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Longitudinal Adherence to Colorectal Cancer Screening Guidelines
Anissa Cyhaniuk, MA, and Megan E. Coombes, MSc
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The Promise and Perils of Big Data in Healthcare
Austin B. Frakt, PhD, and Steven D. Pizer, PhD
Continuity of Care and Changes in Medication Adherence Among Patients With Newly Diagnosed Diabetes
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The Promise and Perils of Big Data in Healthcare

Austin B. Frakt, PhD, and Steven D. Pizer, PhD
Big Data analyses are observational, raising threats to causal inference. Validity checks help, but we must not let enthusiasm about Big Data obscure the science.
Am J Manag Care. 2016;22(2):98-99
Spurred by the accuracy with which companies like Google and Netflix use large amounts of data to anticipate our interests, there is growing investment in "Big Data" applications to healthcare. For example, the FDA’s postmarket surveillance program—the Sentinel Initiative—analyzes billions of drug prescriptions for adverse events.1 However, if we’re not careful, big data healthcare could cause harm.
 
Data are “big” if they either: 1) represent many more subjects than a typical randomized clinical trial (RCT)—tens of thousands or more—and/or 2) they include a broad range—hundreds or more—of clinically relevant patient and provider characteristics. Such data can extend the reach of clinical research to include study of rare events, heterogeneous treatment effects, long-term outcomes, and other topics difficult or impossible to study with RCTs.
 
Some have suggested that big data will rapidly improve healthcare delivery. For instance, finding insufficient guidance in the medical literature, physicians at Stanford’s Packard Children’s Hospital used electronic medical record data to help make an urgent decision about using anticoagulation medication in a lupus patient.2 The strongest proponents of such big data applications believe that with enough information, causal relationships reveal themselves without an RCT.
 
Are they right? For clinical applications, this is a vital question. For instance, for every 5 million packages of x-ray contrast media distributed to healthcare facilities, about 6 individuals die from adverse effects.3 With big data, we learn that such deaths are highly correlated with electrical engineering doctorates awarded, precipitation in Nebraska, and per capita mozzarella cheese consumption (correlations 0.75, 0.85, and 0.74, respectively).
 
However, because we cannot conceive of a causal mechanism, it is obvious that these variables play no causal role in x-ray contrast media deaths. That such high correlations can be easily mined from big data is concerning nonetheless, because it is not always trivial to assess whether they are telling us something useful. For example, observational data reveal that proton pump inhibitor (PPI) use is associated with pneumonia incidence.4 This could be causal because a mechanism is plausible—gastric acid reduction could increase bacterial colonization—but perhaps the association arises because other factors drive both PPI use and pneumonia incidence.
 
Faced with this kind of uncertainty, there is temptation to insist that only an RCT can convince us of causation; however, the very promise of big data is its potential to see what RCTs won’t, thereby improving care in ways that RCTs cannot. Although caution is warranted, we should not dismiss big data too quickly. The way forward requires careful selection of observational research designs coupled with rigorous testing for violations of key assumptions on which causal inference relies.
 


 
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