Health IT's Next Big Challenge: Comparative Effectiveness Research
Innovative approach to medical data analysis can yield new treatment options at a lower cost.
Many academic medical thought leaders insist that the best way to find those treatment protocols is to test them in randomized controlled trials. Such RCTs require a large group of control subjects to receive either a placebo or conventional therapy and a large group to receive the experimental treatment in question. The problem is RCTs are outrageously expensive. In today's cost conscious healthcare system, that's a problem.
Enter comparative effectiveness research. CER compares two or more accepted treatments to determine which are most effective. Medical informatics comes into the picture because it's now possible to get these projects off the ground by analyzing huge patient databases. And much of that patient data can now be gleaned from electronic health record systems.
The American Recovery and Reinvestment Act of 2009 has earmarked $1.1 billion for CER. The Agency for Healthcare Research and Quality (AHRQ), the federal agency tasked with improving the quality, safety, efficiency, and effectiveness of health care, has been using part of that money to fund research on data infrastructure so that clinicians can figure how to take advantage of all the patient data in the Medicare system to compare treatment options. Other AHRQ-sponsored research has been looking at how to create an all-payer, all-claims database that clinicians can tap into for the same purpose.
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Other CER-related projects include one led by David J. Magid, MD, director of research at the Colorado Permanente Group. His team searched through thousands of the group's EHRs to figure out which anti-hypertensive drugs are most effective when patients don't respond to first-line treatment with diuretics. The team managed to keep its research costs down to $200,000, a small fraction of what a randomized controlled trial would cost, and still came up with useful results, namely that beta blockers and ACE inhibitors work well.
Similarly a consortium of large healthcare systems, including Kaiser Permanente and Mayo Clinic, is capitalizing on the power of tens of millions of e-records to generate research. For example, they recently launched programs to mine their EHRs to compare treatment protocols for diabetes.
"With these large databases and detailed clinical information, we can conduct comparative, effective research in real world settings, with a full range of patients, not just those selected for clinical trials," Joe V. Selby, director of Kaiser's research division, states in a recent issue of Scientific American.
Boston's Beth Israel Deaconess Medical Center, one of the teaching hospitals affiliated with Harvard Medical School, recently entered the CER arena in a big way. Starting this month, the medical center launched Clinical Query, a searchable patient data repository that lets researchers and clinicians look for potential connections between diseases, treatment options, and risk factors, which in turn can become the jumping off point for a research project.
So if a Harvard researcher wants to compare the benefits of diuretics to ACE inhibitors among patients with hypertension, he can use Clinical Query to look at the records of more than 2 million patients and 200 million data points, including diagnoses, medications taken, lab values, and radiology images.
A comparison of data on the two classes of high blood pressure meds might reveal that one is more effective than the other. And while the results of that CER analysis may not carry the same weight as a randomized clinical trial in which groups of patients were actually given the drugs in real time to see which were more effective, the CER results can still guide clinicians on treatment options for their patients.
Given the fact that comparative effectiveness research will likely cost far less than a randomized clinical trial, it's time healthcare stakeholders take a closer look at this approach. The challenge for IT departments is going to be getting searchable patient data repositories up and running. Few hospitals have the resources to create their own version of Clinical Query. But at the very least, they need to start ramping up their data warehousing and data mining initiatives.
EHR systems are now collecting invaluable information that physicians can use to detect disease patterns, clusters of patients exposed to specific toxins, and groups of patients who respond well to various drug regimens. We can't waste this gold mine.
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