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EBM analysis
Assessing function of electronic health records for real-world data generation
  1. Daphne Guinn1,2,
  2. Erin E Wilhelm2,
  3. Grazyna Lieberman3,
  4. Sean Khozin4
  1. 1 Program for Regulatory Science and Medicine, Georgetown University, Washington, District of Columbia, USA
  2. 2 Department of Pharmacology and Physiology, Georgetown University, Washington, District of Columbia, USA
  3. 3 Genentech, South San Francisco, California, USA
  4. 4 US Food and Drug Administration, Silver Spring, Maryland, USA
  1. Correspondence to Dr Daphne Guinn, Program for Regulatory Science & Medicine, Georgetown University, Washington DC 20007, USA; dag137{at}georgetown.edu

https://doi.org/10.1136/bmjebm-2018-111111

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    Electronic health records (EHRs) have changed how medical information is captured, and they have the potential to be a rich source of information to improve drug development and clinical care. Accelerated in USA by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, the majority of US healthcare providers use EHRs in practice. Although the Office of the National Coordinator for Health Information Technology within the US Department of Health and Human Services (HHS) set forth standards with the intention to create EHRs that could be used to advance healthcare processes,1–3 there is still much room for improvement to adequately capture patient-provided and clinically relevant data needed to support learning healthcare systems that adapt as new clinical knowledge is gained.4 Currently, stakeholders within regulatory agencies, professional organisations, the pharmaceutical industry and payer groups are exploring how data collected during the delivery of routine healthcare or data related to a patient’s health status, defined as real-world data (RWD), can be analysed to generate clinical evidence, known as real-world evidence, to enhance healthcare and medical product development.5 6 This shared interest has led workshops exploring the incentives that are important to each stakeholder group.6–8 Multistakeholder collaborations have formed to empower RWD projects, such as the work being done by the Food and Drug Administration (FDA) Oncology Center of Excellence, healthcare technology company, Flatiron Health, and the American Society of Clinical Oncology CancerLinQ, which will focus on determining the characteristics and clinical outcomes associated with patients with advanced cancer using RWD collected from the EHR.9 The RWD effort is supported with legislative action. The 21st Century Cures Act requires the US FDA to explore and produce guidance on how RWD can inform decision-making, including label expansion for approved products and postmarket commitments.10 Investigating RWD applications is also a …

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    Footnotes

    • Contributors All authors contributed to the intellectual content and preparation of the manuscript, and approved the final version of the manuscript.

    • Funding This analysis was made possible through support from the Georgetown University Center of Excellence in Regulatory Science and Innovation (CERSI), a cooperative agreement (U01FD004319) funded by the US Food and Drug Administration (FDA). The support for DG’s Fellowship in Regulatory Science was provided by the Pharmaceutical Research and Manufacturers of America (PhRMA). Neither the US Food and Drug Administration nor the Pharmaceutical Research and Manufacturers of America had a role in and preparation or approval of the manuscript. Its content is solely the responsibility of the authors and does not necessarily represent the official views of sponsors or institutions with which the authors are affiliated.

    • Competing interests None declared.

    • Patient consent Not required.

    • Provenance and peer review Not commissioned; internally peer reviewed.