Abstract

Electronic health record (EHR) data have emerged as an important resource for population health and clinical research. There have been significant efforts to leverage EHR data for research; however, given data security concerns and the complexity of the data, EHR data are frequently difficult to access and use for clinical studies. We describe the development of a Clinical Research Datamart (CRDM) that was developed to provide well-curated and easily accessible EHR data to Duke University investigators. The CRDM was designed to (1) contain most of the patient-level data elements needed for research studies; (2) be directly accessible by individuals conducting statistical analyses (including Biostatistics, Epidemiology, and Research Design (BERD) core members); (3) be queried via a code-based system to promote reproducibility and consistency across studies; and (4) utilize a secure protected analytic workspace in which sensitive EHR data can be stored and analyzed. The CRDM utilizes data transformed for the PCORnet data network, and was augmented with additional data tables containing site-specific data elements to provide additional contextual information. We provide descriptions of ideal use cases and discuss dissemination and evaluation methods, including future work to expand the user base and track the use and impact of this data resource. The CRDM utilizes resources developed as part of the Clinical and Translational Science Awards (CTSAs) program and could be replicated by other institutions with CTSAs.

Highlights

  • Electronic health record (EHR) data have emerged as an important resource for population health and clinical research

  • The application of methods derived from statistics, computer science, data engineering, and informatics has resulted in a number of high-impact findings and methodologies that have the potential to transform clinical research, epidemiology, and population health sciences [2,3,4,5,6,7,8]

  • Duke University Health System (DUHS) utilizes the Epic EHR data platform, with all health system data stored in an enterprise data warehouse (EDW), including data related to patient demographics, diagnosis and procedure codes, laboratory orders and results, medication orders and fulfillments, vital signs, encounter location information, provider notes, and other detailed clinical data

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Summary

Introduction

Electronic health record (EHR) data have emerged as an important resource for population health and clinical research. CDMs, such as those used by the National Patient-Centered Clinical Research Network (PCORnet) and/or the Observational Medical Outcomes Partnership (OMOP), comprise a set of rules for how to turn raw EHR data into simpler data models [9]. These efforts have stimulated a significant number of retrospective analyses and innovative multicenter clinical trials [10,11]

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