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Transparent Reporting on Research Using Unstructured Electronic Health Record Data to Generate ‘Real World’ Evidence of Comparative Effectiveness and Safety

Author

Listed:
  • Shirley V. Wang

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Olga V. Patterson

    (University of Utah
    VA Salt Lake City Health Care System)

  • Joshua J. Gagne

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Jeffrey S. Brown

    (Harvard Pilgrim Health Care Institute and Harvard Medical School)

  • Robert Ball

    (Food and Drug Administration)

  • Pall Jonsson

    (National Institute for Health and Care Excellence)

  • Adam Wright

    (Brigham and Women’s Hospital, Harvard Medical School)

  • Li Zhou

    (Brigham and Women’s Hospital, Harvard Medical School)

  • Wim Goettsch

    (The National Healthcare Institute (ZIN)
    Utrecht University)

  • Andrew Bate

    (Pfizer)

Abstract

Research that makes secondary use of administrative and clinical healthcare databases is increasingly influential for regulatory, reimbursement, and other healthcare decision-making. Consequently, there are numerous guidance documents on reporting for studies that use ‘real-world’ data captured in administrative claims and electronic health record (EHR) databases. These guidance documents are intended to improve transparency, reproducibility, and the ability to evaluate validity and relevance of design and analysis decisions. However, existing guidance does not differentiate between structured and unstructured information contained in EHRs, registries, or other healthcare data sources. While unstructured text is convenient and readily interpretable in clinical practice, it can be difficult to use for investigation of causal questions, e.g., comparative effectiveness and safety, until data have been cleaned and algorithms applied to extract relevant information to structured fields for analysis. The goal of this paper is to increase transparency for healthcare decision makers and causal inference researchers by providing general recommendations for reporting on steps taken to make unstructured text-based data usable for comparative effectiveness and safety research. These recommendations are designed to be used as an adjunct for existing reporting guidance. They are intended to provide sufficient context and supporting information for causal inference studies involving use of natural language processing- or machine learning-derived data fields, so that researchers, reviewers, and decision makers can be confident in their ability to evaluate the validity and relevance of derived measures for exposures, inclusion/exclusion criteria, covariates, and outcomes for the causal question of interest.

Suggested Citation

  • Shirley V. Wang & Olga V. Patterson & Joshua J. Gagne & Jeffrey S. Brown & Robert Ball & Pall Jonsson & Adam Wright & Li Zhou & Wim Goettsch & Andrew Bate, 2019. "Transparent Reporting on Research Using Unstructured Electronic Health Record Data to Generate ‘Real World’ Evidence of Comparative Effectiveness and Safety," Drug Safety, Springer, vol. 42(11), pages 1297-1309, November.
  • Handle: RePEc:spr:drugsa:v:42:y:2019:i:11:d:10.1007_s40264-019-00851-0
    DOI: 10.1007/s40264-019-00851-0
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    References listed on IDEAS

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    1. Government of India, 2017. "National Health Policy 2017," Working Papers id:11664, eSocialSciences.
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    Cited by:

    1. Andrew Bate & Steve F. Hobbiger, 2021. "Artificial Intelligence, Real-World Automation and the Safety of Medicines," Drug Safety, Springer, vol. 44(2), pages 125-132, February.

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