Role of electronic health records in comparative effectiveness research.

The gold standard in evaluating treatment effects are randomized controlled trials (RCTs). Their design minimizes bias and maximizes our ability to identify causality. By contrast, observational data, which is routinely collected for other purposes, has many well-known limitations, including selection bias. Why then are we seeing such enthusiasm for ‘big data’ in healthcare, driven in part by the relentless growth in adoption of electronic health records (EHRs)? The first part of the answer is that, despite their many strengths, RCTs also have limitations. First, they do not represent real-world populations or settings. Their stringent exclusion criteria mean that the evidence they produce will not directly replicate the circumstances of many at-risk patients with common comorbidities, who might also benefit from the interventions under trial [1]. Most RCTs are also geographically localized, both in terms of the demographics of the participants as well as in clinical setting. RCTs also often lack the size required to detect the small effect sizes and significant variance encountered in many comparative effectiveness studies, and tend to be too short to detect long-term effects of interventions [2,3]. These limitations are imposed by the need to create controlled conditions, as well as by funding and ethical constraints common to experimental studies. The second motivation for the renewed interest in observational data is the enormous amount of digital data now being collected by clinical institutions, industry and government, and our recent technical capacity to warehouse, link and analyze data in volumes unprecedented a decade ago. Not only are clinical data being accumulated rapidly, they are providing an increasingly detailed record of individual behaviors and journeys. Together, these new attributes of observational data may become a ‘game changer’. The reason we randomize is to deal with the effects of unmodeled variation, and current strategies to deal with such variation in observational studies remain controversial [4]. However, if we had access to health records that included deep phenotypic, genotypic and environmental data, then at some stage we should reach a crossover point where observational data and RCTs are of equivalent value. At that moment we should be able to pull together, through case-matching, a personalized ‘virtual cohort’ of individuals whose collective recorded clinical destiny is at least as predictive of treatment outcomes as any RCT for a given patient. There is ongoing discussion of the relative merits of observational studies and RCTs, and the complementary roles that different forms of evidence play in contributing to the evidence base [3,5–8]. Historically, RCTs were designed to overcome the problems encountered in observational analyses and have therefore been seen as superior to observational studies rather than complementary. However, the rapid growth in EHR data has generated an unprecedented source of information, making it essential that we reassess this artificial wedge separating RCTs and observational studies, and recognize the important complementary roles both must play. “...the recent availability of large electronic health records data sets is challenging us to reconsider the role that observational studies can play in evidence-based medicine.”