Leveraging EMR to Embed Comparative-Effectiveness Research into Clinical Practice
A new approach to health care delivery may well provide a first taste of the value that can be realized when health IT is integrated into systems for delivering care and improving quality. The hope that simply plugging an electronic medical record (EMR) system into existing medical practices is worth the costs and will on its own improve health outcomes has yet to prove itself. But a paper just published in Clinical Trials provides a more enlightened vision for implementing EMR.
A cooperative agreement between the Veterans Affairs Cooperative Studies Program Coordinating Center in Boston and the schools for Medicine and Public Health at Boston University, guided in part by a Stanford biostatistician, has resulted in the development of the “Point-of-care clinical trial.” This approach uses a programmable EMR to essentially run a persistent clinical trial, and allows the clinical knowledge gained by the growing data to be fed back into clinical practice.
In a nutshell here’s how it works:
Let’s say you are interested in seeing which of two (or three) treatments was most effective at controlling blood sugar. Your EMR is adaptable enough that after programming in your variables it identifies the type of patient that’d be a candidate (in this case a diabetic). When a patient is seen that meets the criteria, the clinician can choose which treatment protocol is preferred. If there’s no preferred treatment, the clinician can indicate that, and the EMR randomizes the patient to one of the testing arms of the study. Data is gathered going forward, and your trial is underway and constantly growing.
This approach has numerous advantages over the traditional Randomized Clinical Trial (RCT), considered the gold standard of medical research. These include:
--It’s an RCT of the specific patient population that you serve. Typically RCT findings require that you apply the generalized knowledge of an often-idealized study population that may not resemble your patients to the realities of your local context.
--It’s quick and economical. Rather than waiting for a long and costly trial to be performed, you begin gathering data practically from day one. Naturally it’ll take time for your data to become robust, since it takes time for our study population to grow. Plus, you have a feedback loop to respond to findings very quickly. What if you begin to notice a trend in side effects? Rather than wait for a trial to be performed you could take corrective action to see if you can reduce those harms.
--It infuses data collection and quality improvement into the everyday practice of clinical medicine. This promotes a culture of quality, and also removes the barrier between researchers and clinicians. The value of this speaks for itself.
--It's a great example of how the redesign of health care processes and systems is a value-added proposition: Not only improving systems by reducing unwarranted variation, inefficiency and waste, but also providing additional benefits, such as high value health outcomes and comparative effectiveness data and ability to develop and test hypothesis in clinical practice, as a byproduct of what is already done everyday. Another term for this: Health care delivery science.
This approach essentially melds the best parts of observational research (relatively quick and inexpensive) with the RCT (randomized design), and could breath new life into the hope that adopting EMR will not only result in digitized health information, but also an investment into clinical research and better care.
Finally, an innovative use of EMR that actually has the potential to improve clinical outcomes. As an aside, this partnership may also provide a glimpse of what “Government Healthcare” is really all about: Using self-contained health systems such as the VA (the only system in the nation where the “government” could actually be said to provide patient care) as proving grounds for innovative new methods of delivering care, improving performance, and gathering comparative-effectiveness data for rational clinical and policy decision making.