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. 2024 Jun;33(6):e5815.
doi: 10.1002/pds.5815.

OpenSAFELY: A platform for analysing electronic health records designed for reproducible research

Linda Nab  1 , Andrea L Schaffer  1 , William Hulme  1 , Nicholas J DeVito  1 , Iain Dillingham  1 , Milan Wiedemann  1 , Colm D Andrews  1 , Helen Curtis  1 , Louis Fisher  1 , Amelia Green  1 , Jon Massey  1 , Caroline E Walters  1 , Rose Higgins  1 , Christine Cunningham  1 , Jessica Morley  1 , Amir Mehrkar  1 , Liam Hart  1 , Simon Davy  1 , David Evans  1 , George Hickman  1 , Peter Inglesby  1 , Caroline E Morton  1 , Rebecca M Smith  1 , Tom Ward  1 , Thomas O'Dwyer  1 , Steven Maude  1 , Lucy Bridges  1 , Ben F C Butler-Cole  1 , Catherine L Stables  1 , Pete Stokes  1 , Chris Bates  2 , Jonny Cockburn  2 , Frank Hester  2 , John Parry  2 , Krishnan Bhaskaran  3 , Anna Schultze  3 , Christopher T Rentsch  3 , Rohini Mathur  3 , Laurie A Tomlinson  3 , Elizabeth J Williamson  3 , Liam Smeeth  3 , Alex Walker  1 , Sebastian Bacon  1 , Brian MacKenna  1 , Ben Goldacre  1
Affiliations

OpenSAFELY: A platform for analysing electronic health records designed for reproducible research

Linda Nab et al. Pharmacoepidemiol Drug Saf. 2024 Jun.

Abstract

Electronic health records (EHRs) and other administrative health data are increasingly used in research to generate evidence on the effectiveness, safety, and utilisation of medical products and services, and to inform public health guidance and policy. Reproducibility is a fundamental step for research credibility and promotes trust in evidence generated from EHRs. At present, ensuring research using EHRs is reproducible can be challenging for researchers. Research software platforms can provide technical solutions to enhance the reproducibility of research conducted using EHRs. In response to the COVID-19 pandemic, we developed the secure, transparent, analytic open-source software platform OpenSAFELY designed with reproducible research in mind. OpenSAFELY mitigates common barriers to reproducible research by: standardising key workflows around data preparation; removing barriers to code-sharing in secure analysis environments; enforcing public sharing of programming code and codelists; ensuring the same computational environment is used everywhere; integrating new and existing tools that encourage and enable the use of reproducible working practices; and providing an audit trail for all code that is run against the real data to increase transparency. This paper describes OpenSAFELY's reproducibility-by-design approach in detail.

Keywords: OpenSAFELY; electronic health records; open science; reproducibility; research platform.

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Conflict of interest statement

Conflicts of interest

BG has received research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he also receives personal income from speaking and writing for lay audiences on the misuse of science. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data.

Figures

Figure 1
Figure 1. Overview of transformations from health care delivery to analysis results in research using linked electronic health records.
Transformations in a general research setting (panel A, figure adapted from “Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0” by Wang et al., licensed under CC BY 4.0), and transformations in OpenSAFELY* (panel B). Abbreviations: GPSS: general practice system supplier; ehrQL: Electronic Health Records Query Language. * This diagram represents a simplified overview of conceptual levels of data identifiability and data preparation in the OpenSAFELY platform from the perspective of a platform user. A detailed description of the tiered structure of data in OpenSAFELY, including data controllership and accessibility, is found in the Supplemental Material.
See this image and copyright information in PMC

References

    1. Wang SV, Schneeweiss S, Berger ML, et al. Reporting to improve reproducibility and facilitate validity assessment for healthcare database studies V1.0. Pharmacoepidemiol Drug Saf. 2017;26(9):1018–1032. doi: 10.1002/pds.4295. - DOI - PMC - PubMed
    1. Orsini LS, Berger M, Crown W, et al. Improving transparency to build trust in real-world secondary data studies for hypothesis testing—Why, what, and how: recommendations and a road map from the real-world evidence transparency initiative. Value Health. 2020;23(9):1128–1136. doi: 10.1016/j.jval.2020.04.002. - DOI - PubMed
    1. Goldacre B, Morley J. Better, broader, safer: using health data for research and analysis. A review commissioned by the Secretary of State for Health and Social Care. Department of Health and Social Care; Published online 2022.
    1. Wang SV, Sreedhara SK, Schneeweiss S. Reproducibility of real-world evidence studies using clinical practice data to inform regulatory and coverage decisions. Nat Commun. 2022;13(1):5126. doi: 10.1038/s41467-022-32310-3. - DOI - PMC - PubMed
    1. Seibold H, Czerny S, Decke S, et al. A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses. PLOS ONE. 2021;16(6):e0251194. doi: 10.1371/journal.pone.0251194. - DOI - PMC - PubMed

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