Healthcare // Clinical Information Systems
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8/20/2012
09:22 AM
Paul Cerrato
Paul Cerrato
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Health IT's Next Big Challenge: Comparative Effectiveness Research

Innovative approach to medical data analysis can yield new treatment options at a lower cost.

5 Key Elements For Clinical Decision Support Systems
5 Key Elements For Clinical Decision Support Systems
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Healthcare providers are being pushed to deliver more cost effective medical care and to improve the health of not just individual patients but large populations. One key to carrying out both mandates is finding more clinically effective treatment options.

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.

[ Most of the largest healthcare data security and privacy breaches have involved lost or stolen mobile computing devices. For possible solutions, see 7 Tools To Tighten Healthcare Data Security. ]

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.

InformationWeek Healthcare brought together eight top IT execs to discuss BYOD, Meaningful Use, accountable care, and other contentious issues. Also in the new, all-digital CIO Roundtable issue: Why use IT systems to help cut medical costs if physicians ignore the cost of the care they provide? (Free with registration.)

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Tom LaSusa
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Tom LaSusa,
User Rank: Apprentice
8/29/2012 | 2:13:44 PM
re: Health IT's Next Big Challenge: Comparative Effectiveness Research
Editor's Note: The following comment was written by Richard Tannen, MD

Having spent more than a decade rigorously assessing whether an Electronic Medical Record Database can provide reliable information about healthcare, I was delighted to see someone champion its use for "Comparative Effectiveness" research.

However, while our research efforts have demonstrated the feasibility of this strategy, I would caution that the obstacles that need to be overcome to put it into practice are considerable. They have not been addressed by the various sources quoted in your article, that describe what they or others have done.

Our group using the large (approximately 8.0 Million patient records EMR) United Kingdom General Practice Research Database, has shown that reliable answers can be obtained to determine the effectiveness of treatments1. BUT it only occurs when a properly-constructed, sufficiently-large database is used, and EQUALLY IMPORTANTLY when the vexing problem of "unrecognized confounding" can be overcome (a major advance made by our group)1,2.

To my knowledge no database in the US has been tested to determine whether it can yield reliable results for such studies, and there are huge issues to surmount in order to develop a database with the important characteristics of the UK database. This is because the organized socialized health care system in the UK lends itself to establishing a properly structured EHR database, whereas the unstructured nature of healthcare in the US poses a major challenge to achieve this goal.

The challenges to surmount this issue are beginning to be addressed in the US, and hopefully the effort will be successful. In my judgment a properly constructed, validated DATABASE of 50+ million patient records can transform healthcare by addressing Comparative Effectiveness research and other related questions. In addition, more methods to address "unrecognized confounding" need to be developed. It will take adequate funding and a coordinated effort by many in the healthcare field as well as the government to make this happen. BUT if successful it can usher in a new, dramatically improved era of healthcare.

Richard Tannen, MD

1. 1. Tannen RL, Weiner MG, Xie D. Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and randomized controlled trial findings. BMJ 2009, 338; b81 [doi:10.1136/bmj.b81]

2. Yu M, Xie D, Wang X, Weiner MG, Tannen RL. Prior event rate ratio adjustment: numerical studies of a statistical method to address unrecognized confounding in observational studies. Pharmacoepidemiology and Drug Safety 2012, 21(S2): 60-68.

3. Tannen RL, Xie D, Wang X, Yu M, Weiner MG. A New "Comparative Effectiveness" Assessment Strategy using the THIN Database: Comparison of the Cardiac Complications of Pioglitazone and Rosiglitazone.
jaysimmons
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jaysimmons,
User Rank: Apprentice
11/28/2012 | 7:27:23 AM
re: Health IT's Next Big Challenge: Comparative Effectiveness Research
Tom: I enjoyed your Editor's Note (from Richard Tannen, MD). It brings up a valid point, which is also brought up in the article; data alone isn't enough to make proper analysis of treatments. I agree that drawing comparisons between the US and UK healthcare systems (in terms of data structure) may be skewed due to major structural differences, but I don't doubt that the US wouldn't be able to pull together a database of equal size. Perhaps 50 million patient records may not be achieved anytime soon, but a database of several million may not be too far off.

Jay Simmons
Information Week Contributor
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