103-1117.
4. Pan E, Johnston D, Walker J, Adler-Milstein J, Bates DW, and Middleton B, The Value of Healthcare Information Exchange and Interoperability. 2004, Center for Information Technology Leadership: Boston, MA.
5. Blumenthal D, Launching HITECH. New England Journal of Medicine, 2010. 362: 382-385.
6. Himmelstein DU and Woolhandler S, Hope and hype: predicting the impact of electronic medical records. Health Affairs, 2005. 24: 1121-1123.
7. Goodman C, Savings in electronic medical record systems? Do it for the quality. Health Affairs, 2005. 24: 1124-1126.
8. Walker JM, Electronic medical records and health care transformation. Health Affairs, 2005. 24: 1118-1120.
9. Orszag P, Evidence on the Costs and Benefits of Health Information Technology. 2008, Congressional Budget Office: Washington, DC, http://www.cbo.gov/ftpdocs/91xx/doc9168/05-20-HealthIT.pdf.
10. Hartman M, Martin AB, Benson J, and Catlin A, National health spending in 2011: overall growth remains low, but some payers and services show signs of acceleration. Health Affairs, 2013. 32: 87-99.
11. Mandl KD and Kohane IS, Escaping the EHR trap--the future of health IT. New England Journal of Medicine, 2012. 366: 2240-2242.
Thursday, January 17, 2013
What Do Twenty-First Century Healthcare Professional Students Need to Learn About Informatics?
I am increasingly involved in efforts to determine the content and competencies in informatics for 21st century clinicians. Not only medical students at Oregon Health & Science University (OHSU) but also other healthcare professionals, such as nurses, physical/occupational therapists, nutritionists, and others at OHSU and some other health science universities.
This effort is congruent with the growing push for interprofessional education. The rationale behind inter professional education is that if the healthcare system is to embrace the vision of team-based coordinated care, then clinicians of the future must have at least some of their education together. The 21st century clinician needs to understand that the care they provide will be monitored for quality, safety, and cost. This will hopefully be done in a constructive and self-improving way, but also making sure that mistakes and waste are not swept under the proverbial rug of the paper chart (or not documented at all).
Informatics can be viewed as the ultimate interprofessional activity. There is really very little about informatics that is specific to any healthcare professional. Yes, physicians, nurses, and others need to learn the informatics applications specific to their work. But the underlying concepts of informatics, i.e., the use of information to improve health and healthcare, really apply to any healthcare professional (not to mention the patient, the researcher, and others!).
So what does the 21st century clinician need to know about informatics? Rather than provide a list, I will explain my thoughts in narrative form, showing the key informatics concepts that might comprise a list underlined so that they may form a list later.
A first critical concept is that informatics is not the same as computer literacy. Computer literacy is one of many requirements to use informatics successfully, but knowing how to use a computing device (PC, tablet, or smartphone) is not the same as having skills in informatics, i.e., using that device to improve health, healthcare, public health, or research.
Certainly one fundamental skill for 21st century clinicians is something we began teaching in the late 20th century, which is how to find information to apply to patient care. This is not just knowing what terms to enter into a search engine, but the whole process of asking answerable questions, finding information to answer them, critically appraising that information, and applying it to patients (or populations). The skills of the 21st century clinician start, and not stop, with the knowledge of how to enter simple queries into Google or Pubmed. The 21st century clinician should a power searcher, a skill we often associate with librarians or informaticians. Not that there is no roles for librarians and informaticians, but they should be more teachers and consultants when it comes to finding information.
Starting from the beginning, the 21st century clinician must be skilled information retrieval, what some might term search or others might term knowledge management. Whatever we call it, the 21st century clinician must know how to formulate a clinical question as an answerable one, and then be able to select the appropriate resource and make optimal use of it. This use needs to include knowing what content is in different search systems. This clinician must know that Google has almost all pages on the "visible" Web but not the part that is hidden from its indexing crawlers, while Pubmed is a bibliographic database that indexes biomedical and clinical journal literature. The 21st century clinician must know about specialized resources such as the AHRQ Guidelines Clearinghouse and the CDC Travel site. He or she should also have an understanding of the major commercial publishers as well as what their professional societies offers. Once they know how to use a search site, they must be able to phrase an appropriate query. Even sites like Google, with its ultra-simple interface, has additional features that can greatly enhance its retrieval capabilities. Pubmed has a myriad of features of great value to clinicians, probably the most important being the Clinical Queries interface that help focus the content of the search on more evidence-based articles. But it also offers much more.
Finally, once information is retrieved, the 21st century clinician must know how to critically appraise information retrieved and apply it to the patient (or population). As with searching, the type of appraisal varies with the search engine used. With output from general search engines like Google, the clinician must be able to assess the trustworthiness of the information. Google's algorithm of ranking pages by number of others that link to it actually does a pretty good job of promoting reputable sites to the top of the output. But it is not perfect, and the user must be discriminating. (Back in the 1990s, we used to teach clinicians to avoid using general search engines, since they did not discriminate well among good vs. poor sites, but that is less of an issue, not only because search engines are better but also because people are more savvy about the Web.)
Of course, clinician competency in informatics in the 21st century goes well beyond searching. The modern clinician must also know how to make optimal use of patient data and information. He or she must know how to use informatics to strive for Berwick's triple aim of better health, better care, and lower cost. In my mind, the best vision for this approach comes from the recent Institute of Medicine (IOM) report, Best Care, Lower Cost. This report creates a framework from which essentially all informatics competencies can be contextualized. It presents a compelling vision for a healthcare system that is patient-centered, learning, and population-based, concepts to be explained more fully below.
This also means an understanding that the patient record is more than "charting," and that its value goes beyond being able to read it. Certainly the 21st century clinician must be facile with all aspects of the electronic health record (EHR), being able to easily move from one system to another, and to understand why it is critical for health information exchange (HIE) to make any one record as complete as possible. But the EHR is more than looking up information about a patient. It becomes critically important as healthcare moves from a focus on quantity to one of value. The notion of value includes quality, patient safety, and attention to cost. This requires coordination of care, and not just providing medical procedures, nursing interventions, therapies, etc. in isolation. Coordination requires teamwork and communication.
In this context, the health record is no longer a passive collection of information used mainly to justify billing. Rather, it is a source of data, organized into coherent information, that allows the healthcare team to deliver the best, safest, and most cost-effective care. As such, the 21st century clinician must have a basic understanding of informatics issues, such as capturing data that is correct and complete as well as consistent in its expression. He or she must be able to work in partnership with informatics professionals to achieve what we know is so critical in the application of informatics, such as adhering to standards, achieving system interoperability, appropriately and optimally implementing clinical decision support, and maintaining security to assure privacy and confidentiality.
This view also requires that the 21st century clinician have some understanding of areas like quality measurement and improvement. If nothing else, he or she should understand quality measures because his or her work will increasingly be measured, used to assess quality and how to improve it, and maybe even influence their level of pay. But they should also understand the rationale for measuring quality, including how consistent quality of care is now, and how to work with clinical leaders to select, implement, and improve measures.
Another important area of safe, effective, and coordinated care is patient engagement. Not only is patient engagement the best thing to do from a healthcare standpoint, but 21st century patients, especially aging and Internet-savvy baby boomers, will demand it. Patients will want healthcare that adapts the online features of other modern industries, such as being able to view their own data and interact with their clinicians and healthcare system (e.g., online scheduling of appointments, prescription refills, and even consultations that are appropriate for online). These will likely take the form of a personal health record, accessible from a patient portal that allows access to all information, not just that from the system of the provider organization.
The 21st century clinician must also have some knowledge and understanding of the appropriate use of telemedicine and telehealth, done both to remote locations as well as more locally in patient's homes and other settings.
Complementary to the patient-centric view, the 21st century clinician must also understand population-based care and the informatics underlying it. The clinician and their team will be caring for populations of patients. They must be able to view their care needs and results across their patient population. When a new test or treatment comes along that is determined to be highly effective, they must be able to quickly identify patients who are candidates for it. They must also be able to identify outliers in their populations who require intervention, such as those with excessively high blood pressure or blood sugar, missed appointments or screen tests, or those at risk for hospital (re-)admission.
I also believe there are other areas where 21st century clinicians should have an understanding. One of these is bioinformatics, especially as it relates to personalized medicine. No, the modern clinician need not understand complicated gene sequencing algorithms, but he or she should have an understanding of how genomics and related areas are transforming our understanding of maintaining health, diagnosing disease, and treating it. If the vision of personalized medicine comes to pass, the 21st century clinician will need the help of decision support and other tools for help in applying it to individual patients. He or she should at least have a basic understanding of genomewide association studies and their ramifications.
The 21st century clinician must also understand the strengths and limitations of clinical research. He or she must understand the differences and value contributed by experimental and observational studies. Ideally, the student will have participated in research while in their training. But even if not, he or she should understand issues like data quality, study design, and the limitations that come from the sharp focus perspective of a clinical study. The 21st century student should more generally be able to participate well in the learning health system laid out in the vision of groups like the IOM.
There is certainly a great deal of informatics for the 21st century clinician to learn and be able to apply. From the pedagogical standpoint, there is also the issue of how to deliver it. One way not to deliver it is to have its own separate course, isolated from the rest of the curriculum. There will be a need for educators who are specialists in informatics to (collaboratively with clinical educators) design the learning and to deliver that which is appropriate for lecture, group discussions, and other didactic settings. But informatics is one of those topics that is best infused throughout the curriculum, especially in clinical settings where it is being used.
Curriculum change can be hard. Academia can be one of the most tradition-bound settings, resistant to change. But just as healthcare must change, so must the education of its clinicians. Informatics is one excellent means of fostering interprofessional learning and interaction.
Friday, January 11, 2013
Eligibility for the Clinical Informatics Subspecialty
(Postscript: This posting was originally published on January 11, 2013. Thanks to two colleagues, Ted Shortliffe and Ben Munger, a few minor corrections and clarifications have been made, and it is being re-posted on January 13, 2013.)
One of the most common email messages I receive these days is an inquiry from a physician about his or her eligibility for the clinical informatics subspecialty. I am writing this posting in part to have a link to send to people in reply to those emails. But before I go further, let me make one vital disclaimer clear: I am not the decision-maker! That will be the exclusive role of the American Board of Preventive Medicine (ABPM), which is the administrative home for the subspecialty. I can give educated guesses based on what the ABPM has said and written, but ultimately it is their decision whether or not someone is eligible.
Shortly before the end of 2012, the ABPM released a one-page document on the clinical informatics subspecialty certification exam and qualifications for eligibility. The first exam will be available during a two-week window between October 7-18, 2013, with registration opening in March. The registration process will include a determination of whether an applicant is eligible for board certification, i.e., be allowed to take the exam.
The next most common questions I am asked are (a) what educational programs at Oregon Health & Science University (OHSU) will make me eligible and (b) what educational programs at OHSU or elsewhere will best prepare me for the exam? I will address those questions after providing what I know about the primary question on eligibility.
The eligibility requirements are clearly laid out in the ABPM document, so those who want to determine if they are eligible should read them carefully. The first three requirements are relatively straight-forward. In short, they are:
The first of the two pathways is the "practice pathway." Those who have been working in informatics professionally for at least 25% time during any three of the previous five years, and can have a supervisory individual attest to it, are eligible for this pathway. "Working" in informatics not only includes "practice" (i.e., being a Chief Medical Information Officer), but also teaching and research.
The second pathway is the "non-traditional fellowship," which is any informatics fellowship of 24 or more months duration deemed acceptable by ABPM. At the November 4, 2012 panel at the American Medical Informatics Association (AMIA) Annual Symposium, Dr. William Greaves of ABPM stated this would be composed of informatics educational programs that were listed in the proposal submitted to ABPM by AMIA in 2009. This list, which has not been made public by ABPM or AMIA, included programs that were funded by training grants from the National Library of Medicine (NLM) or were members of the AMIA Academic Forum at the time the proposal was submitted by AMIA to ABMS in 2009. (I can say that OHSU was definitely on the list, since we were both NLM-funded and a member of the Academic Forum at that time and still are. both). Dr. Greaves also said that ABPM would review applicants trained in other fellowships for eligibility on a case-by-case basis.
The ABPM document also states that time spent in training in informatics could be applied to the practice pathway at one-half the value of practice time. In other words, someone in an educational program for at least 50% time during the previous five years would be eligible to take the certification exam. My interpretation of what he said (remember, I do not make the rules and they may change!) is that someone in a master's degree program that involves the equivalent of one and a half years of full-time study would thus be eligible. If my interpretation is correct, this would mean that completing the Master of Biomedical Informatics (MBI) Program at OHSU within a five-year time span would make one eligible, since it requires six academic quarters of full-time study. The OHSU Graduate Certificate Program, on the other hand, which is a subset of the MBI requiring about nine months of study if done full-time, thus would not be enough. Presumably one can mix and match to achieve eligibility, i.e., with some practice and some education.
As for the common question I get about which OHSU program would make one eligible, the answer then depends on how much practice pathway eligibility one has. If one completely meets the practice pathway criteria, then how much education is a moot point; they are eligible on the basis of professional work. But for those who have not worked in the field enough to quality by the practice pathway, it likely means they would need to complete the MBI or have enough education to make up for the "shortfall" in their practice time, perhaps with a Graduate Certificate.
The second common question I get asked is, what educational programs at OHSU and elsewhere will best prepare one for the exam? This answer is also part guess, as ABPM has not released any information about the exam content beyond saying it will likely reflect the core content outline that submitted with the ABMS proposal and published in JAMIA in 2009. Last year I created a matrix that mapped each element of the core content outline to an OHSU course. The sum of OHSU courses pretty much covered the outline, but the problem is that it required 23 courses to do so. This is about 50% more than required for the MBI. (Remember that OHSU is on an academic quarter system, so the number of courses is larger than programs on a semester system.) However, it is also clear from the matrix that a relatively constrained set of courses could cover a fairly large portion of the core content outline. Furthermore, we are undertaking a process to reorganize the curriculum with an eye to creating a set of courses that will be "core" for the core content, i.e., cover a substantial portion of the outline, while still maintaining the balance that we believe is important to learn in informatics. (The core content outline is likely to be fairly similar for the certifications that emerge for other healthcare professionals and PhDs that AMIA is now planning to propose. In fact, it is not very specific to "physician" informatics, and actually provides a good overview of the critical content necessary for mastery by all who work in clinical informatics.)
I believe that the eight courses required to obtain a Graduate Certificate can be fashioned in a way to prepare one well for the certification exam. Indeed, I can also see where the content of the Graduate Certificate program could form the basis of the didactic portion of a clinical informatics fellowship (perhaps allowing the practice time and fellowship project to add enough credits to qualify one for a master's degree).
I am also sometimes asked if the 10x10 ("ten by ten") course (which is the equivalent of one course, the introductory course, in our Graduate Certificate and MBI programs), or the AMIA Board Review course I will be directing, will cover enough to enable someone to pass the exam by just taking one or both. I believe it is unlikely that these courses alone, without any other formal training, would give one enough knowledge to pass the exam (although I suspect some will try, and perhaps succeed). It should be noted that achieving a sufficient grade of the optional final exam in the OHSU 10x10 course will provide credit to those eligible for study in the OHSU Graduate Certificate or MBI programs, which gets one course under their belt and gives them a trajectory for more.
(By the way, for those who are wondering: The AMIA Board Review course details will be announced in February. The current working plan is to offer the course three times between June and September, after the ABPM registration period opens but enough in advance of the actual exam. There will likely be East Coast, Midwest, and West Coast sites for the three offerings. The courses in the first year will be all face-to-face, although online versions will be developed for subsequent years. A brief interview of me about the course from the AMIA Symposium is available, as is an interview of Dr. Greaves.
Ironically, one activity for which there is no guarantee of being adequately prepared is a traditional research-oriented fellowship, such as those funded by NLM training grants. If one's course of study in one of these fellowships includes a course of study containing a good deal of practical clinical informatics courses, then that preparation should be excellent. However, not all informatics fellowships offer such coursework, as the primary purpose of the NLM-funded fellowships is to train future researchers. There may also be individuals in these fellowships who are extremely well-trained in other areas of informatics, such as bioinformatics or imaging informatics, who will technically be eligible for certification though not really well-prepared to pass an exam focused on practical clinical informatics. Indeed, even those who have a clinically oriented but highly theoretical curriculum may not be able to pass the exam that will have a very practical and applied focus.
I should also reiterate that this eligibility process only applies to the first five years of the subspecialty. After that time, the only way to achieve eligibility for certification will be in an ACGME-accredited clinical informatics fellowship. No details about these fellowships have been released, other than their proposed requirements in the ABMS proposal that was also published in JAMIA in 2009, and a statement by ABPM that such fellowships will be required to be 24 months (two years) in duration. I have previously raised some concerns about what these fellowships might look like, how they will be funded by healthcare organizations, and what will be the ramifications for the way many physicians train in informatics now, which is through graduate programs, often online. A lack of flexibility in these fellowships could limit clinical informatics training mainly to those at the beginning of their careers, which is currently not how most physicians train in informatics.
Let me summarize the answers to the questions of (a) am I eligible in the first five years of the subspecialty, (b) can OHSU make me eligible, and (c) can OHSU help me pass the certification exam?
One of the most common email messages I receive these days is an inquiry from a physician about his or her eligibility for the clinical informatics subspecialty. I am writing this posting in part to have a link to send to people in reply to those emails. But before I go further, let me make one vital disclaimer clear: I am not the decision-maker! That will be the exclusive role of the American Board of Preventive Medicine (ABPM), which is the administrative home for the subspecialty. I can give educated guesses based on what the ABPM has said and written, but ultimately it is their decision whether or not someone is eligible.
Shortly before the end of 2012, the ABPM released a one-page document on the clinical informatics subspecialty certification exam and qualifications for eligibility. The first exam will be available during a two-week window between October 7-18, 2013, with registration opening in March. The registration process will include a determination of whether an applicant is eligible for board certification, i.e., be allowed to take the exam.
The next most common questions I am asked are (a) what educational programs at Oregon Health & Science University (OHSU) will make me eligible and (b) what educational programs at OHSU or elsewhere will best prepare me for the exam? I will address those questions after providing what I know about the primary question on eligibility.
The eligibility requirements are clearly laid out in the ABPM document, so those who want to determine if they are eligible should read them carefully. The first three requirements are relatively straight-forward. In short, they are:
- Primary certification by one of the 23 member boards of the American Board of Medical Specialties (ABMS)
- Graduate from a US, Canadian, or other medical school deemed acceptable by the ABPM
- Unrestricted license to practice medicine in the US or Canada
The first of the two pathways is the "practice pathway." Those who have been working in informatics professionally for at least 25% time during any three of the previous five years, and can have a supervisory individual attest to it, are eligible for this pathway. "Working" in informatics not only includes "practice" (i.e., being a Chief Medical Information Officer), but also teaching and research.
The second pathway is the "non-traditional fellowship," which is any informatics fellowship of 24 or more months duration deemed acceptable by ABPM. At the November 4, 2012 panel at the American Medical Informatics Association (AMIA) Annual Symposium, Dr. William Greaves of ABPM stated this would be composed of informatics educational programs that were listed in the proposal submitted to ABPM by AMIA in 2009. This list, which has not been made public by ABPM or AMIA, included programs that were funded by training grants from the National Library of Medicine (NLM) or were members of the AMIA Academic Forum at the time the proposal was submitted by AMIA to ABMS in 2009. (I can say that OHSU was definitely on the list, since we were both NLM-funded and a member of the Academic Forum at that time and still are. both). Dr. Greaves also said that ABPM would review applicants trained in other fellowships for eligibility on a case-by-case basis.
The ABPM document also states that time spent in training in informatics could be applied to the practice pathway at one-half the value of practice time. In other words, someone in an educational program for at least 50% time during the previous five years would be eligible to take the certification exam. My interpretation of what he said (remember, I do not make the rules and they may change!) is that someone in a master's degree program that involves the equivalent of one and a half years of full-time study would thus be eligible. If my interpretation is correct, this would mean that completing the Master of Biomedical Informatics (MBI) Program at OHSU within a five-year time span would make one eligible, since it requires six academic quarters of full-time study. The OHSU Graduate Certificate Program, on the other hand, which is a subset of the MBI requiring about nine months of study if done full-time, thus would not be enough. Presumably one can mix and match to achieve eligibility, i.e., with some practice and some education.
As for the common question I get about which OHSU program would make one eligible, the answer then depends on how much practice pathway eligibility one has. If one completely meets the practice pathway criteria, then how much education is a moot point; they are eligible on the basis of professional work. But for those who have not worked in the field enough to quality by the practice pathway, it likely means they would need to complete the MBI or have enough education to make up for the "shortfall" in their practice time, perhaps with a Graduate Certificate.
The second common question I get asked is, what educational programs at OHSU and elsewhere will best prepare one for the exam? This answer is also part guess, as ABPM has not released any information about the exam content beyond saying it will likely reflect the core content outline that submitted with the ABMS proposal and published in JAMIA in 2009. Last year I created a matrix that mapped each element of the core content outline to an OHSU course. The sum of OHSU courses pretty much covered the outline, but the problem is that it required 23 courses to do so. This is about 50% more than required for the MBI. (Remember that OHSU is on an academic quarter system, so the number of courses is larger than programs on a semester system.) However, it is also clear from the matrix that a relatively constrained set of courses could cover a fairly large portion of the core content outline. Furthermore, we are undertaking a process to reorganize the curriculum with an eye to creating a set of courses that will be "core" for the core content, i.e., cover a substantial portion of the outline, while still maintaining the balance that we believe is important to learn in informatics. (The core content outline is likely to be fairly similar for the certifications that emerge for other healthcare professionals and PhDs that AMIA is now planning to propose. In fact, it is not very specific to "physician" informatics, and actually provides a good overview of the critical content necessary for mastery by all who work in clinical informatics.)
I believe that the eight courses required to obtain a Graduate Certificate can be fashioned in a way to prepare one well for the certification exam. Indeed, I can also see where the content of the Graduate Certificate program could form the basis of the didactic portion of a clinical informatics fellowship (perhaps allowing the practice time and fellowship project to add enough credits to qualify one for a master's degree).
I am also sometimes asked if the 10x10 ("ten by ten") course (which is the equivalent of one course, the introductory course, in our Graduate Certificate and MBI programs), or the AMIA Board Review course I will be directing, will cover enough to enable someone to pass the exam by just taking one or both. I believe it is unlikely that these courses alone, without any other formal training, would give one enough knowledge to pass the exam (although I suspect some will try, and perhaps succeed). It should be noted that achieving a sufficient grade of the optional final exam in the OHSU 10x10 course will provide credit to those eligible for study in the OHSU Graduate Certificate or MBI programs, which gets one course under their belt and gives them a trajectory for more.
(By the way, for those who are wondering: The AMIA Board Review course details will be announced in February. The current working plan is to offer the course three times between June and September, after the ABPM registration period opens but enough in advance of the actual exam. There will likely be East Coast, Midwest, and West Coast sites for the three offerings. The courses in the first year will be all face-to-face, although online versions will be developed for subsequent years. A brief interview of me about the course from the AMIA Symposium is available, as is an interview of Dr. Greaves.
Ironically, one activity for which there is no guarantee of being adequately prepared is a traditional research-oriented fellowship, such as those funded by NLM training grants. If one's course of study in one of these fellowships includes a course of study containing a good deal of practical clinical informatics courses, then that preparation should be excellent. However, not all informatics fellowships offer such coursework, as the primary purpose of the NLM-funded fellowships is to train future researchers. There may also be individuals in these fellowships who are extremely well-trained in other areas of informatics, such as bioinformatics or imaging informatics, who will technically be eligible for certification though not really well-prepared to pass an exam focused on practical clinical informatics. Indeed, even those who have a clinically oriented but highly theoretical curriculum may not be able to pass the exam that will have a very practical and applied focus.
I should also reiterate that this eligibility process only applies to the first five years of the subspecialty. After that time, the only way to achieve eligibility for certification will be in an ACGME-accredited clinical informatics fellowship. No details about these fellowships have been released, other than their proposed requirements in the ABMS proposal that was also published in JAMIA in 2009, and a statement by ABPM that such fellowships will be required to be 24 months (two years) in duration. I have previously raised some concerns about what these fellowships might look like, how they will be funded by healthcare organizations, and what will be the ramifications for the way many physicians train in informatics now, which is through graduate programs, often online. A lack of flexibility in these fellowships could limit clinical informatics training mainly to those at the beginning of their careers, which is currently not how most physicians train in informatics.
Let me summarize the answers to the questions of (a) am I eligible in the first five years of the subspecialty, (b) can OHSU make me eligible, and (c) can OHSU help me pass the certification exam?
- Any US or Canadian physician who has a primary board, has a license to practice medicine, and was education in an acceptable medical school is eligible.
- Further eligibility is required by either having "practiced" informatics for at least one-quarter time in three out of the last five years or who has completed an informatics fellowship at a to-be-released list of institutions.
- Although the rules are not clear, educational time in an approved informatics program will count at one-half the value of practice time, i.e., having been in an educational for at least one-half time in the last five years. This may allow those who have insufficient practice time to obtain eligibility through educational programs. (For those with no practice time at all, I interpret this to mean one could be eligible through the OHSU master's program but not the Graduate Certificate Program.)
- The curriculum of the OHSU Graduate Certificate Program as it now stands can be tailored to cover a substantial fraction of the core content likely to be on the exam, and will be reorganized in the next 1-2 years to allow it to do this even more efficiently.
- The 10x10 and AMIA Board Review courses are unlikely to enable one with no other formal training in informatics to pass the exam (though anyone is able to try!).
Saturday, December 29, 2012
Annual Reflections at the End of 2012
It has been somewhat of a tradition for me with this blog to post a reflection at the end of each year. I did so in 2009, 2010, and 2011. So here goes for 2012.
I am pleased at the success of this blog, closing in on 90,000 page views and having 315 followers (as I write this). Of course, the success is not really a matter of page views and followers, but rather the substantive knowledge and insights that the blog contributes. To that end, I am pleased that others re-post, tweet, and/or respond to some of my postings. This also reinforces my decision to not post every train of thought, but rather to only do so when I believe I have something important to say. This is my 40th post of 2012. That is the most postings for any of my four years, but still reflecting my decision only to write when I have something I consider meaningful to say, and not just posting because I feel like I must on a given day.
This past year has been another successful one for myself and the field. Much of it, of course, revolves around work related to the HITECH Act, which has defined the last few years of our informatics careers for many of us in the field. The coming year will mark a transformation, as least for me, as the major ONC projects that have consumed much of our time and thinking will be drawing to a close. The curriculum project is already winding down, as we transition from a development role to a support role, with ONC funding slated to cease at the end of March. As noted in a recent update on the project, I do hope there will be a way to sustain the updating and enhancement of the curriculum. At this time, however, there is no funding source that has been identified to do that. By the same token, the University-Based Training (UBT) program is also starting to wind down. While we will have students wending their way through their UBT program studies for most of 2013, the funding for this project will be completed by next year at this time.
Of course, I did accomplish a great deal more in 2012 than just efforts related to HITECH. The department I lead at Oregon Health & Science University (OHSU), the Department of Medical Informatics & Clinical Epidemiology, continues to thrive, with the renewal of our evidence-based practice center as well as several new informatics-related grants and projects. Student enrollment in our informatics educational program above and beyond UBT remains strong. I am grateful to have a position that allows me to provide leadership and vision while still enabling me to pursue my own intellectual activities in teaching and research.
On a personal level, this year saw a number of accomplishments. Here are some of the accolades and achievements of 2012:
- Served as General Conference Chair of the ACM SIGIR 2012 Conference
- Served as Scientific Program Committee Chair of the AMIA 2012 Annual Symposium
- Named as Course Director for the AMIA Clinical Informatics Subspecialty Board Review Course
- Named among the Top 25 Clinical Informaticists by Modern Healthcare magazine for the third year in a row
- Named an "EHR Game Changer" by Health Data Management magazine (slide show and article)
- Appointed to the Workforce Subgroup of the Certification and Adoption Workgroup of the ONC Health IT Policy Committee
- And, on a lighter note, named as a Fellow of the American College of Medical Informatimusicology (ACMImimi)
Let me close this posting by noting that anyone and everyone can also contribute to the Department of Medical Informatics & Clinical Epidemiology. We launched a major philanthropic drive this year, and any support you are willing to give will greatly help our current and future programs. I hope you will consider a gift to our program on our department giving page.
In the meantime, I look forward to another exciting year for informatics and this blog in 2013, and wish everyone a healthy and happy New Year.
Saturday, December 22, 2012
Knowns and Unknowns About the Health IT Workforce
I have had the opportunity over the last few months to serve on a Federal Advisory Committee (FACA) workgroup focused on health information technology (HIT) workforce policy recommendations for one of the two main HIT advisory committees to the Office of the National Coordinator for Health IT. It has been a pleasure to interact with a diverse cross-section of people who have interest in workforce issues.
One of my pleas to the group has been to seek more data to inform our (at this point potential) policy prescriptions. I am a person who prefers to make decisions based on data. I know that we cannot always get the data we need or want to make a decision, and sometimes we are not able to get data at all. But everything else being equal, I prefer to have my decisions driven by facts that have some basis in evidence.
We are starting to some additional data emerge. Led by Dr. Susan Fenton, the state of Texas has carried out a series of focus groups of HIT employers in the state. The larger report of this work was recently distilled down to a journal article (Fenton S, Joost E, Gongora-Ferraez M. Health information technology knowledge and skills needed by HIT employers. Applied Clinical Informatics. 2012;3:448-61.). The emerging results present some interesting conclusions:
- Employers report needing a diversity of skills and knowledge
- The usual adage of needing knowledge of healthcare seems to be increasingly complemented by the need to understand and know how to work with data
- In additional to skills and knowledge, ability to think critically and solve problems is key
- There is no consensus on optimal career paths
The second point is borne out in some additional data that was presented to the group by Norma Morganti, another member of the workgroup who also directs the Midwest Community College Health Information Technology Consortium, one of the five ONC Community College Consortium programs. She convened a focus group in Ohio to ascertain competencies for the workforce involved in HIT aspects of health system transformation, particularly the primary care medical home. Among the critical competencies required are those requiring population management and data analytics.
These data support a shift we are noticing in our own program for skills of our graduates. While knowledge of healthcare is still key, there seems to be a growing preference among those who hire our graduates for those with strong data skills and understanding. This really is not surprising, given the shift I described earlier this year in the focus of the work of informatics from implementation (many healthcare organizations have already done that) to analytics (now they need to figure out what to do with the data).
It will be interesting to see how this trend evolves. I am fairly certain (though as always, will change my views if supported by the data!) that there will be a growing need among informatics professionals to know how to make use of data to improve health, healthcare, research, etc.. After all, that is the primary of purpose of informatics, to make use of data and information to improve all aspects of human health.
Saturday, December 15, 2012
I Am a Runner
Every now and then, I take a break from informatics in my blog to write about other things. Let me take a detour in this posting and talk about running.
I am one of those people who is a runner. Running has been an important part of my life. I am not a fanatic, and keep running in the context of the rest of my life, but I one of those people who runs regularly no matter where I am or what the weather is, and feels uneasy when I am not able to do so.
Running is connected to major parts of my life. One is my devotion to personal health. Running keeps my heart strong, my weight under control, and my appetite in check. I can't claim that running will make me immortal, and as a physician I know that sometimes people get illnesses that have nothing to do with their personal health habits.
Do I run to extend to my life? I believe that running will probably extend my life, but I cannot be sure. And even if it did not, I would still run. Along with healthy eating, running makes me feel good today. Regular exercise and healthy eating give me more energy in my daily function, here and now.
There is some growing scientific evidence as to what is the proper amount of running. A recent paper by O'Keefe and Lavie, summarizing research they and others have done, was published Online First in the journal Heart [O'Keefe, J. and C. Lavie (2012). Run for your life ... at a comfortable speed and not too far. doi:10.1136/heartjnl-2012-302886]. The evidence seems to show a U-shaped phenomenon, where all-cause mortality is lowest at a running distance of 10-20 miles per week. Mortality goes up both for those who do not exercise as well as those who do so excessively. I am pleased to fall into the category of distance with the lowest mortality, but again I do not run primarily to extend my life.
Does running help my personal discipline? Perhaps not as much now as earlier in my life, but there is no question that my breaking out from being an underachiever in my early part of high school was due in part to the discipline I developed from running. That discipline certainly played a role in my future career and other life accomplishments.
Another aspect to running I enjoy is its "portability." That is, one can run anywhere. As someone who travels quite a bit, I enjoy being able to run. In fact, one of the enjoyments is getting to see parts of places I travel to around the world that I might not ordinarily see if I were just attending conferences or visiting the tourist sites. I have run on six continents (one of these days will do Antarctica!) and in countries as diverse as Cuba, Zimbabwe, Thailand, Slovenia, Egypt, and Brazil.
An additional fun aspect to my running in recent years has been my acquisition of a Garmin global positioning system (GPS) watch, which enables me to track my distance and speed as well as plot my route after a run (or bike ride) on a Google or Bing map. I do not track all of my runs, but I enjoy being able to see where I have run, especially when traveling in distant places. These include Buenos Aires, Argentina; Singapore; San Francisco; Washington, DC; my old high school (New Trier West) area; Honolulu; Oaxaca, Mexico; Bangkok, Thailand; Gabarone, Botswana; and Cape Town, South Africa (among others!).
Of course, the GPS watch is the only technology I use while running, which gets to another advantage of my running time. This is the ability to disconnect from all my other devices - computers, tablets, smartphones, etc. - and have some time for solitary thinking. Sometimes I come up with my best ideas for research, teaching, or even things unrelated to work while running (including many of the thoughts in this blog entry).
A common adage among sports medicine physicians is that runners don't know when to stop. I do usually stop when I get sick (e.g., a cold or the flu) or injured. In fact, my running has been somewhat impacted over the last couple years by some knee problems. Fortunately, the pain is not exacerbated by running (in fact, running and movement tend to make it feel better), but it has led me to reduce my mileage somewhat. I no longer run races, though occasionally like to participate for the thrill of doing so.
I will continue to run for as long as I can in my life. I believe it is contributing to my health and well-being, not only in the future but most importantly in the present.
I am one of those people who is a runner. Running has been an important part of my life. I am not a fanatic, and keep running in the context of the rest of my life, but I one of those people who runs regularly no matter where I am or what the weather is, and feels uneasy when I am not able to do so.
Running is connected to major parts of my life. One is my devotion to personal health. Running keeps my heart strong, my weight under control, and my appetite in check. I can't claim that running will make me immortal, and as a physician I know that sometimes people get illnesses that have nothing to do with their personal health habits.
Do I run to extend to my life? I believe that running will probably extend my life, but I cannot be sure. And even if it did not, I would still run. Along with healthy eating, running makes me feel good today. Regular exercise and healthy eating give me more energy in my daily function, here and now.
There is some growing scientific evidence as to what is the proper amount of running. A recent paper by O'Keefe and Lavie, summarizing research they and others have done, was published Online First in the journal Heart [O'Keefe, J. and C. Lavie (2012). Run for your life ... at a comfortable speed and not too far. doi:10.1136/heartjnl-2012-302886]. The evidence seems to show a U-shaped phenomenon, where all-cause mortality is lowest at a running distance of 10-20 miles per week. Mortality goes up both for those who do not exercise as well as those who do so excessively. I am pleased to fall into the category of distance with the lowest mortality, but again I do not run primarily to extend my life.
Does running help my personal discipline? Perhaps not as much now as earlier in my life, but there is no question that my breaking out from being an underachiever in my early part of high school was due in part to the discipline I developed from running. That discipline certainly played a role in my future career and other life accomplishments.
Another aspect to running I enjoy is its "portability." That is, one can run anywhere. As someone who travels quite a bit, I enjoy being able to run. In fact, one of the enjoyments is getting to see parts of places I travel to around the world that I might not ordinarily see if I were just attending conferences or visiting the tourist sites. I have run on six continents (one of these days will do Antarctica!) and in countries as diverse as Cuba, Zimbabwe, Thailand, Slovenia, Egypt, and Brazil.
An additional fun aspect to my running in recent years has been my acquisition of a Garmin global positioning system (GPS) watch, which enables me to track my distance and speed as well as plot my route after a run (or bike ride) on a Google or Bing map. I do not track all of my runs, but I enjoy being able to see where I have run, especially when traveling in distant places. These include Buenos Aires, Argentina; Singapore; San Francisco; Washington, DC; my old high school (New Trier West) area; Honolulu; Oaxaca, Mexico; Bangkok, Thailand; Gabarone, Botswana; and Cape Town, South Africa (among others!).
Of course, the GPS watch is the only technology I use while running, which gets to another advantage of my running time. This is the ability to disconnect from all my other devices - computers, tablets, smartphones, etc. - and have some time for solitary thinking. Sometimes I come up with my best ideas for research, teaching, or even things unrelated to work while running (including many of the thoughts in this blog entry).
A common adage among sports medicine physicians is that runners don't know when to stop. I do usually stop when I get sick (e.g., a cold or the flu) or injured. In fact, my running has been somewhat impacted over the last couple years by some knee problems. Fortunately, the pain is not exacerbated by running (in fact, running and movement tend to make it feel better), but it has led me to reduce my mileage somewhat. I no longer run races, though occasionally like to participate for the thrill of doing so.
I will continue to run for as long as I can in my life. I believe it is contributing to my health and well-being, not only in the future but most importantly in the present.
Friday, December 14, 2012
If MOOCs Are the Answer, What is the Question?
I have written about massive open online courses (MOOCs) before, once wondering whether disruptive innovation was finally coming to higher education and then further noting that a colleague was creating a MOOC from the materials of the ONC Health IT Curriculum. (I am pleased to report that the MOOC is making good progress.) MOOCs also have attained quite a bit of discussion as part or all of the solution to the problem of runaway costs of higher education in the United States. The New York Times has called this year the Year of the MOOC, while others wonder if this is finally the time that Silicon Valley-style disruptive innovation will come to higher education.
I have skin in this game in a number of ways. One is that I direct a large graduate program in a public health science university that has minimal government financial support, i.e., the program is mostly dependent on tuition, training grants, and other sources of funding. This graduate program is in an academic department that I chair that is likewise being asked to achieve increasing fiscal self-sufficiency in all its activities. I am also reaching the end of a well-funded project to develop a health information technology (HIT) curriculum for colleges and universities. In addition, I am the parent of two children, one of whom recently completed a bachelor's degree and the other who is still in undergraduate studies but planning further education beyond her bachelor's degree, both in public state universities. And of course, I am a US citizen concerned about my country's long-term fiscal solvency while maintaining economic competitiveness through a highly educated populace.
To some, MOOCs are seen as a way to reduce the costs of higher education, which is under increasing scrutiny to demonstrate its value. Based on my own experience with distance learning, I am optimistic that online education can be efficient and scalable. Although I do not find myself in agreement with many of the political positions of Texas Gov. Rick Perry, I admit to having sympathy for his challenge to higher education to create a $10,000 bachelor's degree.
That said, I recognize that online courses alone do not an education make, especially a college education. College is also about maturation, participating in non-academic activities, and developing skills beyond just mastering of knowledge, such as leadership, mentorship, volunteerism, and more. I have no doubt that MOOCs can replace the kind of large lecture classes I took as an undergraduate at the University of Illinois, i.e., the "101" classes. But I am less convinced they can replace the smaller courses, the hands-on experiences, the volunteer activities, and so forth.
As enthusiastic as I am about the use of educational technology, I do not see online courses alone comprising the entire educational experience. Even in our online graduate program, we encourage networking and participation in professional organizations among our students. We have created a practicum and internship program that allows even our remote students to get real-world experience. A "distance education" in our program is not just a succession of online courses. Our students are engaged in a virtual community with us.
At the same time, I also worry that low-cost college education may create a two-class system, one of children of parents with the means to afford a four-year in-residence college education and all of its benefits, and the other of students whose college experience is mostly impersonal. I believe we need a balance.
Another interesting aspect about MOOCs and other online repositories of educational materials is the notion of "openness." I was prodded into thinking about this by some from the Office of the National Coordinator for Health IT (ONC) who want to see the curriculum be maintained in some open, perhaps crowdsourcing, project. This made me realize that MOOCs and similar initiatives are open in the sense that they are accessible to many people. But the openness is only one-way, i.e., the rest of the world cannot alter the "open" materials.
That is not necessarily a bad thing. Phenomena like Wikipedia not withstanding, I believe there is a role for materials that have authorship and authority. The Web facilitates their annotation, but not their underlying alteration. Even Wikipedia and the myriad of open-source software projects have found a need for governance. I relish the idea of everyone in the world annotating the ONC HIT curriculum, but I am less enthusiastic about everyone in the world updating the source materials.
Notwithstanding my concerns, I am excited to play a small role in the disruptive innovation of higher education through my own work. But I also know that MOOCs are not the complete solution. I envision a future where students are wedded to an educational institution, but have the flexibility of online learning and the ability to have some of their learning come from other teachers and institutions. Perhaps that is why initiatives like Semester Online, where ten universities are sharing courses among each other, with appropriate transfers of academic credit and tuition money, will survive if MOOCs turn out to be a passing fad. We can probably learn from systems like the European Credit Transfer System (ETCS), which standardizes credits for higher education and allows their transfer across educational institutions.
I hope we can achieve a happy middle ground of making the best use of the dissemination and collaboration afforded by the Internet while still recognizing the value attachment to a real institution of higher learning. I also believe the cost of higher education can be reduced, but as former Harvard President Derek Bok used to say, If you think education is expensive, try ignorance.
I have skin in this game in a number of ways. One is that I direct a large graduate program in a public health science university that has minimal government financial support, i.e., the program is mostly dependent on tuition, training grants, and other sources of funding. This graduate program is in an academic department that I chair that is likewise being asked to achieve increasing fiscal self-sufficiency in all its activities. I am also reaching the end of a well-funded project to develop a health information technology (HIT) curriculum for colleges and universities. In addition, I am the parent of two children, one of whom recently completed a bachelor's degree and the other who is still in undergraduate studies but planning further education beyond her bachelor's degree, both in public state universities. And of course, I am a US citizen concerned about my country's long-term fiscal solvency while maintaining economic competitiveness through a highly educated populace.
To some, MOOCs are seen as a way to reduce the costs of higher education, which is under increasing scrutiny to demonstrate its value. Based on my own experience with distance learning, I am optimistic that online education can be efficient and scalable. Although I do not find myself in agreement with many of the political positions of Texas Gov. Rick Perry, I admit to having sympathy for his challenge to higher education to create a $10,000 bachelor's degree.
That said, I recognize that online courses alone do not an education make, especially a college education. College is also about maturation, participating in non-academic activities, and developing skills beyond just mastering of knowledge, such as leadership, mentorship, volunteerism, and more. I have no doubt that MOOCs can replace the kind of large lecture classes I took as an undergraduate at the University of Illinois, i.e., the "101" classes. But I am less convinced they can replace the smaller courses, the hands-on experiences, the volunteer activities, and so forth.
As enthusiastic as I am about the use of educational technology, I do not see online courses alone comprising the entire educational experience. Even in our online graduate program, we encourage networking and participation in professional organizations among our students. We have created a practicum and internship program that allows even our remote students to get real-world experience. A "distance education" in our program is not just a succession of online courses. Our students are engaged in a virtual community with us.
At the same time, I also worry that low-cost college education may create a two-class system, one of children of parents with the means to afford a four-year in-residence college education and all of its benefits, and the other of students whose college experience is mostly impersonal. I believe we need a balance.
Another interesting aspect about MOOCs and other online repositories of educational materials is the notion of "openness." I was prodded into thinking about this by some from the Office of the National Coordinator for Health IT (ONC) who want to see the curriculum be maintained in some open, perhaps crowdsourcing, project. This made me realize that MOOCs and similar initiatives are open in the sense that they are accessible to many people. But the openness is only one-way, i.e., the rest of the world cannot alter the "open" materials.
That is not necessarily a bad thing. Phenomena like Wikipedia not withstanding, I believe there is a role for materials that have authorship and authority. The Web facilitates their annotation, but not their underlying alteration. Even Wikipedia and the myriad of open-source software projects have found a need for governance. I relish the idea of everyone in the world annotating the ONC HIT curriculum, but I am less enthusiastic about everyone in the world updating the source materials.
Notwithstanding my concerns, I am excited to play a small role in the disruptive innovation of higher education through my own work. But I also know that MOOCs are not the complete solution. I envision a future where students are wedded to an educational institution, but have the flexibility of online learning and the ability to have some of their learning come from other teachers and institutions. Perhaps that is why initiatives like Semester Online, where ten universities are sharing courses among each other, with appropriate transfers of academic credit and tuition money, will survive if MOOCs turn out to be a passing fad. We can probably learn from systems like the European Credit Transfer System (ETCS), which standardizes credits for higher education and allows their transfer across educational institutions.
I hope we can achieve a happy middle ground of making the best use of the dissemination and collaboration afforded by the Internet while still recognizing the value attachment to a real institution of higher learning. I also believe the cost of higher education can be reduced, but as former Harvard President Derek Bok used to say, If you think education is expensive, try ignorance.
Tuesday, December 11, 2012
Update on the ONC Health IT Curriculum
In my previous posting, I noted the end was in sight for the Oregon Health & Science University (OHSU) University-Based Training Grant (UBT) grant from the Office of the National Coordinator for Health IT (ONC). This is even more so the case for our other major ONC workforce project, the ONC Curriculum Development Centers (CDCs) program, as the ONC funding portion of this project will end in March, 2013. This program has produced three successive versions of the health information technology (HIT) curriculum for community college programs, and it has had broad use beyond those programs, including our own informatics graduate program at OHSU.
Since the first version of the curriculum, the materials have been available for download from the National Training and Dissemination Center (NTDC) web site. Since the second version, anyone in the world has been able to create a login and download up to the entire curriculum. Technical support has been available for the faculty of the community college consortia.
Even though the ONC funding will be ending, the materials will live on. While there is no funding as of now to support and update the materials, options are being explored. In the meantime, the NTDC web site will be maintained by OHSU for at least one year after the ONC funding ends. In addition, ONC plans to incorporate the materials into its developing National Learning Consortium. Others have moved the materials on to other sites, including one that is turning them into a massive open online course (MOOC).
As the ONC funding is winding down, we are producing the final deliverables of the project. The major last deliverable we are most excited about is a new version of the VistA for Education (VFE) environment that is used as a fully function electronic health record (EHR) for the lab-based components of the curriculum. Based on the VistA EHR of the US Veteran's Administration (VA), the learning curriculum provides both learning to use as well as configure the VistA system for computers running the Microsoft Windows operating system. One of the limitations of this EHR has been the use of a version that requires the commercial Intersystems Cache environment (as is done in operational settings by the VA itself). A new version of VFE has been developed that is based on the open-source GT.M environment (with help from the WorldVistA community). As GT.M runs only in the Linux environment, we have had to develop a virtual machine approach in order for VFE to run on Windows. But we have succeeded at developing this, including an installer that takes the user through all the required steps. The exercises based on VistA from Components 7 and 11 of the curriculum have been revised to run properly with this new version of VistA.
Other project-end deliverables include updating of various materials, including revising the last unit of Component 1 to include coverage of Stage 2 of meaningful use. We were planning to update the materials for the new HIPAA regulations that were supposed to be released in 2012, but those have not come out yet, so will not be in this version of the curriculum.
Overall, the reception of the materials has been gratifying. The NTDC download site has nearly 10,000 registered users. We are currently conducting a survey of those users that we hope to publish in the near future.
Clearly these materials represent a highly worthwhile product funded by the American Recovery and Reinvestment Act (ARRA). Even if they do become out of date, they will continue to be directly useful for some time to come, and serve as a foundation for other educators to develop their own curricular materials using ours as a foundation for some time to come.
Since the first version of the curriculum, the materials have been available for download from the National Training and Dissemination Center (NTDC) web site. Since the second version, anyone in the world has been able to create a login and download up to the entire curriculum. Technical support has been available for the faculty of the community college consortia.
Even though the ONC funding will be ending, the materials will live on. While there is no funding as of now to support and update the materials, options are being explored. In the meantime, the NTDC web site will be maintained by OHSU for at least one year after the ONC funding ends. In addition, ONC plans to incorporate the materials into its developing National Learning Consortium. Others have moved the materials on to other sites, including one that is turning them into a massive open online course (MOOC).
As the ONC funding is winding down, we are producing the final deliverables of the project. The major last deliverable we are most excited about is a new version of the VistA for Education (VFE) environment that is used as a fully function electronic health record (EHR) for the lab-based components of the curriculum. Based on the VistA EHR of the US Veteran's Administration (VA), the learning curriculum provides both learning to use as well as configure the VistA system for computers running the Microsoft Windows operating system. One of the limitations of this EHR has been the use of a version that requires the commercial Intersystems Cache environment (as is done in operational settings by the VA itself). A new version of VFE has been developed that is based on the open-source GT.M environment (with help from the WorldVistA community). As GT.M runs only in the Linux environment, we have had to develop a virtual machine approach in order for VFE to run on Windows. But we have succeeded at developing this, including an installer that takes the user through all the required steps. The exercises based on VistA from Components 7 and 11 of the curriculum have been revised to run properly with this new version of VistA.
Other project-end deliverables include updating of various materials, including revising the last unit of Component 1 to include coverage of Stage 2 of meaningful use. We were planning to update the materials for the new HIPAA regulations that were supposed to be released in 2012, but those have not come out yet, so will not be in this version of the curriculum.
Overall, the reception of the materials has been gratifying. The NTDC download site has nearly 10,000 registered users. We are currently conducting a survey of those users that we hope to publish in the near future.
Clearly these materials represent a highly worthwhile product funded by the American Recovery and Reinvestment Act (ARRA). Even if they do become out of date, they will continue to be directly useful for some time to come, and serve as a foundation for other educators to develop their own curricular materials using ours as a foundation for some time to come.
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