Sunday, April 16, 2017

Participating in the March for Science

I plan to participate in the March for Science in Portland on April 22nd. I did not come to this decision lightly. This was different, for example, from my decision to participate in the Women’s March in January. That was a very easy decision to make based on my political views.

But science is more than just politics to me. It is of course my livelihood, as I am a faculty at Oregon Health & Science University (OHSU) and one whose work is supported by public funds. Science is also, however, the dispassionate pursuit - to the best of human ability - for discerning truth. As such, I do not want to see science subverted for political or other aims. I also want to be careful that others do not subvert the message of the march itself, which in my view is to inform people about the value of public support and taxpayer funding of science.

I am pleased that many organizations have reached similar decisions. The American Association for the Advancement of Science (AAAS) has endorsed the march, and I agree with their statement that the march is a "nonpartisan set of activities that aim to promote science education and the use of scientific evidence to inform policy." I am pleased that OHSU supports the march as well.

In the end, my concerns about the real threats to science outweighed my worries about science being subverted by politics. I consider the threats to science by the current political leadership of the US to be significant. I do not consider science to be a partisan subject. I cannot look at climate change, gun violence, or immunization-preventable diseases and state that research about them is driven by an ideological or political agenda. Yes, science is always full of disagreement and is never truly “settled,” but there are bounds of truth and there is always a need to probe even what we believe to be true further. One of the beauties of science and its dispassionate search for answers is that it is self-correcting. So when science gets something wrong, there is a good likelihood that it will be corrected by further research.

Of course, I also recognize that the public purse to fund science, or anything else that the government funds, is not unlimited. That is why we have a political process to debate and enact appropriate amounts of taxation and public spending. It is also important to remember that basic research funded by the government is not research that would be funded by private industry. In fact, industry well knows it benefits from the basic research that enables companies to develop profitable products.

I also believe that the scientific enterprise in the US is a very efficient and effective allocation of tax dollars. The National Institutes of Health (NIH) budget of about $30 billion is about 1% of overall federal spending. Federal research grants not only fund scientific research but also education and training for the next generations of scientists, clinicians, and others. NIH funding is mostly awarded through highly competitive funding opportunities that often times only have success rates of 10-15%. Despite what detractors says, the life of writing grant proposals is not a cushy one.

Even beyond the research itself, the money spent on scientific research brings money back to communities. When a faculty like myself is awarded a grant, that money not only advances research and education, but it creates jobs for people in the local community. In turn, all of those who are funded by the grant turn around and spend money in grocery stores, restaurants, and other local businesses. And of course the reality is that if my institution and state and local governments were not funded by this money, it would end up in other states. OHSU commissioned a study about five years ago showing a multiplier effect in terms of money spent on the institution having an impact back on the local economy.

Although this march is about science generally, I hope that some other points specific to biomedical research come across. As pointed out by OHSU leadership, an abrupt cut in funding, such as the 18% cut to NIH proposed by the Trump Administration, will have outsized impact due to the fact that most NIH grants are multi-year awards. This means that only a portion of a given year’s funding goes to new projects. An abrupt cut will mean that for the first year of the big cuts, very few new grants would be able to be awarded. Given how competitive the environment for funding already is, we stand to lose the momentum of both established and emerging scientists.

I also hope that another point that comes out is the misguided plan to in essence eliminate the Agency for Healthcare Research & Quality (AHRQ). While it will ostensibly be folded into NIH (AHRQ currently exists within the Department of Health and Human Services but outside NIH), the claim that its research is duplicated by other NIH entities is simply not true. AHRQ performs critical and novel research in under-researched areas of health and healthcare, such as patient safety, healthcare quality, and evidence-based medicine. As with other basic research, industry may benefit and even develop products from this research, the basic research is too far removed from their product cycle for them to want to fund it. As this is not the first time that efforts have been made to de-fund AHRQ, I have written about the value of AHRQ before.

I look forward to participating in the March for Science and advocating for the benefits of scientific research and its funding by the federal government. I hope the outpouring of support will education the downside to neglecting basic scientific research and the importance of training new scientists.

Sunday, February 19, 2017

Big Change, Little Change: OHSU Biomedical Informatics Graduate Program Renames Tracks

The Oregon Health & Science University (OHSU) Biomedical Informatics Graduate Program is renaming the two tracks of its program. While the changes to the names of the tracks are small, they reflect the big changes in the field and evolving content of the curriculum.

Since 2006, the program has had two “tracks,” which have been called Clinical Informatics (CI) and Bioinformatics & Computational Biology (BCB). These two pathways through the program have been called “tracks” because they represent two different foci within the larger field of biomedical informatics, which is the discipline that acquires, organizes, and uses data, information, and knowledge to advance health-related sciences. Historically, the differences between the tracks represented their informatics focus, in particular people, populations, and healthcare (clinical informatics) vs. cellular and molecular biology, genomics, and imaging (bioinformatics).

In recent years, however, these distinctions have blurred as “omics” science has worked its way into clinical medicine. At the same time, health, healthcare, and public health have become much more data-driven, due in no small part to the large-scale adoption of electronic health records. As such, the two tracks have begun to represent different but still distinct foci, mostly in their depth of quantitative methods (deep vs. applied) but also in coverage of other topics (e.g., system implementation, especially in complex health environments; usability; and clinical data quality and standards).

The program believes that both tracks possess a set of common competencies at a high level that reflect the essential knowledge and skills of individuals who work in biomedical informatics. The curriculum organizes these competencies into “domains,” which are groups of required and elective courses that comprise the core curriculum of each track. To reflect the evolution of the program, the program has renamed the BCB track to Bioinformatics and Computational Biomedicine (still abbreviated BCB) and the CI track to Health and Clinical Informatics (now to be abbreviated HCI). The table lists below lists the common competencies and the names of the domains for each track. Each of the domains contains required courses, individual competency courses (where students are required to select a certain number of courses from a larger list, which used to be called “k of n” courses), and elective courses.

The program will continue the overall structure of the curriculum with the “knowledge base” that represents the core curriculum of the master’s degree and the base curriculum for advanced study in the PhD program. A thesis or capstone is added to the knowledge base to qualify for the MS or MBI (latter in the HCI Track only) degrees, respectively. Additional courses are required for the PhD, ultimately culminating in a dissertation.

The materials and Web site for the program will be updated quickly to reflect the new names. The program will also be evolving course content as well as introducing new courses to reflect the foci of the new tracks. The program still fundamentally aims to train future researchers and leaders in the field of biomedical informatics.

Wednesday, February 1, 2017

A New Textbook on Health Systems Science

Many aspects of academic medicine, from the structure of the medical school curriculum to the organization of departments in Schools of Medicine, are neatly segregated into two buckets: basic science and clinical science. In the jargon of medical schools and education, basic science refers to the basic biomedical sciences that have traditionally been taught in the first two years of medical school, such as anatomy, physiology, biochemistry, and pharmacology. While plenty of clinical material has migrated into the first two years of medical school over the years, such as learning to interact professionally with patients and perform a physical examination, the main focus of the first half of medical school has historically been on basic science, culminating in the US Medical Education Licensure Examination (USMLE) Step 1 exam.

Once students finish their basic science years, they move on to the clinical sciences, where they begin rotations, also called clerkships or clinical experiences. They usually first rotate through the core medical specialties, i.e., internal medicine, surgery, pediatrics, obstetrics/gynecology, and psychiatry. This is then followed by rotations in other specialties and subspecialties, ultimately leading to graduation and the start of their residency training.

This division of medical education goes beyond just the medical school curriculum. The organizational structure in most medical schools is to group academic departments into basic science and clinical departments. These two types of departments usually have different funding models. Basic science departments are usually funded by base budgets for teaching and grants for research, with an expectation that just about all faculty have research grant funding. Clinical departments have base budgets and research programs as well, but they perform another activity, which is clinical care that provides practice opportunities (and revenues) for faculty and learning experiences for students, residents, and fellows. In many clinical departments in medical schools, research activity is modest and may be partially subsidized by the margins from clinical revenues.

The focus on these two groups of sciences takes the perspective of the physician taking care of a single patient, i.e., applying the best biomedical science through the lens of a specific clinical specialty. However, despite its primacy, there is more to the practice of medicine than taking care of single patients. Physicians and other clinicians work in a healthcare system that has other concerns, such as continually increasing costs, worries about patient safety, and questions about the quality of care delivered. As such, 21st century clinicians must be competent in more than the diagnosis and treatment of disease in individual patients. This has led to emergence of the notion of a “third science” of medicine, which focuses on how to optimally provide healthcare for patients and populations. While some describe this as “healthcare delivery science” (my preference) or “implementation science,” the emerging name, as given to a textbook in this area, is now “health systems science.”

The textbook is published by the American Medical Association (AMA), which has been supporting innovation in medical education through its Accelerating Change in Education (ACE) consortium, funded by grants to medical schools [1]. OHSU was one of the original grantees in this program to establish “medical schools of the future.” I have been pleased that one outcome of this program has been the expansion of instruction in clinical informatics for medical students, which I consider to be an essential competency for 21st century physicians [2].

The titles of the chapters of the new textbook describe the important topics covered by health systems science:
  1. Health Systems Science in Medical Education
  2. What Is Health Systems Science? Building an Integrated Vision
  3. The Health Care Delivery System
  4. Value in Health Care
  5. Patient Safety
  6. Quality Improvement
  7. Principles of Teamwork and Team Science
  8. Leadership in Health Care
  9. Clinical Informatics
  10. Population Health
  11. Socio-Ecologic Determinants of Health
  12. Health Care Policy and Economics
  13. Application of Foundational Skills to Health Systems Science
  14. The Use of Assessment to Support Learning and Improvement in Health Systems Science
  15. The Future of Health Systems Science
I am delighted myself to be the lead author of one of the chapters, not surprisingly the one on clinical informatics [3]. I hope this chapter will introduce many new generations of medical and other health professions students to the informatics field and its role in healthcare delivery. Of course, informatics plays many roles beyond healthcare delivery, such as informing the care of individual patients and facilitating all types of research, but the effective use of data and informatics is a key aspect of health systems science.

I hope that this new textbook will lead the way in emphasizing the importance of health systems science in the work of physicians and other healthcare professionals. Clinicians have long known that diagnosing and treating disease, while the centerpiece of medical practice, cannot be carried out in a vacuum outside the realm of the patient’s and larger health system’s context. The care delivered to those individual patients will be better if the clinician has the perspective of that larger system.


1. Skochelak, SE, Hawkins, RE, et al., Eds. (2017). Health Systems Science. New York, NY, Elsevier.
2. Hersh, WR, Gorman, PN, et al. (2014). Beyond information retrieval and EHR use: competencies in clinical informatics for medical education. Advances in Medical Education and Practice. 5: 205-212.
3. Hersh, W and Ehrenfeld, J (2017). Clinical Informatics. in Health Systems Science. S. Skochelak, R. Hawkins, L. Lawson et al. New York, NY, Elsevier: 105-116.

Sunday, January 22, 2017

Response to Request for Information (RFI): Strategic Plan for the National Library of Medicine, National Institutes of Health

Under the leadership of its new Director, Patricia Brennan, PhD, RN, the National Library of Medicine (NLM) is undertaking a strategic planning process to develop goals and priorities for the NLM going forward. This process builds on a Request for Information (RFI) in 2015 from the NLM Working Group of the Advisory Committee to the National Institutes of Health (NIH) Director (ACD) to obtain input for a report on a vision for the future of NLM in the context of NLM’s leadership transition and emerging NIH data science priorities. The report was released in 2015. I posted to this blog both the comments that I submitted for the report as well as an overview of the report after it was published.

The new RFI asks for comments on four themes:
  1. Role of NLM in advancing data science, open science, and biomedical informatics
  2. Role of NLM in advancing biomedical discovery and translational science
  3. Role of NLM in supporting the public’s health: clinical systems, public health systems and services, and personal health
  4. Role of NLM in building collections to support discovery and health in the 21st century
For each theme, respondents are asked to:
  1. Identify what you consider an audacious goal in this area – a challenge that may be daunting but would represent a huge leap forward were it to be achieved
  2. The most important thing NLM does in this area, from your perspective
  3. Research areas that are most critical for NLM to conduct or support
  4. Other comments, suggestions, or considerations, keeping in mind that the aim is to build the NLM of the future
In the remainder of this post, I will provide the comments I submitted to the RFI. I chose to limit my comments to the first of the four themes because the role of NLM is to advance the other themes – discovery, translation, and the public’s health – in the context of the first theme – namely the field of biomedical informatics, and data/open science within it.

a. Identify what you consider an audacious goal in this area – a challenge that may be daunting but would represent a huge leap forward were it to be achieved. Include input on the barriers to and benefits of achieving the goal.

I have chosen to focus my comments on the first of the four themes because the role of NLM is to advance the other themes – discovery, translation, and the public’s health – by advancing the first theme – namely the field of biomedical informatics, and data/open science within it. Therefore, the most audacious goal for all of NLM is to build and sustain the infrastructure of biomedical informatics, i.e., the people, technology, and resources to advance discovery, translation, and the public’s health.

Biomedical informatics must leverage both achievements that are new, such as digital and networking technologies, as well as goals that are enduring, such as improving individual health, healthcare, public health, and research. The NLM must promote, educate about, and fund biomedical informatics and related disciplines to the appropriate level they deserve in relation to the larger biomedical research enterprise. While research in domain-specific areas (e.g., cancer, cardiovascular, mental health) is important, biomedical informatics can provide fundamental tools to advance science in all domain-specific areas. To achieve this, we still need basic research in biomedical informatics itself, improving our knowledge and tools in many areas, including but not limited to human-computer interaction, natural language understanding, standards and interoperability, data quality, the intersection of people and organizational issues with information technology, workflow analysis, etc.

b. The most important thing NLM does in this area, from your perspective.

Although there are many institutes within NIH (e.g., NCI, NHLBI, and the Fogarty International Center) and other entities outside of NIH (e.g., AHRQ and PCORI) that fund research in informatics-related areas, NLM is the only entity that funds basic research in biomedical informatics. Most of the other institutes and entities that fund informatics support projects that are highly applied and/or domain-focused. These projects are important, but basic informatics research is also key to improving discovery, translation, and the public’s health.

The NLM is also unique in developing emerging technologies, some of which we cannot foresee now. When I was an NLM informatics postdoctoral fellow in the late 1980s, I could not have imagined the emergence of the World Wide Web, the wireless ubiquitous Internet, modern mobile devices, or the widespread adoption of electronic health records that we now have. There are likely new technologies coming down the road that few if any of us can predict that will have major impacts on health and healthcare. It is critical that the NLM and the research it supports enable these technologies to be put to optimal usage.

c. Research areas that are most critical for NLM to conduct or support.

Although it is critical for NLM to support research in biomedical informatics as applied to all areas of individual and public health and of healthcare and research, it is nearly unique in funding basic research in clinical informatics. A good deal of informatics research in the other NIH institutes is focused in basic science, e.g., genomics, bioinformatics, and computational biology. AHRQ and PCORI support clinical informatics research, but it is highly applied. Only NLM funds critical basic research in clinical informatics, and this function is vitally important as we strive to use informatics to achieve the triple aim of better health, improved healthcare, and reduced costs.

d. Other comments, suggestions, or considerations, keeping in mind that the aim is to build the NLM of the future.

Another critical function of NLM that has provided value and should be further augmented is its training programs for those who aspire to careers in informatics research. I count myself among many whose NLM fellowship training led to a successful career as a researcher, educator, and academician generally. NLM training grants have also provided support for my university to educate the next generation of informatics researchers who have gone on to become successful researchers and other leaders in the field.

A final problem is that I would like to see addressed is the name itself, "National Library of Medicine." This name does not connote all of what NLM does. Yes the NLM is a world-renowned biomedical library, and that function is critically important to continue. But NLM also provides cutting-edge research and training in informatics, and an ideal change for NLM would be a name change to something like the "National Biomedical and Health Informatics Institute," of which a robust and innovative National Library of Medicine would be a vital part.

I look forward to seeing other input to the new NLM strategic planning process and the resulting strategic plan that will set priorities going forward for this great public resource that has benefitted patients, the healthcare system, and students, faculty, and others who have worked in biomedical informatics to advance human health.

Friday, January 20, 2017

What is the Value of Those Who Create and Disseminate Knowledge?

There is an old adage, “Those who can’t do, teach.” (And Woody Allen’s further, “Those who can’t teach, teach gym.”) My usual retort is a quote from Aristotle, "Those that know, do. Those that understand, teach.”

But we seem to be entering an era where an individual’s worth is related mostly to his or her wealth. In addition, there are plenty of people, many of same mind-set, who are highly critical of academia, in particular of people whose livelihood involves creating and/or disseminating knowledge.

I am not uncritical of some aspects of the academic world in which I work, but I am even more aghast toward those who believe it to be misguided or unnecessary.

In essence, my job involves the creation and dissemination of knowledge. This takes a certain skill set and collection of talents, just like any other knowledge-oriented job. I believe that this work is important to society and worthy of its investment, even though the lion’s share of the funding of my teaching work comes from learners who pay tuition.

My job is hardly stress-free. Academia is like most pursuits in life, where a certain amount of stress and competition is good, leading to productivity and innovation. And there are times when the stress and pursuit become counterproductive.

I owe a lot to subsidized public academia that has enabled my professional success in life. I attended public schools for my entire education, from kindergarten through medical school. When I started college at the University of Illinois in 1976, tuition was $293 per semester. Not per course or per credit, but for all of the courses I took that term. Even medical school, also at University of Illinois, was relatively inexpensive for me, with tuition around $3000 per year when I started in 1980. I am not against students have some “skin in the game” in higher education, but it must be within the means of anyone who wants to pursue it. By the same token, I believe that we in academia need to be accountable in providing a skill set that enables individuals to succeed in their chosen careers.

I am extremely gratified to have an academic job that I mostly enjoy going to each day. While most higher education faculty positions have a combination of research, teaching, and service, I have found my most passion in teaching. I particularly enjoy, and have received feedback from others, that I have a knack for taking bodies of knowledge and distilling out the big themes and most salient facts. I do also enjoy research and building on the synergy of the two that characterizes optimal higher education. I make a good salary as a department chair at a public medical school. I could certainly make more money in other pursuits, but I have had plenty to live comfortably, save for retirement, send my children to college, and handle unexpected expenses.

I don’t begrudge rich people their wealth, especially those who earned it from modest beginnings and/or by producing things that truly benefit society. But wealth is hardly the only measure of a person’s contributions and value to society, and there must always be a role for those who create and disseminate knowledge.

Thursday, January 12, 2017

What is the Right Approach to Sharing Clinical Research Data?

While many people and organizations have long called for data from randomized clinical trials (RCTs) and other clinical research to be shared with other researchers for re-analysis and other re-use, the impetus for it accelerated about a year ago with two publications. One was a call by the International Committee of Medical Journal Editors (ICMJE) for de-identified data from RCTs to be shared as condition of publication [1]. The other was the publication of an editorial in the New England Journal of Medicine wondering whether those who do secondary analysis of such data were “research parasites” [2]. The latter set off a fury of debate across the spectrum, e.g. [3], from those who argued that primary researchers labored hard to devise experiments and collect their data, thus having claim to control over it, to those who argued that since most research is government-funded, the taxpayers deserve to have access to that data. (Some of those in the latter group proudly adopted the “research parasite” tag.)

Many groups and initiatives have advocated for the potential value of wider re-use of data from clinical research. The cancer genomics community has long seen the value of a data commons to facilitate sharing among researchers [4]. Recent US federal research initiatives, such as the Precision Medicine Initiative [5] and the 21st Century Cures program [6] envision an important role for large repositories of data to accompany patients in cutting-edge research. There are a number of large-scale efforts in clinical data collection that are beginning to accumulate substantial amounts of data, such as the National Patient-Centered Clinical Research Network (PCORNet) and the Observational Health Data Sciences and Informatics (OHDSI) initiative.

As with many contentious debates, there are valid points on both sides. The case for requiring publication of data is strong. As most research is taxpayer-funded, it only seems fair that those who paid are entitled to all the data for which they paid. Likewise, all of the subjects were real people who potentially took risks to participate in the research, and their data should be used for discovery of knowledge to the fullest extent possible. And finally, new discoveries may emerge from re-analysis of data. This was actually the case that prompted the Longo “ esearch parasites” editorial, which was praising the “right way” to do secondary analysis, including working with the original researchers. The paper that the editorial described had discovered that the lack of expression of a gene (CDX2) was associated with benefit from adjuvant chemotherapy [7].

Some researchers, however, are pushing back. They argue that those who carry out the work of designing, implementing, and evaluating experiments certainly have some exclusive rights to the data generated by their work. Some also question whether the cost is a good expenditure of limited research dollars, especially since the demand for such data sets may be modest and the benefit is not clear. One group of 282 researchers in 33 countries, the International Consortium of Investigators for Fairness in Trial Data Sharing, notes that there are risks, such as misleading or inaccurate analyses as well as efforts aimed at discrediting or undermining the original research [8]. They also express concern about the costs, given that there are over 27,000 RCTs performed each year. As such, this group calls for an embargo on reuse of data for two years plus another half-year for each year of the length of the RCT. Even those who support data sharing point out the requirement for proper curation, wide availability to all researchers, and appropriate credit to and involvement of those who originally obtained the data [9].

There are a number of challenges to more widespread dissemination of RCT data for re-use. A number of pharmaceutical companies have begun making such data available over the last few years. Their experience has shown that the costs are not insignificant (estimated to be about $30,000-$50,000 per RCT) and a scientific review process is essential [10]. Another analysis found that the time to re-analyze data sets can be long, and so far the number of publications have been few [11]. An additional study found that identifiable data sets were only explicitly visible from 12% of all clinical research funded by the National Institutes of Health in 2011 [12]. This means that from 2011 alone, there are possibly more than 200,000 data sets that could be made publicly available, indicating some type of prioritization might be required.

There are also a number of informatics-related issues to be addressed. These not only include adherence to standards and interoperability [13], but also attention to workflows, integration with other data, such as that from electronic health records (EHRs), and consumer/patient engagement [14]. Clearly the trialists who generate the data must be given incentives for their data to be re-used [15]. My own work assessing the caveats of re-using EHR data is somewhat applicable here too, in that even RCT data may not have the breadth of data or cover sufficient periods of time for additional analyses [16].

There is definitely great potential for re-use of RCT and other clinical research data to advanced research and ultimately health and clinical care for the population. However, it must be done in ways that represent an appropriate use of resources and result in data that truly advances research, clinical care, and ultimately individual health.

1. Taichman, DB, Backus, J, et al. (2016). Sharing clinical trial data: a proposal from the International Committee of Medical Journal Editors. New England Journal of Medicine. 374: 384-386.
2. Longo, DL and Drazen, JM (2016). Data sharing. New England Journal of Medicine. 374: 276-277.
3. Berger, B, Gaasterland, T, et al. (2016). ISCB’s initial reaction to The New England Journal of Medicine Editorial on data sharing. PLoS Computational Biology. 12(3): e1004816.
4. Grossman, RL, Heath, AP, et al. (2016). Toward a shared vision for cancer genomic data. New England Journal of Medicine. 379: 1109-1112.
5. Collins, FS and Varmus, H (2015). A new initiative on precision medicine. New England Journal of Medicine. 372: 793-795.
6. Kesselheim, AS and Avorn, J (2017). New "21st Century Cures" legislation: speed and ease vs science. Journal of the American Medical Association. Epub ahead of print.
7. Dalerba, P, Sahoo, D, et al. (2016). CDX2 as a prognostic biomarker in stage II and stage III colon cancer. New England Journal of Medicine. 374: 211-222.
8. Anonymous (2016). Toward fairness in data sharing. New England Journal of Medicine. 375: 405-407.
9. Merson, L, Gaye, O, et al. (2016). Avoiding data dumpsters — toward equitable and useful data sharing. New England Journal of Medicine. 374: 2414-2415.
10. Rockhold, F, Nisen, P, et al. (2016). Data sharing at a crossroads. New England Journal of Medicine. 375: 1115-1117.
11. Strom, BL, Buyse, ME, et al. (2016). Data sharing — is the juice worth the squeeze? New England Journal of Medicine. 375: 1608-1609.
12. Read, KB, Sheehan, JR, et al. (2015). Sizing the problem of improving discovery and access to NIH-funded data: a preliminary study. PLoS ONE. 10(7): e0132735.
13. Kush, R and Goldman, M (2016). Fostering responsible data sharing through standards. New England Journal of Medicine. 370: 2163-2165.
14. Tenenbaum, JD, Avillach, P, et al. (2016). An informatics research agenda to support precision medicine: seven key areas. Journal of the American Medical Informatics Association. 23: 791-795.
15. Lo, B and DeMets, DL (2016). Incentives for clinical trialists to share data. New England Journal of Medicine. 375: 1112-1115.
16. Hersh, WR, Weiner, MG, et al. (2013). Caveats for the use of operational electronic health record data in comparative effectiveness research. Medical Care. 51(Suppl 3): S30-S37.

Friday, December 30, 2016

A Different Annual Reflection For This Past Year

Every year since the inception of this blog, my last posting of the year has been a reflection looking back over the year that is ending. This year’s reflection marks the completion of eight years of this blog, and writing this year’s posting feels different. This is no doubt because this blog has been very much tied into the key events of informatics over the last decade, in particular the Health Information Technology for Economic and Clinical Health (HITECH) Act and other actions emanating from the Presidency of Barack Obama. This has been a period of activist government with respect to our field, and now the US electorate (at least according to the rules of the Electoral College) has chosen a different path going forward.

Fortunately, the need for informatics is not going away. Even if the Affordable Care Act is repealed, the underlying problems in healthcare that led to its passage are still a challenge. Healthcare in the US is still the most fragmented, expensive, and inefficient of any country in the world. This does not mean I would want to get seriously ill anywhere else in the world, but I still believe there is also an ethical imperative to provide basic healthcare to all citizens in the least costly manner. Medicine is supposed to be a calling for physicians, and not just a job. Although I no longer care for patients directly, I view my work as a physician-informatician to support the delivery of more universal and efficient care by supporting the data, information, and knowledge needs of healthcare delivery and patients.

Informatics also supports other aspects of health that will also continue to be important even if reform of the US healthcare delivery system takes different directions. Informatics should support the health of the population through public health. It can support expansion of our knowledge and best practices by enhancing basic, clinical, and translational research. It can extend the reach of healthcare through telehealth and telemedicine. And because the US is still a prosperous nation to whom many still look for leadership, we can share our knowledge and tools for better health and healthcare with our fellow planetary citizens around the world, especially clinical and informatics professionals.

As for the blog itself, it continues to thrive. I am always gratified when people tell me they find it a valuable source of information, especially for key topics in the application of informatics as well as for issues for people seeking to start or advance careers in the field. The number of page views continues to increase, and in this last month, the total barreled through the 400,000 mark for the (including this) 267 posts I have made over the eight years. I have no plans to change anything with my approach to the blog any time soon.

There is no question that for people who work in academia, in research, and in health IT that there is uncertainty as to the future. Nonetheless, I am grateful that I have a loving family, wonderful colleagues, and a great many other friends who bring happiness and stability to my life.

Wednesday, December 28, 2016

Benchmarks to Assess the New President

According to the rules of US elections, Donald Trump won the Presidency and Republicans control the Senate and House of Representatives. I respect that.

This does not mean, however, that Trump and his party have any sort of mandate. Not only did Trump lose the popular vote by about 2.8 million votes (48.2%-46.2%), but he won three large states (Pennsylvania, Michigan, and Wisconsin) that would have swung the election to Hillary Clinton with a combined total of only 77,000 votes. And while this year there was a narrow overall majority of votes for House Republican candidates this year, in recent years there has been a majority of votes for Democratic candidates despite a hefty majority of seats filled by Republicans, owing to gerrymandering. There were also more popular votes for Democratic Senate candidates this year, although it was somewhat anomalous due to the California Senate race being a run-off between two Democratic candidates.

There is no question that this election was tilted to Mr. Trump by the growing number of mostly white working class people who have been left behind by both economic and social changes in our society. His election was also aided by an unknown amount from Russian hacking, fake news, and the questionable decision by the FBI to raise its investigation of the email issue in the last weeks of the campaign. This was a candidate who set new records for fact-checkers disputing his statements and had a large following of people who believed those falsehoods.

As such, the outcome of this election is anything but a mandate for Donald Trump. Yes, he did obtain more electoral votes than Hillary Clinton, but his victory was extremely narrow, and he and other Republicans need to be careful of overreach. This is especially true since it is not clear that Mr. Trump really stands for the kind of people and their views that he is installing in his political leadership. (It is often not clear what he stands for at all, since his governing philosophy is not very detailed or consistent.)

But the new Republican majority may find it harder to improve upon the economic situation than what they have been handed. The US economy certainly still has a number of problems, especially income inequality and technology that is changing the nature of work, especially manual work. By most measures, however, the US economy is actually doing well. We finish the year, and President Obama’s second term, with strong economic growth (Gross Domestic Product [GDP] up at a 3.5% annual rate last quarter and being positive most of his second term), low unemployment (currently 4.6%, nearly full employment), low inflation, and a booming stock market (Dow Jones Industrial Average closing in on 20,000). Gas prices are low and the proportion of people lacking health insurance is lower than it has been in decades.

I believe an important task is to hold President Trump accountable. We will want to see how he adheres to his conflicting campaign pledges and the results of those policies when they are implemented. This includes promises to massively slash taxes, increase defense and infrastructure spending, make no cuts to Medicare or Social Security, build a border wall and deport 11 million people, renegotiate trade deals and implement tariffs if necessary, and come up with "something better" as the Affordable Care Act is repealed. While I disagree with many of these actions, it will be important to see whether Mr. Trump carries them out, and if he does, what is their impact.

Even though a good deal of what Mr. Trump says bothers many of us, I believe it will be more important to look at his actions. I hope he will especially be held accountable by those who are not conservative ideologues, such as workers who have been displaced from coal-mining and manufacturing jobs and those who don’t believe that their new health insurance they have received through the Affordable Care Act will be taken away. I also hope the impact of his policies on the environment, including climate change, will be objectively measured. And, of course, an objective assessment of a foreign policy administered via Twitter.

While I believe Mr. Trump should be judged more for his actions and their outcomes, I don't think he should be let off the hook for his words either. This includes all the vitriol he spread through the years of President Obama, from stoking the fires of the birther movement to making false statements on the economy. Despite attempts to "unify" the electorate after a divisive election, we cannot forget Mr. Trump's insults and lies about individual people and of groups, from women to Muslims to Mexicans. I still shake my head in amazement when people are asked to not take everything Trump said during the campaign literally, that it is legitimate to enter some sort of "post-truth" era., or that a good proportion of his

In the end, a President is not responsible for everything that happens on his or her watch. But for a narcissistic individual who takes credit for things that go right, even when that credit is not deserved, we should also hold him or her to objective measures of performance as well. While Mr. Trump has mastered the neutering of the press through social media and other means, I hope that responsible journalism will rise to the task and objectively report the impact of the words and policies that emanate from his Presidency and his political party.

Thursday, December 8, 2016

Coping With Adversarial Information Retrieval in Modern Times

When I first chose my area of research focus in my postdoctoral fellowship in biomedical informatics in the late 1980s, I was intrigued by information retrieval (IR; also known as search). While most in informatics were still focused on artificial intelligence and expert systems, I was fascinated by the notion that computers could provide information in response to users entering text. At that time, of course, there were only modest amounts of information to retrieve. The main source was bibliographic databases such as MEDLINE. While the full text of journals and even some textbooks was starting to become available, it was mostly text and not figures or images.

The world of search started to change with the advent of the World Wide Web in the early 1990s. I had actually been skeptical that the Web could even deliver more than text in real-time, given how slow the Internet was at that time. This was also a time when my colleagues at Oregon Health & Science University (OHSU) started putting on continuing medical education (CME) courses for physicians about the growing amount of information available (including via CD-ROM drives). But when we taught about searching the Web, we presented many caveats, especially because there was no control over the quality of information [1].

A related happening about this same time was the growth of spam email [2]. In the 1980s and even into the early 1990s, the only real users of Internet email were academics and techies. But as the Web and underlying Internet spread to broader populations, so did spam email, especially because it was so easy to reach massive numbers of people.

These developments all gave rise to the notion of “adversarial” IR, something that was initially difficult to fathom when we were trying to develop the most effective methods to provide access to the highest quality information available [3]. But as content emerged that we hoped users would not retrieve, there started an additional focus in IR that considered ways to avoid providing users the worst information.

One advance that improved the ability of Web searching to retrieve high-quality material was Google and its PageRank algorithm. A major change pioneered by Google was to rank results based not on measures of similarity between words in the query and page, at the time considered to be our best approach, but instead by how many other pages pointed to them. While not perfect, the number of links to a page is indeed associated with its quality, e.g.,, more pages will point to those from the National Library of Medicine or Mayo Clinic than a less credible site.

Of course, this situation resulted in a number of other consequences, not the least of which was the emergence of search engine optimization (SEO), enabling people to fight against PageRank and related algorithms [5]. It also set off a tit-for-tat battle of search engine sites hiring armies of engineers to figure out how people were trying to game their systems [6]. In more recent years, the emergence of new information streams, most notably the Facebook newsfeed, has provided new opportunities and led to the proliferation of “fake news” attributed to impacting the recent US president election [7].

While technology will play some role in solving the adversarial IR problem, it will not succeed by itself. Clever programmers and others will likely always find ways to exploit approaches to limiting the spread of false or incorrect information. The sheer volume of such information makes human intervention an unlikely solution, and of course one person’s high quality information is another person’s trash heap.

The main way to solve the problem, however, is through education. It is all part of basic modern information literacy everyone must have in the 21st century. Just as I have argued that statistics should be a topic taught in high school if not earlier, so should modern information literacy, including related to health. While there will always be shades of gray in terms of information quality, people can and should be taught how to recognize that which is flagrantly false.

I hope we will learn from fake news, newer variants of spam email such as phishing, and other risks of the Internet era that we must train society to better understand our new information ecosystem, and how we can benefit from its value while minimizing its risk.


1. Hersh, WR, Gorman, PN, et al. (1998). Applicability and quality of information for answering clinical questions on the Web. Journal of the American Medical Association. 280: 1307-1308.
2. Goodman, J, Cormack, GV, et al. (2007). Spam and the ongoing battle for the inbox. Communications of the ACM. 50(2): 25-33.
3. Castillo, C and Davison, BD (2011). Adversarial Web Search. Delft, Netherlands, now Publishers.
4. Brin, S and Page, L (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems. 30: 107-117.
5. Anonymous (2015). The Beginner's Guide to SEO. Seattle, WA, SEOmoz.
6. Singhal, A (2004). Challenges in Running a Commercial Web Search Engine. Mountain View, CA, Google.
7. Davis, W (2016). Fake Or Real? How To Self-Check The News And Get The Facts. Washington, DC, National Public Radio.

Tuesday, November 29, 2016

Kudos for the Informatics Professor - Fall 2016 Edition

The year 2016 has been a busy but fun year of personal achievements. Many of the notable accomplishments involved giving talks, both in person and online, and around the country and the world. However, I also had a number of other achievements.

A few months ago I posted about talks during the summer of 2016. The fall of 2016 was equally busy. As I noted at the end of the summer posting, I was slated to give two talks in September. The first was the opening talk at the National Library of Medicine (NLM) Georgia Biomedical Informatics Course entitled, What is Biomedical Informatics? This talk was an updated version from the previous offering in this course delivered in April, 2016. In September, I also provided an online lecture in the National Institutes of Health BD2K Guide to the Fundamentals of Data Science Series entitled, Data Indexing and Retrieval.

In October, I had the opportunity to visit the world-renowned Geisinger Health System, where I met with a number of individuals who have taken courses of mine, both my 10x10 ("ten by ten") course as well as physicians in the new Clinical Informatics Fellowship who are taking online courses in the OHSU Biomedical Informatics Graduate Program. I also presented Grand Rounds on the topic of competencies in clinical informatics required of 21st-century clinicians and informaticians.

Also in October was the 25th Anniversary Celebration of the Biomedical Information Communication Center (BICC) at OHSU. The speakers at the event  included the current and long-time former Directors of the NLM. I provided an overview talk about the OHSU Department of Medical Informatics & Clinical Epidemiology (DMICE) and presented a poster on all of the collaboration that DMICE does at OHSU.

I started November with a talk at the OHSU Informatics Research Conference on Challenge Evaluations in Biomedical Information Retrieval, which was a preparation talk for another 25th anniversary talk to be mentioned in a moment.

In mid-November I was busy at the AMIA Annual Symposium, first leading a workshop on Evidence-Based Informatics at the Clinical Informatics Fellows’ Retreat that took place at my alma mater, the University of Illinois College of Medicine. Next I provided a talk at the AMIA Annual Symposium Learning Showcase entitled, The Full Spectrum Biomedical and Health Informatics Education at Oregon Health & Science University.

My final talk of the fall was at the Celebrating 25 Years of TREC Conference at the National Institute for Standards and Technology (NIST) in Gaithersurg, MD. My talk, The TREC Bio/Medical Tracks, described the various tracks in the biomedical domain at TREC over the years. A video of the talk is in Part 3 (starting around the 50-minute mark) of the Webcast archive page for the meeting.

What other accomplishments did I have this past fall? One was teaching my introductory biomedical and health informatics course to a group of clinical and IT leaders from Bangkok Duisuit Medical Services (BDMS), a network of hospitals in Thailand and a few in nearby countries. OHSU has an ongoing collaboration with BDMS in many areas, including informatics. This offering of the course had the usual recorded lectures and discussion forums, but added other activities, including interactive videoconferences and in-person sessions in both Bangkok and Portland. One of the participants in the course, Dr. Somsak Wankijcharoen, created a video of the experience.

Saturday, November 12, 2016

ABPM Extends “Grandfathering” Period for Clinical Informatics Physician Subspecialty Through 2022

The single most-viewed entry in the history of this blog is a posting from 2013 describing eligibility for the clinical informatics subspecialty for physicians. This was partly due to my wanting to have a standard reply for the frequent emails I received at the time from individuals asking if they would be eligible to sit for the board exam during the "grandfathering" period. I would also mention to them my singular most important piece of advice, which was to try, if possible, to get certified before 2018, after which they would need to complete an Accreditation Council for Graduate Medical Education (ACGME)-accredited fellowship.

Earlier this month, however, the American Board of Preventive Medicine (ABPM) extended the period that allows physicians to be eligible for board certification in the clinical informatics subspecialty by five years, through 2022. This means that the grandfathering (and "grandmothering" for my female colleagues!) period can be used to achieve board eligibility through 2022.

One new issue is how this will impact the growing number of ACGME-accredited fellowships, such as the one we offer at Oregon Health & Science University (OHSU). I still believe those fellowships will be the gold standard for early-career physicians to receive the best training in clinical informatics. But other physicians wanting to enter the field who cannot relocate jobs or families will still be able to pursue other options, one of which is master's degree programs such as our program at OHSU.

The official eligibility statement for the subspecialty is otherwise unchanged from the beginning of the grandfathering period and is documented on the ABPM Web site. The first three eligibility requirements are:
  1. Primary certification by one of the 23 member boards of the American Board of Medical Specialties (ABMS)
  2. Graduate from a US, Canadian, or other medical school deemed acceptable by the ABPM
  3. Unrestricted license to practice medicine in the US or Canada
The fourth requirement is the "pathway" by which one is eligible during the grandfathering era. There are two pathways for eligibility, one of which must be completed to be eligible to take the certification exam under the grandfathering criteria.

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 or other clinical informatics professional or leader), 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 a 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 never been made public by ABPM, 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 eligibility criteria also state that time spent in training in informatics can 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 this 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. This has indeed been the case, i.e., those completing the Master of Biomedical Informatics (MBI) Program at OHSU have been deemed eligible, presumably 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, has not on its own been enough. Some applicants have been able to mix and match to achieve eligibility, i.e., with some practice time combined with some education.

It should be noted that another option for physicians who are not eligible for board exam will be the Advanced Health Informatics Certification being developed by AMIA. This certification will be available to all clinician practitioners of informatics trained at the master's level and higher. It will also provide a pathway for physicians who are not eligible for the board certification pathway.

Overall, I am pleased with this development, although it still presents problems for physicians in the future who will want to transition their careers into informatics in the middle of their careers. But since that day of reckoning has now been put off another five years, I guess we can cross that proverbial bridge when we come to it in the early part of the next decade.

Sunday, October 23, 2016

Biomedical Big Data Science Open Educational Resources (OERs) Released; Feedback Sought

For the last couple years, faculty from the Oregon Health & Science University (OHSU) Department of Medical Informatics & Clinical Epidemiology (DMICE) and Library have been developing open educational resources (OERs) in the area of Biomedical Big Data Science. Funded by a grant from the National Institutes of Health (NIH) Big Data to Knowledge (BD2K) Program, OERs have been produced that can be downloaded, used, and repurposed for a variety of educational audiences by both learners and educators.

Development of the OERs is an ongoing process, but we have reached the point where a critical mass of the content is being made available for use and to obtain feedback. The image below shows the home page for the Web site.

The OERs are intended to be flexible and customizable and we encourage others to use or repurpose these materials for training, workshops and professional development or for dissemination to instructors in various fields. They can be used as "out of the box" courses for students, or as materials for educators to use in courses, training programs, and other learning activities. We ultimately aim to create 32 modules on the following topics:
  1. Biomedical Big Data Science
  2. Introduction to Big Data in Biology and Medicine
  3. Ethical Issues in Use of Big Data
  4. Clinical Standards Related to Big Data
  5. Basic Research Data Standards
  6. Public Health and Big Data
  7. Team Science
  8. Secondary Use (Reuse) of Clinical Data
  9. Publication and Peer Review
  10. Information Retrieval
  11. Version Control and Identifiers
  12. Data Annotation and Curation
  13. Data Tools and Landscape
  14. Ontologies 101
  15. Data Metadata and Provenance
  16. Semantic Data Interoperability
  17. Choice of Algorithms and Algorithm Dynamics
  18. Visualization and Interpretation
  19. Replication, Validation and the Spectrum of Reproducibility
  20. Regulatory Issues in Big Data for Genomics and Health Semantic Web Data
  21. Hosting Data Dissemination and Data Stewardship Workshops
  22. Guidelines for Reporting, Publications, and Data Sharing
  23. Terminology of Biomedical, Clinical, and Translational Research
  24. Computing Concepts for Big Data
  25. Data Modeling
  26. Semantic Web Data
  27. Context-based Selection of Data
  28. Translating the Question
  29. Implications of Provenance and Pre-processing
  30. Data Tells a Story
  31. Statistical Significance, P-hacking and Multiple-testing
  32. Displaying Confidence and Uncertainty
At the present time, 20 of the above modules are available for download and use. We are encouraging their use and seeking feedback from those who make use of them. The feedback will be used to improve the available modules and guide development of those not yet released.

We have also been developing mappings to research competencies in other areas, such as for the NIH Clinical and Translational Science Award (CTSA) consortium research competency requirements and the Medical Library Association professional competencies for health sciences librarians. To this end, we have been able to link these materials to existing efforts, and provide training opportunities for learners and educators working in these areas. We ultimately aim to complete this mapping across all of the BD2K training offerings, to align with other groups, avoid redundancy and to ensure we are meeting the needs of these various groups.

This project is actually one of several projects that have been funded by grants to develop and provide education in biomedical informatics and data science. The other projects include:
We hope that all of these materials are useful for many audiences and look forward to feedback enabling their improvement.

Thursday, October 13, 2016

What Should Be The Spectrum of Career Opportunities for Clinical Informatics Subspecialists?

A common reason given for the establishment of clinical informatics as a physician subspecialty is the recognition of the growing role of physicians who work in informatics professionally, particularly in operational clinical settings. Sometimes this is viewed almost synonymous with the Chief Medical Informatics Officer (CMIO) and related roles in healthcare provider organizations.

However, I prefer to think of the subspecialty more broadly. Even if the CMIO is the most common or aspired to position for clinical informatics subspecialists, we should still consider other career paths, especially for those who will increasingly be trained in formal fellowships. Just as physicians of other specialties may enter private practice, managed care settings, academia, and even industry, so should we view the breadth of options for those trained in clinical informatics. I certainly hope there will be pathways from clinical fellowships into academic careers for these physicians.

I was recently involved in a discussion on an email list where many CMIOs lamented that many of the questions on the clinical informatics subspecialty board exam did not seem pertinent to their day-to-role as CMIOs. That led me to raise the question, do we view this subspecialty as primarily focused on the CMIO role, or should it cover broader aspects of clinical informatics? Not being a CMIO, and being in academia, my sentiments are with the broader view. But on the other hand, as the CMIO is a prominent position for those working in this field, and perhaps the most common one, it does deserve important consideration.

This discussion is highly relevant to those of us standing up ACGME-accredited clinical informatics fellowships. We certainly want our fellows to gain substantial operational experience. But I would advocate that they also learn the fundamentals of the informatics field, and believe that although a little dated since its creation in 2009, the Core Content outline covers it pretty well.

Just as while most physicians in a specialty (e.g., internal medicine) do not use the entire spectrum of knowledge in their fields on a daily basis, I believe our clinical informatics fellowships should take the same approach and that the board exam should reflect comparable breadth. I do not believe there is anything in the Core Content outline that is completely superfluous to the practice of being a CMIO or other jobs applying clinical informatics.

The challenge, then, is how to create a fellowship program and board exam to reflect the broader field. Informatics has always had (and I am a product of) the research-oriented NLM fellowships. Even though focused on research, these fellowships have produced diverse outcomes, including some CMIOs. While the focus on clinical fellowships is somewhat different, there should be no reason why graduates of these fellowships should not be able to pursue careers in academia, research, industry, and other settings.

Monday, October 10, 2016

Apple Watch Series 2: Great Hardware, Software Needs Work

When the original Apple Watch came out, it was a non-starter for me. As one of my main uses of a smartwatch is for running, i.e., to track my runs and view them on a map, the lack of on-board GPS meant that the watch had to be tethered to an iPhone. While I do sometimes run with my iPhone, I might as well carry just my iPhone. In addition, I sometimes run in places where I am not able to use my iPhone, such as countries that do not have international data plans with my carrier, Verizon (admittedly increasingly rare).

I was therefore thrilled to read the announcement of the Apple Watch Series 2, which would have standalone GPS and enable me to track runs without a phone.

I have been using my Apple Watch Series 2 for about a month now, and have some observations and hopes for improvement. If those improvements are made soon, I will add a postscript to this posting.

From a hardware standpoint, the watch is excellent. It is comfortable to wear and works seamlessly with my iPhone 6 (soon to be replaced with a 7 Plus). I have always able to make it through a day (even with a run that consumes 20-30% of the battery life per hour of activity) without having to recharge it.

In addition to capturing my runs, I want to be able to view them on any device, including on a computer via a Web site, and also share them on Facebook. I want to be able to view all of the data, including the map of where I have run, as well as export it via standard formats such as GPX and TCX. For years I have used various Garmin fitness watches, and I appreciated the ease by which I could capture my run, display its data and map on the Garmin Connect Web site, and share it to Facebook and other digital places.

In terms of capturing the run, the Apple Watch Series 2 does great. I am actually impressed at how quickly it locks on the GPS satellites, and the accuracy seems to be equal to my previous Garmin watches.

But I have disappointment in its ability to export or display of data. While the watch’s Workout app is simple and easy to use, and the Activity app on my iPhone easy to use and display results, the data cannot be exported to other apps. I am also disappointed that the Activity app only runs on the iPhone, and therefore cannot be accessed by other hardware, including the iPad or a computer accessing a Web site.

I also dislike the sharing capabilities of the Activity app. When one tries to share the entire exercise activity to Facebook, all that is uploaded is an image from the app, and not the details of distance run, time, map, etc. One can share the map of the Activity, but that is not uploaded with any other data about the run, e.g., distance, time, etc.

Another disappointment is that other fitness apps do not (yet) allow capture of the watch GPS data. For example, while RunKeeper and MapMyRun have Apple Watch apps, they presently do not capture the watch’s GPS data when not tethered to the phone. The SpectraRun workouts app can access and export the run data but it presently does not export the GPS data into the TCX file it generates. I assume that updates to these non-Apple apps will eventually be able to access the GPS, and this might also solve the problem of Activity app data not being exportable.

Fortunately, all of these disappointments should be easily fixable in software, and I am hopeful that Apple and other developers will remedy them quickly. I have had some online dialog with one of the running app developers, and they assured me (and others) that they are trying to quickly update their watch apps to capture the GPS directly from the watch.

Tuesday, October 4, 2016

Update for a Standard Occupational Classification (SOC) Code for Informatics: Likely to Happen But Needing Revision

For years, many in the informatics field have lamented our invisibility when it comes to US government labor statistics. As I and others have been writing for years, there is no Standard Occupational Classification (SOC) code for those who work professionally in informatics [1]. As the SOC is updated by the Bureau of Labor Statistics about once a decade, I was pleased to be appointed to a group led by the Office of National Coordinator for Health IT (ONC) to submit a proposed revision to the 2018 SOC to include a code for health informatics in July 2014.

Like many classifications, the SOC is organized hierarchically. Its hierarchy goes to a depth of four levels, with the levels called Major Group, Minor Group, Broad Group, and Detailed Occupation. The Major Group. Most healthcare occupations are in the Major Group 29-0000, which is subdivided into three Minor Groups, which are in turn broken down into Broad Groups and Detailed Occupations for many health professionals from physicians to phlebotomists. In the last (2010) SOC, there was only one Broad Group and Detailed Occupation pertaining to health IT, namely 29-2070 Medical Records and Health Information Technicians, which mainly referred to those with the Registered Health Information Technologist (RHIT) certification from the health information management (HIM) field. The list below shows the three Minor Groups in the health professions and then more detail for the 29-2070/22-2071 code:
29-0000 Healthcare Practitioners and Technical Occupations
  29-1000 Health Diagnosing and Treating Practitioners
  29-2000 Health Technologists and Technicians
    29-2070 Medical Records and Health Information Technicians
      29-2071 Medical Records and Health Information Technicians
  29-9000 Other Healthcare Practitioners and Technical Occupations
Those from HIM with the Registered Health Information Administrator (RHIA) certification were among those included in the 11-9111 Medical and Health Services Managers category. (The Broad Group 11-0000 serves for Management Occupations.)

Earlier this year, the BLS released its first proposed revisions for the 2018 SOC for public comment. In particular, they released Docket Number 1-0148 -- Health Informatics Practitioners (Multiple), which included the following:
Multiple dockets requested new detailed occupations and improved coverage of occupations related to Health Information Technology such as Health Informatics Practitioners, Medical Records Specialists, and Medical Registrars. The SOCPC partially accepted these recommendations and proposed revising the title for 29-2071 Medical Records and Health Information Technicians to 29-2071 Medical Registrar and Records Specialists, adding Medical Bill Coder as an illustrative example, and adding "Includes medical coders" to the definition. The SOCPC also proposes a new broad and detailed occupations (29-9020 and 29-9021) for Health Information Technology, Health Information Management, and Health Informatics Specialists and Analysts. Finally, the SOCPC proposes adding illustrative examples to the existing 11-9111 Medical and Health Services Managers to include: Clinical Informatics Director, Health Information Services Manager, and Chief Medical Information Officer.

While I was pleased to see that our recommendation for the addition of a code for health informatics practitioners was accepted, myself and others were disappointed that the code lumped together three distinct groups who work professionally with IT in healthcare, namely health informatics, health information management, and health IT. A number of leading health IT organizations support the view that these are distinct. I was pleased to have the opportunities to work with my colleagues from the American Medical Informatics Association (AMIA) and a number of other organizations to draft a letter endorsing the view the there should be three Detailed Occupation codes for these three areas.

In particular, the letter led by AMIA advocates modification to the final 2018 SOC that will be released in 2017 that will split the new 29-9021 code into three new Detailed Occupations defined as follows:
  • Health Informatics professionals: Design, develop, select, test, implement, and evaluate new or modified informatics solutions, data structures, and clinical decision support mechanisms to support patients, healthcare professionals, and improved usability of such systems for patient safety within healthcare contexts.
  • HIM professionals: acquire, analyze, and protect digital and traditional medical information vital to the daily operations management of health information and electronic health records (EHRs).
  • Health IT professionals: Apply knowledge of healthcare and information systems to assist in the design, development, and continued modification of computerized health care systems.
The letter also suggests that, SOCPC for 11-9111 Medical and Health Services Managers to include: Clinical Informatics Director, Health Information Services Manager, and Chief Medical Information Officer. We suggest the addition of “Chief Nursing Informatics Officer” to this list to add further clarity. Experience among our constituencies indicate a proliferation of senior executives and other management-level job titles within and across these distinct occupations, all of which need to be captured under this detailed code.

I agree with AMIA and others that the occupations of health informatics, health information management, and health IT are each important yet unique within healthcare. Having them represented in the SOC separately will hopefully allow further delineation of the contributions each makes to advancing the use of information and technology in healthcare.


1. Hersh, W (2010). The health information technology workforce: estimations of demands and a framework for requirements. Applied Clinical Informatics. 1: 197-212.

Wednesday, September 7, 2016

Free Course in Healthcare Data Analytics Offered by OHSU

I am pleased to announce that the Department of Medical Informatics & Clinical Epidemiology (DMICE) of Oregon Health & Science University (OHSU) is offering a free continuing education course, Update in Health Information Technology: Healthcare Data Analytics, to physicians, nurses, other healthcare professionals, and health informatics/IT professionals. Registration is available at

This course is made freely available via a grant from the Office of the National Coordinator for Health IT (ONC) that I described in a previous posting last year. The grant requires us to have 1000 individuals complete the course by June 2017. The full updated ONC Health IT curriculum will also be made freely available in 2017.

Although the course is open to all healthcare professionals and health informatics/IT professionals, physicians will additionally be able to obtain continuing medical education (CME) credit through OHSU. For physicians certified in the new Clinical Informatics Subspecialty, Lifelong Learning and Self-Assessment (LLSA) credits towards American Board of Preventive Medicine (ABPM) Maintenance of Certification Part II (MOC-II) requirements for the subspecialty are also available.

The course consists of 14 modules that are estimated to take about 18 hours to complete. The course is completely online, and consists of lectures and self-assessment quizzes. References to further information are also provided. Those completing the entire course (viewing all of the lectures and completing the self-assessment quizzes) and evaluation form will receive a Certificate of Completion from OHSU. Physicians will be able to claim 18 credits of CME or (for those certified in Clinical Informatics) MOC-II. (We are not able to offer OHSU academic credit for the course.)

The course will be offered 6 times in overlapping two-month blocks starting in October 2016. Because of the anticipated large enrollment, the entire course will need to be completed during one block in order to receive the Certificate of Completion and CME/MOC-II credit. If the course is not completed during the block, participants can re-enroll in a later block. The course will only be offered for free through May 2017.

The first step in taking the course is registering at Each participant will be asked to provide some basic information, including name, employer, and email address. (All data will be kept confidential by OHSU, with the exception of confidential reporting to ONC.) After registration, participants will be sent login information to OHSU's Sakai Learning Management System. After completing all of the modules and the self-assessment quizzes, each participant will need to complete the evaluation form. He or she will then be sent via email a PDF Certificate of Completion. (Physicians will additionally be sent certifications for CME or MOC-II credit after completing additional evaluation information.)

Within the Sakai system, each module will provide an overview of learning objectives, one or more lecture segments (in MP4 format, viewable on both computers and mobile devices), optional additional materials, and a self-assessment quiz of 5-10 multiple-choice questions. (Those seeking CME or MOC-II credit must achieve a correct rate of 70% to pass; each quiz will be able to be taken up to 5 times.) Sakai will also provide an interactive forum for those having questions or comments about the materials. Due to the anticipated large enrollment, we will encourage participants to interact and answer questions among themselves, with OHSU teaching assistants bringing in course faculty as needed.

The 14 modules of the course include the following:

  • General Health Care Data Analytics
  • Extracting and Working with Data
  • Population Health and the Application of Health IT
  • Applying Health IT to Improve Population Health at the Community Level
  • Identifying Risk and Segmenting Populations: Predictive Analytics for Population Health
  • Big Data, Interoperability, and Analytics for Population Health
  • Data Analytics in Clinical Settings
  • Risk Adjustment and Predictive Modeling
  • Overview of Interoperable Health IT
  • Standards for Interoperable Health IT
  • Implementing Health Interoperability
  • Ensuring the Security and Privacy of Information Shared
  • Secondary Use of Clinical Data
  • Machine Learning and Natural Language Processing

The OHSU course faculty include:

  • William Hersh, MD, Department of Medical Informatics & Clinical Epidemiology
  • Vishnu Mohan, MD, MBI, Department of Medical Informatics & Clinical Epidemiology
  • David Dorr, MD, MS, Department of Medical Informatics & Clinical Epidemiology
  • Peter Graven, PhD, Department of Emergency Medicine
  • Karen Eden, PhD, Department of Medical Informatics & Clinical Epidemiology

The MOC-II credit is important for the new subspecialty, with those who are board-certified needing to obtain a certain amount to re-certify in 10 years. The American Medical Informatics Association (AMIA) has already developed MOC-II activities, largely through its meetings, but will also have online offerings as it implements its learning management system. They will also offer MOC-IV credits in the future.

Sunday, September 4, 2016

Kudos for the Informatics Professor - Summer 2016 Edition

It has been a busy but enjoyable summer for me, with the opportunity to give invited talks at a number of international locations as well as at some international conferences closer to home. I also had some publications released and carried out a number of teaching activities.

My talks began with leading a roundtable discussion at the Society for Imaging Informatics in Medicine 2016 Conference in Portland, OR. The title of the roundtable was, Clinical Informatics Certification for Physicians & Non-Physicians, and I provided a history and overview, and led a discussion of future directions, for the new clinical informatics subspecialty for physicians

Later in July, I ventured to Pisa Italy, where I gave the Keynote Talk at the Medical Information Retrieval (MedIR) Workshop, which was part of the ACM SIGIR 2016 meeting. Entitled, Challenges for Information Retrieval and Text Mining in Biomedicine: Imperatives for Systems and Their Evaluation, my talk described the challenges for search and text processing systems in the biomedical domain for computer science researchers.

In early August, back in Oregon, I delivered the Keynote Talk at the Joint International Conference on Biological Ontology and BioCreative at Oregon State University in Corvallis, OR. My talk, Information Retrieval and Text Mining Evaluation Must Go Beyond “Users”: Incorporating Real-World Context and Outcomes, discussed the challenges of evaluating search and text processing systems in the biomedical domain for bioinformatics researchers.

Later in August I was in a different part of the world, Thailand. Oregon Health & Science University (OHSU) has a growing international collaboration there in partnership with Bangkok Dusuit Medical Services. I delivered Grand Rounds at their flagship Bangkok Hospital. The title of my talk was, Overview of Clinical Informatics Activities in the US. I provided an overview of clinical informatics activities in the US, including adoption of electronic health records and the new clinical informatics subspecialty for physicians.

Also on that trip I was one of the keynote speakers at the HIMSS AsiaPAC 16 Conference in Bangkok. My talk was entitled, Advancing Digital and Patient-Centered Care Requires Competent Clinicians and Informatics Professionals, and I described the knowledge and training needed for optimal use of digital health systems for patients by clinicians and informatics professionals.

Finally on that trip I spent a day leading a workshop on various clinical informatics topics at Phuket International Hospital. Even better was getting to spend a weekend in that lovely beach city (see below)!

I also had release of some published papers this summer. One was a Technical Brief (hardly brief at over 60 pages!) prepared for the Agency for Healthcare Research & Quality (AHRQ) Effective Health Care Program on Telehealth: Mapping the Evidence for Patient Outcomes From Systematic Reviews. Another was a publication describing early experiences with clinical informatics fellowships for physicians in Journal of the American Medical Informatics Association.

I also carried out a substantial amount of teaching this summer. As I have every summer, I directed and taught in the AMIA Clinical Informatics Board Review Course. Next year is the last year of the “grandfathering” period that allows physicians to become board-certified without formal clinical informatics fellowship training, although a proposal has been put forth to the American Board of Preventive Medicine to extend that period for another five years. We will see what their decision is in November.

I also brought to a close the four-month long introductory online course I had been teaching to clinical informatics leaders at BDMS (see above) in Thailand. We spent a couple days at Bangkok Hospital reviewing course content, presenting papers, and preparing for course projects that will be presented when this group visits OHSU in November.

That trip also took me briefly to Singapore, where I led the in-person session at the end of the i10x10 course under the rubric of the Gateway to Health Informatics Course. This was the 15th offering of the course dating back to 2009.

Upon returning from Thailand and Singapore, I gave a lecture to new first-year OHSU medical students like I did last year entitled, Information is Different Now That You’re a Doctor. I enjoy giving this lecture to new medical students and describing the many ways that information is different now that they are becoming professionals, everything from seeking best evidence to maintaining professional behavior with highly private information, especially on social media.

I will also be doing some teaching in the next couple weeks for federal organizations, namely the National Library of Medicine (NLM) and the National Institutes of Health (NIH) Big Data to Knowledge (BD2K) Program. The NLM teaching involves giving the introductory lecture that kicks off their week-long in-residence biomedical informatics course. The BD2K teaching will involve giving a webinar in the year-long BD2K Guide to the Fundamentals of Data Science Series. My overview lecture will focus on data management, indexing, and retrieval.

There will be more talks, publishing, and teaching this fall, so stay tuned!