Thursday, October 30, 2014

OHSU Clinical Informatics Fellowship Accredited and Accepting Applications

The Oregon Health & Science University (OHSU) Clinical Informatics Fellowship Program is accepting applications for its inaugural class of fellows to begin in July, 2015. The program was notified by the Accreditation Council for Graduate Medical Education (ACGME) in September, 2014 that it received initial ACGME accreditation. The program is now launching its application process for its initial group of trainees. These fellowships are for physicians who seek to become board-certified in the new subspecialty of clinical informatics. Many graduates will likely obtain employment in the growing number of Chief Medical Information Officer (CMIO) or related positions in healthcare and vendor organizations.

This fellowship will be structured more like a traditional clinical fellowship than the graduate educational program model that our other offerings. Fellows will work through various rotations in different healthcare settings, not only at OHSU Hospital but also the Portland VA Medical Center. They will also take classes in the OHSU Graduate Certificate Program that will provide them the knowledge base of the field and prepare them for the board certification exam at the end of their fellowship. The program Web site describes the curriculum and other activities in the fellowship.

It is important to note that this clinical informatics fellowship is an addition to the suite of informatics educational offerings by OHSU and does not replace any existing programs. OHSU will continue to have its graduate program (Graduate Certificate, two master's degrees, and PhD degree) as well as its other research fellowships, including the flagship program funded by the National Library of Medicine. The student population will continue include not only physicians, but also those from other healthcare professions, information technology, and a wide variety of other fields. Job opportunities across the biomedical and health informatics continue to be strong and well-compensated.

OHSU was the third program in the country to receive accreditation in the country. Several other programs are also in the process of seeking accreditation, and a number of them will be using OHSU distance learning course materials for the didactic portion of their programs. (This summer, the first two fellows in the Stanford Packard Children's Hospital fellowship program took the introductory biomedical informatics course from OHSU.)

As defined by ACGME, clinical informatics is "the subspecialty of all medical specialties that transforms health care by analyzing, designing, implementing, and evaluating information and communication systems to improve patient care, enhance access to care, advance individual and population health outcomes, and strengthen the clinician-patient relationship." The new specialty was launched in 2013, with physicians already working in the field able to sit for the certification exam by meeting prior practice requirements. Starting in 2018, this "grandfathering" pathway will go away, and only those completing an ACGME-accredited fellowship will be board-eligible. Last year, seven OHSU faculty physicians became board-certified in the new clinical informatics subspecialty, including the program director (William Hersh, MD) and two Associate Program Directors (Vishnu Mohan, MD, MBI; Thomas Yackel, MD, MS, MPH).

We look forward to a great group of applicants and the launch of the fellowship next summer. We also look forward to working with colleagues launching similar programs at other institutions as the field of clinical informatics begins to take hold.

Tuesday, October 21, 2014

What are Realistic Goals for EHR Interoperability?

Last week, the two major advisory committees of the Office of the National Coordinator for Health IT (ONC) met to hear recommendations from ONC on the critical need to advance electronic health record (EHR) interoperability going forward. The ONC Health IT Policy Committee and the ONC Health IT Standards Committee endorsed a draft roadmap for achieving interoperability over 10 years, with incremental accomplishments at three and six years. The materials from the event are worth perusing.

The ONC has been facing pressure for more action on interoperability. Although great progress has resulted from the HITECH Act in terms of achieving near-universal adoption of EHRs in hospitals (94%) [1] and among three-quarters of physicians [2], the use of health information exchange (HIE), which requires interoperability, is far lower. Recently, about 62% of hospitals report exchanging varying amounts of data with outside organizations [3], with only 38% of physicians exchanging data with outside organizations [4]. A recent update of the annual eHI survey shows there are still considerable technical and financial challenges to HIE organizations that raise questions about their sustainability [5]. The challenges with HIE lagging behind EHR adoption was among the reasons that led ONC to publish a ten-year vision for interoperability in the US healthcare system [6].

The ONC was also pressed into action by a report earlier this year from the JASON group, a group of scientists who advise the government [7]. This led to formation of a JASON Report Task Force (JTF) to respond to the report's recommendations, which would feed into the larger process of developing a ten-year road map for interoperability. The JASON report was critical of the current state of the industry, noting the lack of progress on interoperability as well as criticizing current vendor practices that make exchange of data with outside organizations more difficult. The report called for a unified software architecture and public application programming interfaces (APIs) that would quickly replace existing vendor systems.

The JTF presented its recommendations at the meeting. The task force pushed back some on the JASON Report, embracing the larger vision of the report but advocating a more incremental, market-driven approach to reaching their shared goals. In particular, the JTF put forth six recommendations for advancing the health IT ecosystem, which are (mostly quoting from the report, as follows):
  • Focus on interoperability - ONC and CMS should re-align the Meaningful Use program to shift focus to expanding interoperability, and initiating adoption of public APIs. Requirements for interoperability should be added to Meaningful Use Stage 3 as well as EHR certification.
  • Industry-based ecosystem - A market-based coordinated architecture should be defined to create an ecosystem to support API-based interoperability.
  • Data sharing networks in a coordinated architecture - The architecture should loosely couple market-based data sharing networks (agreements). There should not be through a highly prescribed, top-down, approach.
  • Public API as basic conduit of interoperability - The public API should enable data- and document-level access to clinical and financial systems according to current internet standards. It should be public and secure.
  • Priority API services - Core data services and profiles should define the minimal data and document types supported by public APIs. The initial focus should be on clinician-clinician and consumer use cases.
  • Government as market motivator - ONC should proactively monitor the progress of exchange and implement non-regulatory steps to catalyze the adoption of public APIs.
The two advisory committees then presented their draft roadmap, which will be finalized following public comment in March, 2015. The draft roadmap laid out five core building blocks as well as general goals for three, five, and ten years out. The building blocks fall into the categories of:
  • Core technical standards and functions
  • Certification to support adoption and optimization of health IT products and services
  • Privacy and security protections for health information
  • Supportive business, clinical, cultural, and regulatory environments
  • Rules of engagement and governance
The general goals for 2017 advocate a focus on clinicians and individuals being able to send, receive, find, use a basic set of essential health information. Later goals focus on using expanded sources and users of information, improved quality and reduced cost of care, and Increased automation, ultimately aiming to achieve the vision of the learning health system [8].

The meeting was summarized well (as always) by John Halamka, who also described his view of the emerging core technical standards and functions, which include:
  • RESTful architectures for efficient client-server interaction - the emerging industry standard uniform interface between client and server, which is used by most Web-based software platforms (e.g., Google, Facebook)
  • OAuth2 for Internet-based security - another emerging industry standard that allows distributed secure access across systems on the Internet
  • Standard API for query/retrieval of data using standard data markup languages including eXtensible Markup Language (XML) and Javascript Object Notation (JSON). The emerging standard for a health public API is HL7's Fast Health Interoperability Resources (FHIR). They provide a nice overview aimed at clinicians.
All of the speakers noted a need for these standards to handle both documents and discrete data. While the JASON report and the infamous PCAST report of a few years back called for all data elements to be discrete, the reality is that there will always be a need for documents and the narrative text within to explain the patient's story and provide other nuance that purely discrete data cannot describe.

What solutions would I recommend for technical standards as someone who is more focused on the capture, use, and analysis of data but less expert in the nuances of implementation? I take it from the experts that RESTful architectures with OAuth2 security and FHIR APIs with some specified data standards make the most sense. I will advocate for some basic standards for documents and discrete data that will facilitate use of data. For documents, this is Consolidated Clinical Document Architecture (CCDA) with standard metadata including document and section type names. For discrete data, I advocate the use of mature terminology standards for problems and diagnoses (ICD, SNOMED), tests (LOINC), and medications (RxNorm/RXTerms) as well as the National Library of Medicine Value Set Authority Center (VSAC) for quality and other measures. Combined with public APIs, use of these data standards could vastly simplify interoperability and not require the myriad of system-to-system interfaces that add cost and complexity.

I do recognize that the presence of standardized data alone does not guarantee its provenance. For example, many organizations (and people within them) take different approaches to managing problem lists. Likewise, the mere listing of a drug in a patient record is no guarantee it was actually prescribed, filled at the pharmacy, or taken by the patient. Nonetheless, starting to get data into standardized forms will greatly advance interoperability and, as a result, clinical care and secondary uses of the data.

Certainly there will continue to be challenges around interoperability, data standards, and related areas. But the ONC's plans are a good step in moving us toward the vision of a connected, learning healthcare system. I look forward to adding my comments to the public comment process and seeing an achievable and implementable vision for the future.


1. Charles, D, Gabriel, M, et al. (2014). Adoption of Electronic Health Record Systems among U.S. Non-federal Acute Care Hospitals: 2008-2013. Washington, DC, Department of Health and Human Services.
2. Hsiao, CJ and Hing, E (2014). Use and Characteristics of Electronic Health Record Systems Among Office-based Physician Practices: United States, 2001–2013. Hyattsville, MD, National Center for Health Statistics.
3. Swain, M, Charles, D, et al. (2014). Health Information Exchange among U.S. Non-federal Acute Care Hospitals: 2008-2013. Washington, DC, Department of Health and Human Services.
4. Furukawa, MF, King, J, et al. (2014). Despite substantial progress in EHR adoption, health information exchange and patient engagement remain low in office settings. Health Affairs. 33: 1672-1679.
5. Anonymous (2014). 2014 eHI Data Exchange Survey Key Findings. Washington, DC, eHealth Initiative.
6. Anonymous (2014). Connecting Health and Care for the Nation: A 10-Year Vision to Achieve an Interoperable Health IT Infrastructure. Washington, DC, Department of Health and Human Services.
7. Anonymous (2014). A Robust Health Data Infrastructure. McLean, VA, MITRE Corp.
8. Smith, M, Saunders, R, et al. (2012). Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC, National Academies Press.

Saturday, October 11, 2014

OHSU Informatics Awarded NIH Grants Focused on Big Data and Analytics

The Oregon Health & Science University (OHSU) Department of Medical Informatics & Clinical Epidemiology (DMICE) has been awarded two grants to develop educational content and skills courses in the new National Institutes of Health (NIH) Big Data to Knowledge (BD2K) Program. In addition, DMICE was awarded an additional grant in collaboration with Mayo Clinic that makes use of Big Data from electronic health records (EHRs) for the purpose of identifying patients who might be eligible for clinical research studies.

On Thursday, October 9, this first round of BD2K grants were announced. A total of $32 million was awarded for 38 grants in the areas of enabling data utilization, developing analysis methods and software, enhancing training, and establishing centers of excellence. The two DMICE grants total about $1 million over three years. The two grants awarded to OHSU were among nine grants awarded for development of open educational resources and courses. Eight other institutions in addition to OHSU received more than one grant.

The BD2K initiative was launched by NIH in 2012, when it was recognized that an increasingly important aspect of biomedical research was to leverage data from clinical and biological sources. Its mission is to enable biomedical scientists to use big data effectively and appropriately to enhance reproducible research.

The two OHSU BD2K grants were R25 educational grants. Although national in scope, they will also have important local benefits for OHSU, Oregon, and the rest of the Pacific Northwest. One of the R25 grants will develop open educational resources (OERs) that can be adapted for a variety of educational programs, from the undergraduate to graduate and professional levels. The materials will use the same format as the Office of the National Coordinator for Health IT (ONC) curricular materials.

The other R25 grant will develop a Big Data skills course that will make available curricula and data sets to provide training in methods for basic, clinical and translational researchers as well as clinicians, librarians, and others. All researchers, especially graduate students, will be eligible to take the skills course and hone their skills in data.

DMICE plans to incorporate the materials from both grants in its own courses in its biomedical informatics graduate program, while the OHSU Library will utilize the materials via its educational outreach efforts. The OERs will also join the existing ONC curriculum materials on the American Medical Informatics Association (AMIA) Web site.

The OER project will be led by three PIs: William Hersh, MD; Shannon McWeeney, PhD; and Melissa Haendel, PhD. The skills development course will be led by David Dorr, MD, MS, and Drs. McWeeney and Haendel. These four OHSU faculty will also become part of the BD2K national community that NIH is establishing to widely disseminate knowledge, tools, and educational materials around Big Data.

The additional R01 grant is funded by the National Library of Medicine, the NIH institute devoted to basic research in biomedical informatics. Dr. Hersh will be collaborating with new DMICE faculty Stephen Wu, PhD, Adjunct Assistant Professor, as well as colleagues from Mayo Clinic, led by overall project PI, Hongfang Liu, PhD of Mayo Clinic. Both institutions will investigate techniques to use data from 100,000 patients each in their EHR systems for the task of cohort discovery, i.e., identifying patients who might be candidates for research studies.

Monday, October 6, 2014

Ebola in Texas: Who is to "Blame?"

One of the unfortunate consequences of our 24/7 cable news cycle as well as America's political polarization is that every negative event that takes place in society needs to have blame assigned to some person or organization. Sometimes the reactions to adverse news events seems to take the form of a political Rorschach Test, where an individual's reaction to the event demonstrates their underlying political views.

This was no more true recently than the unfortunate story of Thomas Eric Duncan, the man from Liberia who presented to a Dallas hospital with fever, chills, and joint pains. A nurse who saw the patient dutifully documented that the man had traveled from Liberia in the hospital's electronic health record (EHR). However, as is often the case, the physician did not see the nurse's note. The nurse failed to verbally communicate the travel history to the physician, and the physician who saw the patient did not ask about travel history. Thinking this was just a viral illness, the physician discharged the patient home. (An additional challenge with this story is that the facts keep changing. Later reports stated that the physician indeed knew about the patient's travel from Liberia. Either way, this does not change the basic premise of this posting.)

There were certainly things that were done wrong here by many people: The nurse did not verbally report the travel history to the physician. The physician did not read the nurse's note nor take a complete history from the patient. Those who implemented the EHR did not create a workflow that easily allowed the nurse's documentation to be seen by the physician. By the way, physicians not reading nurses' notes is a problem that long predates EHRs.

It would be unfortunate if the lessons learned from this episode are just figuring out who to blame, and then shaming them in the media. Our media, especially the cable news cycle that seems to thrive on pinpointing blame, with political ideologues of all stripes then chiming in with a shibboleth that indicates to which ideology they belong. And of course, the situation is not helped by the right-wing political echo chamber that seeks to tie everything-Obama to every possible adverse news event. It is fascinating to scroll through the readers' comments on various news sites and see how easily people make the "obvious" connections between this event and Obamacare, illegal immigration, the threat of terrorism, and so forth.

The reality is that although the US healthcare and public health systems are far from perfect, we do have the means to isolate and prevent the spread of Ebola. By the same token, we need to remember that the majority of people who walk into emergency departments with fever and joint pains do not have Ebola. In fact, we run the risk now of excessive testing and other resource use because of this one case.

A good outcome of this unfortunate episode would be our learning from it, and figuring out how to build systems of care, which include use of EHRs, that make sure front-line healthcare professionals do not miss cases like this while not interfering with the assessment of the overwhelming majority of routine cases of fever and joint pains that are from more common causes than Ebola. It might even be nice to have the means to prevent the spread of untruthful memes about cases like this, but I am not overly optimistic.

Tuesday, September 30, 2014

Milestones and Greatest Hits for the Informatics Professor Blog

In recent months, this blog has hit several numerical milestones. Over the summer, the blog surpassed 200,000 page views since its inception in January, 2009. The blog now has over 400 followers who regularly get updated about new postings, not to mention those who follow it via Twitter feeds (@OHSUInformatics and @williamhersh, and their numerous retweets), Facebook postings (myself and the various OHSU groups), and a number of sites that repost entries (HITECH Answers, Health Data Management, the American College of Physicians, and others). In addition, the blog recently surpassed 200 postings dating back to early 2009. I am not a "stream of consciousness" type of blogger, but instead only post when I believe I have something interesting and coherent to say.

Perhaps this is a time to reflect back and consider, what are this blog's "greatest hits?" Many of my postings have news pegs that lose longevity over time. But others I consider to be essays of more enduring value. Here is a list of those, which I might consider my all-time greatest hits (and not necessarily those with the most page views):

Thursday, September 25, 2014

Continued Good News for the Health IT Workforce

The job and career opportunities in health information technology (HIT) continue to grow, even though we are reaching the end of the "stimulus" of the Health Information Technology for Economic and Clinical Health (HITECH) Act. Two recent surveys from HIMSS Analytics and show that the bullish attitude I maintain about jobs and careers in HIT and informatics is warranted.

The HIMSS Analytics Survey queried 200 senior executives form healthcare provider and vendor organizations. About 79% reported plans to hire in the following year in last year's (2013) survey,with 84% reporting that they did actually hire during that year. About 82% report planning to hire in the coming year, with about half planning to hire 1-5 FTE and the remainder planning to hire more (10% plan to hire more than 20 FTE!).

The top hiring needs for provider organizations in the past year were in:
  • Clinical Application Support - 64%
  • Help Desk - 57%
  • IT Management - 45%
  • Project Management - 35%
  • IT Security - 34%
The top hiring areas for vendors and consultants were:
  • Sales/Marketing Team - 78%
  • Field Support Staff - 75%
  • Support Staff - 73%
  • Executive Team - 60%
Similar to other surveys in the past, this one continued to show ramifications to organizations due to lack of adequate or qualified staff. About 35% of organizations reported projects being put on hold due to lack of staff, with 38% reported scaling back IT projects for the same reason.

The survey focused more on salaries. It found an average salary of near $90K, with 30% of respondents reporting receiving a bonus at an average of around $13K. Salaries were highest among the following types of positions:
  • Project managers - $111K
  • Healthcare informatics - $94K
  • Systems analyst - $82K
  • Implementation consultant - $81K
  • Clinical applications - $78K
  • Training - $74K
Not surprisingly, salary increased with experience and was also higher for those with healthcare IT experience ($89K) than without ($54). Certification was also associated with higher earnings. Salary varied by geographic region (highest in the Mid-Atlantic and lowest in the Midwest and Southeast) and by EHR vendor experience (highest for Epic and lowest for Allscripts and Meditech). About 80% reported job satisfaction, with the most common reasons being ability to learn new skills, ability to advance careers, and income potential.

These surveys show that informatics continues to be a rewarding career, with good pay and strong job satisfaction. Nothing is certain in healthcare, but the opportunities for careers in informatics will likely be strong in the foreseeable future.

Tuesday, September 23, 2014

Clinfowiki Returns to OHSU

Back in 2005, when he was still a faculty member at Oregon Health & Science University (OHSU), Dean Sittig, PhD established the Clinical Informatics Wiki (ClinfoWiki), a wiki devoted to topics in Clinical Informatics. The Clinfowiki site has been popular over the years, accumulating over 11 million page views. The building out of Clinfowiki was achieved in part by content added by OHSU students for their course project in Clinical Information Systems (BMI 512), a course in OHSU's biomedical informatics graduate program.

When Dr. Sittig moved on to become a Professor at the University of Texas School of Health Information Sciences at Houston, he maintained his role in Clinfowiki but also brought on help from Vishnu Mohan, MD, MBI, a new informatics faculty at OHSU. Dr. Mohan took over teaching the Clinical Information Systems course and continued the Clinofwiki assignment in the class. Some of Dr. Sittig's students at his new university added content as well, as did people from other places who signed up for editing privileges.

Through the course and others who have added content, the wiki currently contains 866 content topics, with over 3000 pages of information. Over 1000 registered users have contributed over 16,500 page edits since Clinowiki was launched.

For this who wish to add or modify content, the Log In/Create Account link at the top right of the screen provides access to a form where individuals can request an account with editing privileges.

Clinfowiki, like many good wikis, represents a stellar example of collaborative knowledge resource development. We hope to see it continue to grow and serve as a useful resource in clinical informatics.

Sunday, September 14, 2014

Efficacy Is Not Leading to Effectiveness: The Dichotomy of Health Information Technology

I often get involved in debates about the value of health information technology (HIT) interventions in healthcare. While the optimist in me likes to point to the growing body of scientific evidence showing efficacy, the realist in me takes seriously the negative outcomes that some studies as well as reported experiences show. This leads to a question that some may ask, which is why does there exist this apparent dichotomy of scientific evidence supporting the use of HIT in the face of widespread dissatisfaction with it in many settings?

A number of "negative" studies have appeared in recent months [1, 2], although these studies have some significant methodologic limitations that I will describe further below. In addition, the scientific basis for use of HIT remains strong. Systematic reviews in recent years have concluded its value, whether approached from the standpoint of clinical outcomes [3] or meaningful use criteria [4]. Nonetheless, there is widespread dissatisfaction among many users of HIT, especially physicians, as exemplified in a couple surveys published by the magazine Medical Economics last year [5]. The advocacy of esteemed groups such as the Institute of Medicine for more study and regulation around HIT safety demonstrates that such problems are real [6].

While some in the informatics field point to more nefarious reasons for this apparent dichotomy, such as financial motivations by those who stand to benefit, i.e., EHR vendors, I believe that HIT has a fundamental difficulty in translating efficacy into effectiveness. The difference between efficacy and effectiveness is a well-known concept in clinical epidemiology, and is best demonstrated that some clinical interventions (tests, treatments, etc.) work well in highly controlled settings, such as well-resourced academic medical centers or when limited to patient populations that lack co-morbid conditions that most patients in the healthcare system typically have [7].

It is also worthwhile to delve further into the methodology of some of these negative studies, especially in the current highly charged political environment around HIT, including its role in healthcare reform. Take the study of Samal et al. [1]. This investigation compared the quality of care as measured by performance on mostly process-based quality measures in a single organization between physicians who achieved Stage 1 of meaningful use vs. those who did not. There are all sorts of issues whether quality measures unrelated to an EHR intervention are a good measure of an EHR system's value. There is also an inconsistent relationship between performance on quality measures and patient outcomes from care [8].

The study by McDonald et al. surveyed internal medicine physicians about various aspects of EHR use, such as whether it added or diminished free time [2]. Nearly 60% of respondents indicated EHR use reduced free time by an average of 77.5 minutes per day. Although many other variables were assessed, such as EHR vendor as well as practice size and setting, there was no analysis of which of these factors may have impacted free time. In particular, it would be interesting to compare the 60% who reported losing time with the 15% who said EHRs made them more efficient and the 26% who said that the time change was neutral. What was it about the physicians who did not lose time with their EHRs that made them different from their colleagues who claimed lost time? Was it their vendor? Or their practice situation or size? Or maybe even the availability of clinical informatics expertise guiding them.

Another concern about this study is that it was a recall-based survey. What would have been more useful was the use of real time-motion studies. These have been done in the past, and the added time is minimal [9]. It would also have been good to ask these physicians if they wanted to return to the days of paper records, with their illegibility, inaccessibility, and other problems.

I am in no way arguing that negative studies of EHR should be discounted. But like all areas of scientific study, we must weigh all the evidence. It is clear that a major challenge to HIT is how to translate efficacy into effectiveness. This requires research looking at why its benefits are not readily generalizable to different settings. Such studies need to assess all possible factors, from healthcare setting type to physician characteristics to the availability of suitable informatics expertise. We must also not lose sight of what we are trying to improve with HIT, namely a healthcare system that is unsafe, wasteful, and achieves suboptimal outcomes [10].


1. Samal, L, Wright, A, et al. (2014). Meaningful use and quality of care. JAMA Internal Medicine. 174: 997-998.
2. McDonald, CJ, Callaghan, RM, et al. (2014). Use of internist's free time by ambulatory care electronic medical record systems. JAMA Internal Medicine: Epub ahead of print.
3. Buntin, MB, Burke, MF, et al. (2011). The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Affairs. 30: 464-471.
4. Jones, SS, Rudin, RS, et al. (2014). Health information technology: an updated systematic review with a focus on meaningful use. Annals of Internal Medicine. 160: 48-54.
5. Verdon, DR (2014). Physician outcry on EHR functionality, cost will shake the health information technology sector. Medical Economics, February 10, 2014.
6. Anonymous (2012). Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC, National Academies Press.
7. Singal, AG, Higgins, PDR, et al. (2014). A primer on effectiveness and efficacy trials. Clinical and Translational Gastroenterology. 5: e45.
8. Houle, SK, McAlister, FA, et al. (2012). Does performance-based remuneration for individual health care practitioners affect patient care?: a systematic review. Annals of Internal Medicine. 157: 889-899.
9. Overhage, JM, Perkins, S, et al. (2001). Controlled trial of direct physician order entry: effects on physicians' time utilization in ambulatory primary care internal medicine practices. Journal of the American Medical Informatics Association. 8: 361-371.
9. Smith, M, Saunders, R, et al. (2012). Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC, National Academies Press.

Saturday, September 6, 2014

Unscrambling Eggs and the Need for Comprehensive Data Standards and Interoperability

Two local informatics-related happenings recently provided teachable moments demonstrating why a comprehensive approach to standards and interoperability is so critical for realizing the value of health IT. Fortunately, the Office of the National Coordinator for Health IT (ONC) has prioritized interoperability among its activities moving forward, and other emerging work on standards provides hope that the problems I will described that occurred locally (and I know occur many other places) might be avoided in the future.

One of the local happenings came from a cardiology-related project that has been trying to improve performance on quality of care measures. As a starting point, the cardiology group wanted to precisely identify the measure of left ventricular ejection fraction (LVEF) from data in its organization's electronic health record (EHR) system. LVEF is an important number for stratifying patients with congestive heart failure (CHF), thus allowing better assessment of the appropriateness of their medical management. The value for LVEF is a number that can be measured in multiple ways, most commonly via an echocardiogram test that uses sound waves to show contraction of the heart wall muscles.

One might think that recording LVEF in an EHR is a relatively straightforward task. Unfortunately, the number itself is not always reported as a single number, but sometimes as a range (e.g., 35-40%) or as a cut-point (e.g., < 25%). Furthermore, different physician groups in the organization (e.g., cardiologists, family physicians, internists, etc.) tend to report LVEF in different stylistic ways. An obvious solution to recording LVEF consistently and accurately might be to designate a specific field in the EHR, although getting all clinicians and technicians in an organization to use such a field properly is not always easy.

The second happening came from a cancer-related project. This institution's cancer center treats both patients who receive all their care within the institution as well as those who are referred from external practices or centers. While the patients getting all their care in the institution have laboratory data in the institutional EHR, the latter come with records that are formatted in different ways in different types of media. Data come in a whole gamut of forms, from being structured electronically to residing in semi-formatted electronic documents to being on scanned document images (PDFs). With the move to personalized medicine, the cancer center desires every data point in electronic form. Even when data are in somewhat structured electronic forms, there is inconsistent use of standards for formatting of data and/or naming of tests. While standards such as LOINC provide format and terminology standardization, not all centers use it, which results in inconsistent formatting and naming of structured data.

Seeking solutions for having lab data in a more consistent format and structure, an external developer was engaged and demonstrated software tools, including those using natural language processing (NLP), that it could employ to decode the data and put into standardized form. There is no question that the cancer center needs to get the data it requires here and now, but it really should not be necessary and would be an unneeded expense if the healthcare industry were to adopt and universally use standards for laboratory and other data. It is unfortunate that healthcare organizations have to spend money on a decoding process that can be likened to unscrambling an egg. It is a waste of time and money to try to reconstitute data that was once structured in a laboratory information system or EHR, and is now in free-text form, or even worse in a scanned image.

This problem is unfortunately not unique to laboratory data. This same problem applies to other types of data, such as pharmacy data, which not only has the same naming and formatting problems but also the addition of data provenance, i.e., what does the data mean. We know that there is drop-off in the proportion of patients who are given prescriptions and those who actually fill them, and then another drop-off among those who fill prescriptions and who actually take the medication [1]. Determining that a patient is actually taking a drug is not a simple matter of seeing if it was mentioned in the physician plan, generated as a prescription, or even filled at a pharmacy. This impacts all aspects of care, but especially downstream applications of the data removed from the care process, such as research or quality measurement.

Therefore while NLP can certainly help in decoding some aspects of the medical record, I believe it is a waste of time and money to try to use it to unscramble eggs. This is another reason why the need for data to adhere to standards and to be interoperable is becoming imperative.

Fortunately, interoperability has become a major priority for ONC, which has launched a process to develop a "shared, nationwide roadmap" to achieving it. This process began earlier in 2014 with the release of a 10-year vision to achieve an interoperable health infrastructure [2]. Subsequently, a process has been launched to develop an explicit roadmap with milestones for three, six, and ten years [3].

Many factors spurred the ONC into action. One was a report last year noting that while adoption of EHRs has been very high, especially in hospitals, there has been much less uptake of health information exchange (HIE) [3]. In addition, earlier this year, a report commissioned by the Agency for Healthcare Quality & Research (AHRQ) was produced by JASON, an independent group of scientists that advises the US government on science and technology issues [4]. The JASON report noted many of the flaws in the current health IT environment, especially the factors impeding interoperability and, as a result, HIE. Part of the ONC action includes a task force to address the issues raised by the JASON report.

The JASON report laments the lack of an architecture supporting standardized application programming interfaces (APIs), which allow interoperating computer programs to call each other and access each other's data. The report also criticizes current EHR vendor technology and business practices, which they call impediments to achieving interoperability. The report recommends a new focus on creating a "unifying software architecture" that will allow migration of data from legacy systems to a new "centrally orchestrated architecture" that will better serve clinical care, research, and patient uses. It proposes that this architecture be based on a set of public APIs for access to clinical documents and discrete data from EHRs, combined with increased consumer control of how data is used.

In addition, the JASON report advocates a transition toward more finely granular data, which the task force views as akin to going from structured documents, such as Consolidated Clinical Document Architecture (CCDA), to more discrete data elements. One new standards activity that may enable this move to more discrete data that is formatted in consistent ways is Fast Health Interoperability Resources (FHIR) [5]. FHIR is viewed by some as an API into structured discrete elements that presumably will adhere to terminology standards, thus potentially playing a major role in efforts to achieve data interoperability [6]. The HL7 Web site has a very readable and informative overview of FHIR from a clinical perspective [7].

It is easy to see how the interoperability work described in the second half of this posting, if implemented properly and successfully, could go a long way to solving the two problems described in the first half. Having a reliable way to define the format and naming of LVEF and laboratory results would enable cardiology groups to improve (among other things) quality measurement and oncology groups to march forward toward the vision of personalized medicine.


1. Tamblyn, R, Eguale, T, et al. (2014). The incidence and determinants of primary nonadherence with prescribed medication in primary care: a cohort study. Annals of Internal Medicine. 160: 441-450.
2. DeSalvo, KB (2014). Developing a Shared, Nationwide Roadmap for Interoperability. Health IT Buzz, August 6, 2014.
3. Anonymous (2013). Principles and Strategy for Accelerating Health Information Exchange (HIE). Washington, DC, Department of Health and Human Services.
4. Anonymous (2014). A Robust Health Data Infrastructure. McLean, VA, MITRE Corp.
5. Slabodkin, G (2014). FHIR Catching On as Open Healthcare Data Standard. Health Data Management, September 4, 2014.
6. Munro, D (2014). Setting Healthcare Interop On Fire. Forbes, March 30, 2014.
7. Anonymous (2014). FHIR for Clinical Users. Ann Arbor, MI, Health Level 7.

Tuesday, August 26, 2014

Beyond Prediction: Data Analytics/Data Science/Big Data Must Demonstrate Value

One of my ongoing concerns for data analytics/data science/Big Data in biomedicine and health is that despite the growth of articles and other writing, the accomplishments of using these tools, especially as would be documented in peer-review journals, continues to be small. I am as enthusiastic as anyone about the prospects for harnessing the growing quantity of data in our operational electronic health record (EHR) and other systems for improving health, healthcare, and research. Yet I also believe that we need to be careful that our enthusiasm does not lead to overselling or outright hype, and that we must demonstrate the value for using data just as we would any other clinical process or tool.

There have been some news reports of the value of using Big Data. However, it would be better to see peer-review publication of such results. From the news, it has been reported that two states, Wyoming and Washington, have shown reduced emergency department visits using data-based methods, while Beth Israel Deaconess Hospital has used data as part of an effort that has helped reduce hospital readmissions by 25%. Another earlier news article reported that IBM Watson has learned from data how to diagnose cancer more accurately than physicians, although when I emailed the physician to whom that quote of its success was attributed, he replied that he had never said it (Samuel Nussbaum, email communication, July 28, 2014).

There also continues to be a spate of well-done research demonstrating the predictive value of data. Just this past week, as I was preparing this post, two interesting and informative studies of prediction came across the wire, one looking at risk for metabolic syndrome in a database of 36,944 individuals maintained by a large health insurer [1] and another looking at prediction of hospital readmission [2]. These studies are important, but all of this must be followed with implementation of approaches that make use of data to show real benefit, such as improved patient outcomes, improved health, or even cost efficiency. The best study to date I am aware of applying predictive data analytics in an effort to improve outcomes was unable to show benefit [3]. Maybe I am wrong that other studies demonstrating the application of Big Data techniques have shown benefit (or have been done at all), and I will certainly stand corrected if there are.

Despite the lack of studies demonstrating benefit, there have been plenty of interesting writings about Big Data. Some publications that have even devoted issues or volumes to the topic. One of these was the July issue of the health policy journal, Health Affairs. There were a number of interesting articles in the issue, although none reported any research results demonstrating the value of Big Data. Among the interesting papers were:
  • Bates et al. detailing what they consider the six most important use cases for Big Data: high-cost patients, readmissions, triage, patient decompensation, adverse events, and treatment optimization for diseases affecting multiple organ systems [4]
  • Krumholz describing the need for new thinking and training (including informatics) in the application of Big Data [5]
  • Curtis et al. discussing four large national multi-purpose data networks that could have substantial impact [6]
  • Longhurst et al. presented the concept of the "Green Button," a tool in the EHR that would aggregate data in an attempt to answer clinical questions for which no prior evidence existed [7]
Also appearing recently was the 2014 Yearbook of Medical Informatics, which is now available via open-access publishing and was devoted this year to the topic of Big Data. Similar to the Health Affairs issue, there were several interesting papers (including one of which I was a co-author that focused on how informatics education must adapt to Big Data [8]) but none reporting patient or organizational benefits of Big Data.

There also continues to be a steady stream of other papers related to re-use of clinical data that provide insights or demonstrate the challenges to working it. Two of these papers come from a recent special issue of Journal of the American Medical Informatics Association (JAMIA) devoted to "high-throughput phenotyping." A paper by Richesson et al. documents the challenges in something so seemingly simple as definitively determining patients diagnosed with diabetes mellitus [9]. Another paper by Pathak et al. documents the detailed work required to standardize and normalize data in the EHR for a single quality measure assessing a serum cholesterol levels below 100 mg/dL for patients with diabetes mellitus [10]. Other recent papers in JAMIA have documented the challenges with the quality of diabetes-related data used for quality indicators in primary care [11] and the significant quantity of non-conformance with the details of the Consolidated Clinical Document Architecture (C-CDA) that undermine interoperability [12].

Despite the slow progress, I am still confident that we will see scientific advances around data analytics/data science/Big Data in biomedicine and health. I agree with Cathy O'Neil, who writes that we should be "skeptics, not cynics" about Big Data [13]. In other words, we should approach data, and the results obtained from it, with informed skepticism. I reiterate what I have written in the past, that we must put data to use in ways that demonstrate benefit, apply a research mentality, and take into account the "provocations" of Dana Boyd, the most important of which is that we must not let the data define our questions of it, and instead seek data that will best answer our questions [14].


1. Steinberg, GB, Church, BW, et al. (2014). Novel predictive models for metabolic syndrome risk: a “big data” analytic approach. American Journal of Managed Care. 20: e221-e228.
2. Hebert, C, Shivade, C, et al. (2014). Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study. BMC Medical Informatics & Decision Making. 14: 65.
3. Amarasingham, R, Patel, PC, et al. (2013). Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study. BMJ Quality & Safety. 22: 998-1005.
4. Bates, DW, Saria, S, et al. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs. 33: 1123-1131.
5. Curtis, LH, Brown, J, et al. (2014). Four health data networks illustrate the potential for a shared national multipurpose big-data network. Health Affairs. 33: 1178-1186.
6. Krumholz, HM (2014). Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Affairs. 33: 1163-1170.
7. Longhurst, CA, Harrington, RA, et al. (2014). A 'green button' for using aggregate patient data at the point of care. Health Affairs. 33: 1229-1235.
8. Otero, P, Hersh, W, et al. (2014). Big Data: Are Biomedical and Health Informatics Training Programs Ready? Yearbook of Medical Informatics 2014. C. Lehmann, B. Séroussi and M. Jaulent: 177-181.
9. Richesson, RL, Rusincovitch, SA, et al. (2013). A comparison of phenotype definitions for diabetes mellitus. Journal of the American Medical Informatics Association. 20: e319-e326.
10. Pathak, J, Bailey, KR, et al. (2013). Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium. Journal of the American Medical Informatics Association. 20: e341-e348.
11. Barkhuysen, P, deGrauw, W, et al. (2014). Is the quality of data in an electronic medical record sufficient for assessing the quality of primary care? Journal of the American Medical Informatics Association. 21: 692-698.
12. D'Amore, JD, Mandel, JC, et al. (2014). Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative. Journal of the American Medical Informatics Association. Epub ahead of print.
13. O'Neil, C (2013). On Being a Data Skeptic. Sebastopol, CA, O'Reilly.
14. Boyd, D and Crawford, K (2011). Six Provocations for Big Data. Cambridge, MA, Microsoft Research.

Monday, August 25, 2014

Healthy Living is Not Alternative Medicine, and Vice Versa

Part of my original interest in a medical career emanated from my interest in personal health. Starting with being a distance runner in high school, developing an interest in nutrition in college, and taking charge of my middle-age weight gain a decade ago, I have always been interested in healthy living.

My early interest in health also led me to develop an interest in complementary and alternative medicine (CAM). In fact, CAM was part of what led me to a medical career, as my initial interest in computers starting in high school waned while the anti-establishment appeal of CAM attracted me as a college student in the late 1970s. Even my attraction to evidence-based medicine (EBM) comes from an adage that appealed to me from the CAM world, which was, "Let truth be your authority, not authority be your truth."

I had a resurgence of interest in the 1990s when the National Institutes of Health (NIH) ramped up its National Center for Complementary and Alternative Medicine (NCCAM) in an effort to bring some scientific rigor and objectivity to the study of CAM. I became involved in some CAM research and education activities at Oregon Health & Science University (OHSU).

Alas, I think it is fair to say that the evidence for CAM interventions still seems to be wanting. I recognize there are limits to EBM and its main tool, the randomized controlled trial (RCT), in assessing CAM therapies. But there is no reason why some CAM therapies should not show some success in RCTs. However, when put to objective evidence-based testing, most of the major CAM therapies do not hold up, including homeopathy [1], acupuncture [2], and antioxidant supplements [3]. While some may argue that taking vitamin supplements is not CAM, they too show no benefit in primary prevention of disease [4]. A number of science-based books have also reviewed the evidence base for CAM and explained research findings in lay terms [5, 6]. One of the most prolific science-based reviewers of CAM studies is Edzard Ernst MD, PhD, a physician formerly employing homeopathy whose Web site is a running commentary on CAM studies and their interpretation.

There is also increasing criticism of the research funding allocated to NCCAM. There is concern not only that NCCAM studies are not justified by the underlying science, but also that few of the studies, especially RCTs, actually have their results published [7, 8]. This has led some prominent researchers to argue that we need science-based evidence more than evidence-based medicine, i.e., RCTs are other evaluative studies that are based on sound scientific underpinnings [9]. (Biological plausibility is one of the tenets of evidence-based medicine, but seems to get lost in the desire to satisfy the clamoring for studies of CAM.)

To their credit, advocates of CAM have always been among the loudest proponents of healthy living, although I have seen my share of exceptions in CAM practitioners who eat poor diets, smoke, or otherwise live unhealthfully. I see even more of the disconnection between use of CAM and healthy living in individuals, who somehow view CAM as insurance against disease from poor health habits.

Unlike most CAM, however there is evidence to support the health benefits of true healthy living. This is very distinct from health benefits of CAM. I do not view healthy living as some form of "alternative medicine." I also recognize that not all health problems are due to poor lifestyle choices. While I am confident that my healthy lifestyle will likely contribute to my better health and longevity, I know there are plenty of medical conditions that have little to do with lifestyle.

So in contrast to CAM, there is very good evidence on a number of fronts that healthy living is associated with better health and longevity. Last year the American College of Cardiology/American Heart Association published a comprehensive guideline to lifestyle interventions for reducing risk of cardiovascular disease [10]. The underlying systematic review exhaustively identifies the evidence supporting diets emphasizing fruits, vegetables, whole grains, low-fat dairy products, lean meats, nontropical vegetable oils and nuts, and legumes, while limiting sweets, sugary beverages, and red meat [11]. The systematic review also finds evidence for limiting saturated and trans fats as well as sodium. It also finds benefit for moderate-to-vigorous intensity exercise 3-4 times per week lasting 40 minutes per session.

Although the evidence for healthy living in enhancing longevity and reducing disease is strong, it is not ironclad, as it is very difficult to perform controlled trials of healthy living. Therefore a good deal of the evidence comes from large-scale observational studies. But this evidence is solid, including an aptly titled study from Europe, Healthy Living is the Best Revenge [12], and a recent analysis that running, an activity I enjoy, reduces all-cause and cardiovascular risk mortality [13]. The latter reaffirms the U-shaped curve showing the most benefit for moderate amounts of running akin to the level I do, i.e., between 9-19 miles per week.

Another adage that resonates with me is that while a simple healthy lifestyle is beneficial, there is little evidence-based (i.e., coming from RCTs and other strong evidence) data for many foods and supplements that are dubbed "miracles." I agree with Katz, who has written a book extolling the virtues of simple healthy living via the diet and exercise regimens supported by the evidence, along with avoidance of smoking [14]. (His advice makes me think, without evidence to support it, that there are diminishing returns from more and more devotion to minutiae of good diet, and that the simple basics probably get you most of the way toward the best health returns.) In addition, like many, I got a kick out of the recent grilling of Dr. Mehmet Oz in the US Senate [15].

The scientific evidence clearly supports the benefits of healthy living, while for the most part lacking it for alternative medicine. It is important to distinguish these two, and also to remember that even the healthiest lifestyle will not prevent all disease. For these reasons, there is still a role for conventional medicine and the research underlying it. I will personally continue to live healthfully, even if I know this will not provide immunity from all illness.


1. Ernst, E (2010). Homeopathy: what does the “best” evidence tell us? Medical Journal of Australia. 192: 458-460.
2. Madsen, MV, Gøtzsche, PC, et al. (2009). Acupuncture treatment for pain: systematic review of randomised clinical trials with acupuncture, placebo acupuncture, and no acupuncture groups. British Medical Journal. 338: a3115.
3. Bjelakovic, G, Nikolova, D, et al. (2013). Antioxidant supplements to prevent mortality. Journal of the American Medical Association. 310: 1178-1179.
4. Fortmann, SP, Burda, BU, et al. (2013). Vitamin and mineral supplements in the primary prevention of cardiovascular disease and cancer: An updated systematic evidence review for the U.S. Preventive Services Task Force. Annals of Internal Medicine. 159: 824-834.
5. Offit, PA (2013). Do You Believe in Magic?: The Sense and Nonsense of Alternative Medicine. New York, NY, Harper.
6. Singh, S and Ernst, E (2009). Trick or Treatment?: Alternative Medicine on Trial. London, England, Corgi.
7. Atwood, KC (2013). The Ongoing Problem with the National Center for Complementary and Alternative Medicine. Skeptical Inquirer, September / October 2013.
8. Mielczarek, EV and Engler, BD (2014). Selling Pseudoscience: A Rent in the Fabric of American Medicine: A Study of Federal Funding Advancing Naturopathy, Acupuncture, Chiropractic, and Energy Healing as Acceptable Medical Protocols Finds Troubling Misuse of Taxpayer Dollars. Skeptical Inquirer, May/June, 2014.
9. Gorski, DH and Novella, SP (2014). Clinical trials of integrative medicine: testing whether magic works? Trends in Molecular Medicine. Epub ahead of print.
10. Eckel, RH, Jakicic, JM, et al. (2014). 2013 AHA/ACC Guideline on Lifestyle Management to Reduce Cardiovascular Risk. Journal of the American College of Cardiology. 129: S76-S99.
11. Eckel, RH, Jakicic, JM, et al. (2013). 2013 Report on Lifestyle Management to Reduce Cardiovascular Risk: Full Work Group Report Supplement. Journal of the American College of Cardiology. 129: Supplement.
12. Ford, ES, Bergmann, MM, et al. (2009). Healthy living is the best revenge: findings from the European Prospective Investigation Into Cancer and Nutrition-Potsdam study. Archives of Internal Medicine. 169: 1355-1362.
13. Lee, DC, Pate, RR, et al. (2014). Leisure-time running reduces all-cause and cardiovascular mortality risk. Journal of the American College of Cardiology. 64: 472-481.
14. Katz, DL (2013). Disease-Proof: The Remarkable Truth About What Makes Us Well. New York, NY, Hudson Street Press.
15. Haiken, M (2014). Dr. Oz's 10 Most Controversial Weight Loss Supplements. Forbes Magazine, June 18, 2014.

Friday, July 25, 2014

Proposing the Addition of a Standard Occupational Classification (SOC) Code for Informatics

About a decade ago, as my interests and work activity began to focus more on informatics education and workforce development, I started to ask questions about the size, scope, and required education of that workforce. Despite seeing great interest in the informatics education programs at Oregon Health & Science University (OHSU), I could find very little data about how many people were working in the field, how many more were needed, what their job activities were, or what knowledge and skills they required. I noted these problems in the first paper on this topic I published [1], and then tried to answer some of the questions on the size of the workforce with the best data I could find, which was the HIMSS Analytics Database. This led to my widely publicized finding of a need for at least 40,000 more health information technology professionals [2], which was part of the motivation for including workforce development in the Health Information Technology for Economic and Clinical Health (HITECH) Act. At the same time, I was learning that many human resources (HR) professionals were unaware of the background and skills of those working in the growing number of clinical informatics roles in healthcare organizations.

One reason for all these problems was the lack of informatics being visible in federal labor statistics. In particular, there was no Standard Occupation Classification (SOC) code for informatics. I came to learn that the importance of such codes cannot be underestimated, as they define the labor statistics maintained by the US government. They also are used by others, such as Human Resources (HR) departments in organizations to classify job offerings.

There is one code that is somewhat related to informatics, and sometimes used to point to workforce needs: 29-2071 Medical Records and Health Information Technicians. The occupations described by this code are those that "compile, process, and maintain medical records of hospital and clinic patients in a manner consistent with medical, administrative, ethical, legal, and regulatory requirements of the health care system. Process, maintain, compile, and report patient information for health requirements and standards in a manner consistent with the healthcare industry's numerical coding system." However, this code refers to the relatively low-level work of coding and maintaining medical records, and not the myriad of activities carried out by informatics professionals.

The SOC system is maintained by the US Bureau of Labor Statistics (BLS) and is revised periodically with a multi-year process. The last update was in 2010, and the informatics field was not organized enough to pursue a revision. The next update will be in 2018, and a few months ago, the government made its first call for public input for modifications to the SOC 2010 system, with recommendations by this past Monday, July 21st. For over the last year, I have been part of a team of individuals and groups (ONC, AHIMA, AMIA, and HIMSS) working to propose the inclusion of the health informatics occupation into the SOC. Our letter was submitted this week, with an AMIA press release noting the large and diverse groups supporting the inclusion.

In the process of preparing the letter, I learned a great deal about the SOC system and the process for revising it. SOC codes are supposed to describe occupations more than specific jobs. There need to be substantial numbers of people in the occupation, which must be unique from others in the SOC. The classification unfortunately has a single hierarchy, which makes it difficult to represent occupations that cross boundaries, such as health informatics. But in the end, the overwhelming sentiment of the group, one I strongly advocated as well, was that health/biomedical/clinical informatics is primarily a health professional occupation and not a computing occupation. Therefore, our overall recommendation was to add a new Health Informatics occupation residing under the major group, 29-0000 Healthcare Practitioners and Technical Occupations.

I was also pleased with several other aspects of the letter:
  • It notes that while we are asking to call the new occupation "health informatics," there are other terms, such as "biomedical informatics" and "clinical informatics," which are used to describe this occupation, and all of these all refer to the same general occupation of "health informatics."
  • There is inclusion of discussion about the new clinical informatics physician subspecialty, which not only demonstrates that informatics is important to medicine (and all health professions) but that it was not unique to any primary medical specialty.
  • It calls out the large and growing number of informatics educational programs, most of which are at the graduate level.
As noted on the BLS site, there are many more steps for revision the 2018 SOC. But it has been made clear from leaders in the field that there is an important occupation of health informatics, which is a health profession that should be included in the SOC.


1. Hersh, WR (2006). Who are the informaticians? What we know and should know. Journal of the American Medical Informatics Association. 13: 166-170.
2. Hersh, WR and Wright, A (2008). What workforce is needed to implement the health information technology agenda? An analysis from the HIMSS Analytics™ Database. AMIA Annual Symposium Proceedings, Washington, DC. American Medical Informatics Association. 303-307.

Wednesday, July 9, 2014

Competencies in Clinical Informatics for Medical Education

I wrote last year about efforts at Oregon Health & Science University (OHSU) to introduce content on clinical informatics as part of its revision of its medical school curriculum. Physician competence in clinical informatics is important for a number of reasons, such as the continuously expanding knowledge base of medicine, the need for care provided to be more accountable, and the desire of patients to interact electronically with the healthcare system the way they interact with other industries (such as retailers and banks). An additional reason for such competence for some physicians is the career opportunity provided by the designation of clinical informatics as a new medical subspecialty.

In order to integrate more clinical informatics into OHSU's curriculum, we established a working group of informatics faculty leaders to develop a set of competencies in clinical informatics. We aimed to go beyond the usual searching and basic EHR skills that increasing numbers of medical schools provide. We also wanted to focus less on mastery of the technology and more on the tasks for which it is used.

From the broad competencies, we also developed specific learning objectives and milestones, an implementation schedule, and mapping to general competency domains. After producing this material, we believed there would be value in publishing our work in a peer-reviewed journal. By doing so, we hoped that this work, and the resulting curricula, will be evaluated by ourselves and our colleagues. To this end, our published paper has just appeared (1). We chose to publish in an open-access journal so everyone can access the paper, and the publisher even provides a video abstract describing the work.

Our next steps involve implementing this new portion of our medical school curriculum. We also hope to evaluate our effort as well as learn from others who are adopt, modify, and/or evaluate our approach.

I might add that there is nothing about this work is highly specific to medical students. In other words, the competencies we have developed likely apply to all health professions students, i.e., nurses, physician assistants, pharmacists, etc.. For that matter, they also should apply to non-clinical students, e.g., health administration, public health, and so forth.


1. Hersh WR, Gorman PN, Biagioli FE, Mohan V, Gold JA, Mejicano GC. Beyond information retrieval and electronic health record use: competencies in clinical informatics for medical education. Advances in Medical Education and Practice. 2014, 5: 205-212.

Thursday, July 3, 2014

Advice to a Young Person Considering a Career in Informatics

One of the biggest challenges I face in introducing potential students to the myriad of career opportunities in biomedical and health informatics that potentially await them comes with young people. I believe that the main reason for this is this group's little exposure to our healthcare system and its myriad of problems and challenges. Like most young people, they tend to be healthy and have had very little experience with healthcare and other health-related areas. While there is little difficulty in explaining the problems that informatics tries to solve to older individuals, perhaps whose parents or children have been impacted by healthcare, or who are among the myriad of mid-career students who already work in healthcare, it is considerably more challenging to introduce someone to the importance of informatics who has had little interaction with healthcare. I was recently invited to write a chapter on the topic of introducing young people to the study of informatics, and a co-editor of the book has allowed me to reproduce it in my blog. The book will be published as: Vaidya, K., Soar, J. [eds.] 2015. Health Informatics for the Curious: Why Study Health Informatics. Canberra, Australia. Forthcoming [ISBN 978-1-925128-71-0]. What follows is an edited version of my draft chapter.

While society will always need professionals who provide hands-on care of patients, there are growing opportunities for others in health professions to contribute to not only contribute to people’s health, but also improve the delivery of healthcare and advanced research. One such profession is biomedical and health informatics, which aims to apply information and associated technologies for the benefit of health and biomedicine.

There are many trends in healthcare that demonstrate increased need for professionals trained in informatics. It begins with the person in good health who aims to maintain their health and prevent disease. If that person becomes a patient, he or she wants to interact with the healthcare system in the same way they interact with other industries such as retail or banking, i.e., through electronically connected means. For those who work in healthcare, informatics competence is needed to function in their profession, such as accessing clinical knowledge and being guided by clinical decision support. As patients, especially those with one or more chronic illnesses, are cared for by teams of individuals from home caregivers to medical subspecialists, there will be a growing need for care teams to communicate effectively and coordinate care. Likewise, as the population disperses, systems employing telemedicine and other forms of remote communication will be required.

Moving to the population level, public health authorities need to be vigilant about health-related threats, whether natural (emerging infectious diseases) or manmade (bioterrorism). And of course we will continue to require a robust medical research infrastructure, with particular promise for data-intensive research methods, such as identifying genomic causes of health and disease or leveraging the data in the growing number of electronic health record systems. As new models of healthcare financing demand more accountability for care, information systems will be required so patients can be tracked and complications can be identified and addressed early. Some combine all of the needs described in this paragraph together into the concept of the learning health system, which continuously learns based on accumulated data and its analysis [1].

A common thread across all of these trends is the growing use of data and information systems. Unlike the common uses of information technology (IT), these applications are more complex, from their need to be standardized, interoperable, and reliable as well as their requirement to protect safety and individual privacy. The field that most directly addresses these issues is what I prefer to call biomedical and health informatics [2].

A variety of data points show that professionals from this discipline are in high demand. An analysis of online job postings found 226,356 positions advertised between 2007-2011 [3]. In the meantime, a survey of healthcare CIOs shows a concern for shortages of workers in this area who have the proper skills [4]. For physicians working in this area, there is now a new medical subspecialty has been designated [5]. The nursing profession has had a specialization in nursing informatics for over a decade, and we are likely to see more certifications, as the American Medical Informatics Association (AMIA) has created a task force to develop an Advanced Interprofessional Informatics Certification that will apply to all informatics professionals, not just those who are physicians and nurses.

The occupation providing the expertise and leadership in health IT is also called, for short, informatics [2]. Other adjectives sometimes appear before “informatics” in other contexts, such as clinical informatics, biomedical informatics, bioinformatics, etc., but all generally refer to the discipline working to apply information to improve health and healthcare delivery [2]. While the occupation of informatics is fundamentally a health profession, it is not just an extension of a specific healthcare field, i.e., a physician, nurse, or allied health professional who is savvy with IT. By the same token, those who work in the occupation of health informatics are not IT professionals or managers who happen to be applying general IT skills to health or healthcare settings.

This unique occupation is increasingly valued in healthcare organizations. In the United States, for example, an analysis by the Office of the National Coordinator for Health IT of a comprehensive database of 84 million online job postings to find a total of health IT-related 434,282 job postings between 2007-2011, with 226,356 health IT core jobs and 207,926 health IT-related clinical user jobs [3]. The former would contain many who work in the occupation of informatics.

Informatics is more about information than technology, with the latter being a tool, albeit an important one, to enable better use of information. The former School of Informatics at the State University of New York Buffalo defined informatics as the Venn diagram showing the intersection of people, information, and technology. Friedman has defined a “fundamental theorem” of informatics, which states that informatics is more about using technology to help people do cognitive tasks better than about building systems to mimic or replace human expertise [6]. He has also defined informatics as “cross-training,” bridging an application domain (such as public health or medicine) with basic information sciences [7].

Within informatics are a myriad of sub-disciplines, all of which apply the same fundamental science and methods, but focused on particular subject domains. As shown in the first figure below, informatics proceeds along a continuum from the cellular and molecular (bioinformatics) to the person (medical or clinical informatics) to the population (public health informatics). Within clinical informatics may be a focus on specific healthcare disciplines, such as nursing (nursing informatics), dentistry (dental informatics), pathology (pathology informatics), etc. as well as among consumers and patients (consumer health informatics). There are also disciplines in informatics that apply across the cell-person-population spectrum:
  • Imaging informatics – informatics with a focus on imaging, including the use of PACS systems to store and retrieve images in health care settings
  • Research informatics – the use of informatics to facilitate biomedical and health research, including a focus on clinical and translational research that aims to accelerate research findings into healthcare

What are the competencies required for a career in informatics? They can be grouped into three categories, as shown in the next figure below, which broadly include health/biomedical domain knowledge, information and computing science, and people/communication skills. (This is an update of a figure I have published elsewhere, e.g., [2].)

Does one need to be a clinician to be trained and effective in a job in informatics? Must one know computer programming? The answers are no and no. Informatics is a very heterogeneous field, and there are opportunities for individuals from all types of backgrounds. One thing that is clear, however, is that the type of informatics job you assume will be somewhat dependent on your background. Those with healthcare backgrounds, particularly medicine or nursing, are likely to draw on that expertise for their informatics work in roles such as a Chief Medical or Nursing Informatics Officer. Those who do not have healthcare backgrounds still have plenty of opportunities in the field, but are more likely to end up in the wide variety of other jobs that are available.

Informatics is a career for the 21st century. There are a wide variety of jobs for people with diverse backgrounds, interests, and talents, all of whom can serve the health of society through effective use of information and associated technologies. The pathway to get to that career usually involves graduate (i.e., beyond a bachelor's degree) education, and a database of such programs is available from AMIA and includes our program at Oregon Health & Science University (OHSU).


1.     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.
2.     Hersh W, A stimulus to define informatics and health information technology. BMC Medical Informatics & Decision Making, 2009. 9: 24.
3.     Schwartz A, Magoulas R, and Buntin M, Tracking labor demand with online job postings: the case of health IT workers and the HITECH Act. Industrial Relations: A Journal of Economy and Society, 2013. 52: 941–968.
4.     Anonymous, Demand Persists for Experienced Health IT Staff. 2012, College of Healthcare Information Management Executives: Ann Arbor, MI, _survey_report.pdf.
5.     Detmer DE and Shortliffe EH, Clinical informatics: prospects for a new medical subspecialty. Journal of the American Medical Association, 2014. 311: 2067-2068.
6.     Friedman CP, A 'fundamental theorem' of biomedical informatics. Journal of the American Medical Informatics Association, 2009. 16: 169-170.
7.     Friedman CP, What informatics is and isn't. Journal of the American Medical Informatics Association, 2012. 20: 224-226.

Wednesday, June 18, 2014

Eligibility for the Clinical Informatics Subspecialty, 2014 Update

One of the posts in this blog with the most page views ever is my January, 2013 description on eligibility for the clinical informatics subspecialty for physicians. No doubt part of the reason for its popularity was my using the post as a starting point for replying to those emailing or otherwise contacting me with questions about their own eligibility.

A year later, I still get such emails and inquiries. While the advice in the 2013 post is largely still correct, we have had the ensuing experience of the first year of the board exam, who qualified to sit for it, and what proportion of those taking the test passed. We can also put various educational offerings in context, not only for their content, but also for how the two boards qualifying physicians for the exam, the American Board of Preventive Medicine (ABPM) and the American Board of Pathology (ABP), viewed them in terms of eligibility to sit for the exam.

The official eligibility statement for the subspecialty is unchanged from last year and is documented in the same PDF file posted then from the ABPM (and summarized by the ABP). One must be a physician who has board certification in one of the primary 23 subspecialties. They must have an active and unrestricted medical license in one US state. For the first five years of the subspecialty (through 2018), the "practice pathway" or completing a "non-traditional fellowship" (i.e. one not accredited by the Accreditation Council for Graduate Medical Education, or ACGME) will allow physicians to "grandfather" the training requirements, i.e., take the exam without completing a formal fellowship accredited by the ACGME.

I have some observations about who was deemed eligible for the exam, although as always, let me give the standard disclaimer that ABPM and ABP are the ultimate arbiters of eligibility, and anyone who has questions should contact ABPM (for physicians in any specialty except pathology) and ABP (for physicians in pathology). I am only interpreting their rules.

One concern many had was the "nontraditional fellowship" for eligibility, in particular whether a master's degree in informatics would allow one to qualify. I argued that a master's degree alone should have qualified someone, since if nothing else, it (at least ours at OHSU) might meet the practice pathway time requirement, with educational time being "worth" one-half of the time of practice, and a master's degree being equivalent to at least 1-1/2 years of full-time study (i.e., 0.5 FTE over three years). (I also asserted last year that anyone education from OHSU would have the background to pass the exam. Experience bore me out, as at least 40 OHSU informatics alumni and current students - some qualifying by additional practice time in the practice pathway - passed the exam, and I am not aware of anyone from our program who did not pass it.)

We have also learned from the experience of having the first exam offered. It was exciting to see 456 diplomates newly certified in the subspecialty, including myself. However, I (and a number of others) were somewhat surprised at the pass rate of 91% for the exam being so high, given the vast body of knowledge covered by the exam and the lack of formal training, especially "book" training, of many who took the exam. It is not uncommon for pass rates for those grandfathering training requirements into a new subspecialty to be much lower. We do not know how the exam or its pass rate may change this year or beyond.

This challenges my statement in last year's posting that a single course, such as 10x10 ("ten by ten") or the American Medical Informatics Association (AMIA) Clinical Informatics Board Review Course, may not be enough. But perhaps with the experience brought to the table by qualifying via the practice pathway, a large amount of additional education is not necessary.

One bit of advice I can certainly give to any physician who meets the practice pathway qualifications (or can do so before 2018) is to sit for the exam before the end of grandfathering period. After that time, the only way to become certified in the subspecialty will be to complete a two-year, on-site, ACGME-accredited fellowship. While we are excited to be developing such a fellowship at OHSU, it will be a challenge for those who are mid-career, with jobs, family, and/or geographical roots, to up and move to become board-certified.

There are actually a number of categories of individuals for whom getting certified in the subspecialty after the grandfathering period will be a challenge:
  • Those who are mid-career - I have written in the past that the age range of OHSU online informatics students, including physicians, is spread almost evenly across all ages up to 65.
  • Those pursuing research training in informatics, such as an NLM fellowship or, in the case of some of our current students, in an MD/PhD program (and will not finish their residency until after the grandfathering period ends). Why must these individuals also need to pursue a clinical fellowship?
  • Those who already have had long medical training experiences, such as subspecialists with six or more years of training - Would such individuals want to do two additional years of informatics when, as I recently pointed out, it might be an ideal experience for them to overlay informatics and their subspecialty training?
Fortunately one option for physicians who do want some sort of certification will be the Advanced Interprofessional Informatics Certification being developed by AMIA. These physicians can and will still apply informatics to make important contributions to healthcare. I am pleased to report that AMIA has revamped its efforts to create this certification, not only for these physicians but also other practitioners of informatics.