Huge databases, appropriately mined, can provide important clinical info

By ACSH Staff — Jan 16, 2013
A randomized, controlled trial is known as the gold standard when it comes to medical research but electronic medical records are paving the way for wily scientists to conduct other types of research.

A randomized, controlled trial is known as the gold standard when it comes to medical research but electronic medical records are paving the way for wily scientists to conduct other types of research.

Vast databases amassed via the burgeoning collection of electronically-recorded information are allowing scientists to tease out information from the medical records of thousands or even millions of patients, the New York Times reports. (The records are de-identified, with personal information removed, before they can be used for research).

For example, Kaiser Permanente used its enormous database of 9 million patients to find a possible connection between men taking statin drugs having a lower risk of recurrence for prostate cancer.

Other researchers mined three databases and found an unexpected, but strong connection between taking both the antidepressant Paxil and the cholesterol-lowering pravastatin, and developing high blood sugar. (Neither drug alone is known to raise blood sugar, so this finding is quite interesting).

Scientists say this form of data mining is not going to replace randomized, controlled trials, but they might supplement them. R.C.T. s have their own sorts of biases, Dr. Jonathan Darer, in the division of clinical innovation at Geisinger Health System, told the Times. Frequently they exclude the most important populations. It s great to do guidelines for patients who only have diabetes, for example. Unfortunately those aren t the patients I see in my clinic, where they also have osteoporosis or hypertension or dementia and other health problems. What I really need is help with those complex patients.

ACSH s Dr. Josh Bloom notes that this is analogous to issues that he constantly dealt with during the drug discovery process. You get a ton of data some of it will be meaningful and some will not. The trick is to look at the data, pick out meaningful trends, and ignore the rest. Not always so easy.

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