Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory: Table 1

Abstract

Widespread sharing of data from electronic health records and patient-reported outcomes can strengthen the national capacity for conducting cost-effective clinical trials and allow research to be embedded within routine care delivery. While pragmatic clinical trials (PCTs) have been performed for decades, they now can draw on rich sources of clinical and operational data that are continuously fed back to inform research and practice. The Health Care Systems Collaboratory program, initiated by the NIH Common Fund in 2012, engages healthcare systems as partners in discussing and promoting activities, tools, and strategies for supporting active participation in PCTs. The NIH Collaboratory consists of seven demonstration projects, and seven problem-specific working group 'Cores', aimed at leveraging the data captured in heterogeneous 'real-world' environments for research, thereby improving the efficiency, relevance, and generalizability of trials. Here, we introduce the Collaboratory, focusing on its Phenotype, Data Standards, and Data Quality Core, and present early observations from researchers implementing PCTs within large healthcare systems. We also identify gaps in knowledge and present an informatics research agenda that includes identifying methods for the definition and appropriate application of phenotypes in diverse healthcare settings, and methods for validating both the definition and execution of electronic health records based phenotypes.

CKD as a Model for Improving Chronic Disease Care through Electronic Health Records

“…CKD-related EHR data could also facilitate observational, comparative effectiveness, and safety studies of diagnostic and therapeutic approaches to kidney diseases (43)(44)(45)(46). Large aggregated datasets could be used to evaluate differences in risk-adjusted clinical outcomes and costs between providers and health systems as well as potentially identify processes that may account for these differences.…”

Assessing The Value Of Patient-Generated Data To Comparative Effectiveness Research

“…26 Six demonstration projects are being developed that have shown promise in establishing improved data fidelity and quality to support large-scale research across systems. 27 Three of the projects incorporate routinely collected patient-reported outcomes as an important data set.…”

Pharmacoepidemiology in the Era of Real-World Evidence

“…Distributed data networks have been proven to be a viable and preferred approach to analyzing multiple databases [21, 22]. In the North America alone, there are distributed networks created to support medical product safety surveillance [10, 23], comparative effectiveness research [24], pragmatic trials embedded within health systems [25], and public health surveillance [26]. The creation, maintenance, and expansion of distributed data networks require stable funding, proper governance, and analytic methods that produce valid results while protecting patient privacy [22, 27].…”