Friday, May 19, 2017

Failure to Translate: Why Have Evidence-Based EHR Interventions Not Generalized?

(This posting of mine was published today in The Health Care Blog and I am reposting it here.)

The adoption of electronic health records (EHRs) has increased substantially in hospitals and clinician offices in large part due to the “meaningful use” program of the Health Information Technology for Clinical and Economic Health (HITECH) Act. The motivation for increasing EHR use in the HITECH Act was supported by evidence-based interventions for known significant problems in healthcare. In spite of widespread adoption, EHRs have become a significant burden to physicians in terms of time and dissatisfaction with practice. This raises a question as to why EHR interventions have been difficult to generalize across the health care system, despite evidence that they contribute to addressing major challenges in health care.

EHR interventions address known problems in health care of patient safety, quality of care, cost, and accessibility of information. These problems were identified a decade or two ago but still persist. Patient safety problems due to medical errors were brought to light with the publication of the Institute of Medicine report, To Err is Human [1], with recent analyses indicating medical errors are still a problem and may be underestimated [2]. Deficiencies in the quality of medical care delivered was identified almost a decade and a half ago [3] and continues to be a problem [4]. The excess cost of care in the US has been a persistent challenge [5] and continues to the present [6]. A final problem motivating the use of EHRs has been access to patient information that is known to exist but is inaccessible [7], with access now stymied by “information blocking” [8].

These problems motivated initial research on the value of EHRs. One early study found that display of charges during order entry resulted in a 12.7% decrease in total charges and 0.9 days shorter length of stay [9]. Another study found that computerized provider order entry (CPOE) led to nonintercepted serious medication errors decreasing by 55%, from 10.7 events per 1000 patient-days to 4.86 events, with preventable ADEs reduced by 17% [10]. Additional studies of CPOE showed a reduction in redundant laboratory tests [11] and improved prescribing behavior of equally efficacious but less costly medications [12]. Another study found that CPOE increased the use of important “corollary orders” by 25% [13]. Additional studies followed from many institutions that were collated in systematic reviews and built the evidence-based case for EHRs [14-17]. There were some caveats about the evidence base, such as publication bias [18] and the benefits mostly emanating from “health IT leader” institutions that made investments both in EHRs and the personnel and leadership to use them successfully.

Despite the robust evidence base, why have the benefits of EHR adoption failed to generalize now that we have widespread adoption? There are several reasons, some of which emanate from well-intentioned circumvention of the EHR for other purposes. For example, both institutions and payers (including the US government) view the EHR as a tool and modify prioritization of functions for cost reduction. There is also a desire to use the EHR to collect data for quality measurement - which should be done - but not in ways that add substantial burden to the clinician. Additionally, there are the meaningful use regulations, which were implemented to insure that the substantive government investment in EHRs led to their use in clinically important ways but are now criticized as being a distraction for clinicians and vendors.

There are also some less nobly intentioned reasons why the value of EHRs has not generalized. One is “volume-based billing,” or the connection of billing to the volume of documentation, which leads to pernicious documentation practices [19]. Another is financial motivation for revenues of EHR vendors, who may be selling systems that are burdensome to use or not ready for widespread adoption. Much of the early evidence for the benefits of EHRs came from “home grown” systems, most of which have been replaced by commercial EHRs. These commercial EHRs do more than just provide clinical functionality; they redesign the delivery of care, sometimes beneficial but other times not. It thus can take a large expenditure on an EHR infrastructure before any marginal benefit from a particular clinical benefit can be achieved, even if the rationale for that function is evidence-based.

Nonetheless, a number of “health IT leader” institutions have sustained successful EHR use and quality of care, such as Kaiser-Permanente [20], Geisinger [21], and the Veteran’s Health Administration [22]. These institutions are not only integrated delivery systems but also have substantial expertise in clinical informatics. These qualities enable them to prioritize use of IT in the context of patients and practitioners as well as incorporate known best practices from clinical informatics focused on standards, interoperability, usability, workflow, and user engagement.

How, then, do we move forward? We can start by building on the technology foundation, albeit imperfect, that has come about from the HITECH Act. We must focus on translation, aiming to understand how to diversely implement functionality that is highly supported by the evidence while carrying out further research in areas where the evidence is less clear. As with any clinical intervention, we must pay attention to both beneficial and adverse effects, learning from the growing body of knowledge on safe use of EHRs [23]. We must also train and deploy clinician informatics leaders who provide expertise at the intersection of health care and IT [24].

Finally, we also reflect on the perspective of the larger value of IT in health care settings. Approaches to cost containment, quality measurement, and billing via documentation must be reformulated to leverage the EHR and reduce burden on clinicians. We should focus on issues such as practice and IT system redesign, best practices for the patient-practitioner-computer triad, and practitioner well-being [25]. We must build on value from other uses of EHRs and IT, including patient engagement and support for clinical research. Leadership for these changes must come from leading health care systems, professional associations, academia, and government.


1. Kohn LT, Corrigan JM, and Donaldson MS, eds. To Err Is Human: Building a Safer Health System. 2000, National Academies Press: Washington, DC.
2. Classen DC, Resar R, Griffin F, Federico F, Frankel T, Kimmel N, et al., 'Global trigger tool' shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff, 2011. 30: 4581-4589.
3. McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, et al., The quality of health care delivered to adults in the United States. N Engl J Med, 2003. 348: 2635-2645.
4. Levine DM, Linder JA, and Landon BE, The quality of outpatient care delivered to adults in the United States, 2002 to 2013. JAMA Intern Med, 2016. 176: 1778-1790.
5. Anderson GF, Frogner BK, Johns RA, and Reinhardt UE, Health care spending and use of information technology in OECD countries. Health Aff, 2006. 25: 819-831.
6. Squires D and Anderson C, U.S. Health Care from a Global Perspective: Spending, Use of Services, Prices, and Health in 13 Countries. 2015, The Commonwealth Fund: New York, NY,
7. Smith PC, Araya-Guerra R, Bublitz C, Parnes B, Dickinson LM, VanVorst R, et al., Missing clinical information during primary care visits. JAMA, 2005. 293: 565-571.
8. Adler-Milstein J and Pfeifer E, Information blocking: is it occurring and what policy strategies can address it? Milbank Q, 2017. 95: 117-135.
9. Tierney WM, Miller ME, Overhage JM, and McDonald CJ, Physician inpatient order writing on microcomputer workstations: effects on resource utilization. JAMA, 1993. 269: 379-383.
10. Bates DW, Leape LL, Cullen DJ, Laird N, Petersen LA, Teich JM, et al., Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA, 1998. 280: 1311-1316.
11. Bates DW, Kuperman GJ, Rittenberg E, Teich JM, Fiskio J, Ma'luf N, et al., A randomized trial of a computer-based intervention to reduce utilization of redundant laboratory tests. Am J Med, 1999. 106: 144-150.
12. Teich JM, Merchia PR, Schmiz JL, Kuperman GJ, Spurr CD, and Bates DW, Effects of computerized physician order entry on prescribing practices. Arch Int Med, 2000. 160: 2741-2747.
13. Overhage JM, Tierney WM, Zhou XH, and McDonald CJ, A randomized trial of "corollary orders" to prevent errors of omission. JAMA, 1997. 4: 364-375.
14. Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, et al., Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med, 2006. 144: 742-752.
15. Goldzweig CL, Towfigh A, Maglione M, and Shekelle PG, Costs and benefits of health information technology: new trends from the literature. Health Aff, 2009. 28: w282-w293.
16. Buntin MB, Burke MF, Hoaglin MC, and Blumenthal D, The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff, 2011. 30: 464-471.
17. Jones EB and Furukawa MF, Adoption and use of electronic health records among federally qualified health centers grew substantially during 2010-12. Health Aff, 2014. 33: 1254-1261.
18. Vawdrey DK and Hripcsak G, Publication bias in clinical trials of electronic health records. J Biomed Inform, 2013. 46: 139-141.
19. Kuhn T, Basch P, Barr M, and Yackel T, Clinical documentation in the 21st century: executive summary of a policy position paper from the American College of Physicians. Ann Intern Med, 2015. 162: 301-303.
20. Liang LL, Connected for Health - Using Electronic Health Records to Transform Care Delivery. 2010, San Francisco, CA: Jossey-Bass.
21. Maeng DD, Davis DE, Tomcavage J, Graf TR, and Procopio KM, Improving patient experience by transforming primary care: evidence from Geisinger's patient-centered medical homes. Pop Health Manag, 2013. 16: 157-163.
22. Longman P, Best Care Anywhere: Why VA Health Care is Better Than Yours. 2007, Sausalito, CA: Polipoint Press.
23. Sittig DF, Ash JS, and Singh H, The SAFER guides: empowering organizations to improve the safety and effectiveness of electronic health records. Am J Manag Care, 2014. 20: 418-423.
24. Detmer DE and Shortliffe EH, Clinical informatics: prospects for a new medical subspecialty. JAMA, 2014. 311: 2067-2068.
25. Adler-Milstein J, Embi PJ, Middleton B, Sarkar IN, and Smith J, Crossing the health IT chasm: considerations and policy recommendations to overcome current challenges and enable value-based care. J Am Med Inform Assoc, 2017: Epub ahead of print.

Monday, May 1, 2017

Navigating OHSU Informatics Education Programs and Content

Not infrequently, I receive emails asking about or even expressing confusion about the various informatics educational programs and products of Oregon Health & Science University (OHSU). With a couple of grant-funded curriculum development projects about to end, this is probably a good time for a posting here to help sort things out. Before I do that, I must give a plug to US News & World Report, which recently plugged informatics as a graduate health degree that expanded both knowledge and career opportunities.

OHSU has a number of educational programs in biomedical informatics. The core of all these programs is the Biomedical Informatics Graduate Program, which provides masters and PhD degrees in two tracks, health and clinical informatics (HCI) and bioinformatics and computational biomedicine (BCB). The HCI track also offers a Graduate Certificate that is a subset of the masters program, and these two programs available in a distance learning format.

OHSU also offers two fellowship programs. One is a long-standing research-oriented fellowship program for PhD and postdoctoral students funded by the National Library of Medicine. The postdoctoral option also includes a masters degree. More recently, a clinically oriented fellowship for physicians has been launched. This fellowship is accredited by the Accreditation Council for Graduate Medical Education (ACGME) and allows sitting for the clinical informatics subspecialty board exam. The clinical informatics fellowship also provides the Graduate Certificate with an option to pursue the masters degree.

OHSU also was the original participant in the AMIA 10x10 (“ten by ten”) program. The OHSU 10x10 course is a repackaging of the introductory course from the HCI track of the graduate program, and those completing the OHSU 10x10 course can take the optional final exam to receive academic credit from OHSU.

The OHSU biomedical informatics program has also participated in the development of a number of public repositories of educational materials that have been funded by US federal grants. OHSU was funded in the original and subsequent update of the Office of the National Coordinator for Health IT (ONC) curriculum. Development of the original curriculum was stopped when funding ended in 2013, with the archive freely available on the American Medical Informatics Association (AMIA) Web site. The update has been expanded to 24 components, each of which is about a college course in size. It has been under development since 2015 and will be made publicly available on the ONC Web site ( this summer.

All of the grantees of the ONC update project have also been required to offer short-term training to 1000 incumbent healthcare professionals. The OHSU offering has focused on healthcare data analytics, and has also provided continuing medical education (CME) for all physicians and Maintenance of Certification (MOC)-II credit for physicians certified in the clinical informatics subspecialty. The free courses offered as part of the ONC grant will be wrapping up at the end of May. We will likely start offering the course again in the future for a fee.

OHSU has also been funded to develop open educational resources (OERs) and data skills courses funded by two grants under the Big Data to Knowledge (BD2K) initiative of the National Institutes of Health (NIH). About 20 modules have been developed for various topics in biomedical science. The materials from this project are currently housed on a Web site that will transition to a permanent archive on GitHub when funding ends for the project later this year.

The long-term maintenance of repository materials is uncertain at this time. We are hopeful that resources to keep them up to date will be found, and OHSU will certainly continue to use them in its own educational programs.