<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Nab L</submitter><funding>Health Data Research UK</funding><funding>Barts Charity</funding><funding>Medical Research Council</funding><funding>National Institute for Health Research (NIHR)</funding><funding>National Institute for Health and Care Research</funding><funding>UK Research and Innovation</funding><funding>Wellcome Trust</funding><pagination>e5815</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7616137</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>33(6)</volume><pubmed_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 re</pubmed_abstract><journal>Pharmacoepidemiology and drug safety</journal><pubmed_title>OpenSAFELY: A platform for analysing electronic health records designed for reproducible research.</pubmed_title><pmcid>PMC7616137</pmcid><funding_grant_id>COV‐LT‐0009</funding_grant_id><funding_grant_id>MR/W016729/1</funding_grant_id><funding_grant_id>MC_PC_20058</funding_grant_id><funding_grant_id>MGU0504</funding_grant_id><funding_grant_id>MC_PC_20059</funding_grant_id><funding_grant_id>COV‐LT2‐0073</funding_grant_id><funding_grant_id>222097/Z/20/Z</funding_grant_id><funding_grant_id>COV-LT2-0073</funding_grant_id><funding_grant_id>220283/Z/20/Z</funding_grant_id><funding_grant_id>HDRUK2021.000</funding_grant_id><funding_grant_id>COV-LT-0009</funding_grant_id><funding_grant_id>220283</funding_grant_id><funding_grant_id>224485</funding_grant_id><funding_grant_id>MC_PC_20030</funding_grant_id><funding_grant_id>222097</funding_grant_id><funding_grant_id>MR/V015737/1</funding_grant_id><funding_grant_id>NIHR135559</funding_grant_id><funding_grant_id>224485/Z/21/Z</funding_grant_id><pubmed_authors>Morton CE</pubmed_authors><pubmed_authors>Davy S</pubmed_authors><pubmed_authors>Higgins R</pubmed_authors><pubmed_authors>Parry J</pubmed_authors><pubmed_authors>Walters CE</pubmed_authors><pubmed_authors>Stables CL</pubmed_authors><pubmed_authors>Williamson EJ</pubmed_authors><pubmed_authors>Massey J</pubmed_authors><pubmed_authors>Cunningham C</pubmed_authors><pubmed_authors>Bhaskaran K</pubmed_authors><pubmed_authors>Dillingham I</pubmed_authors><pubmed_authors>Schaffer AL</pubmed_authors><pubmed_authors>Bridges L</pubmed_authors><pubmed_authors>Tomlinson LA</pubmed_authors><pubmed_authors>Smith RM</pubmed_authors><pubmed_authors>Maude S</pubmed_authors><pubmed_authors>Goldacre B</pubmed_authors><pubmed_authors>Ward T</pubmed_authors><pubmed_authors>DeVito NJ</pubmed_authors><pubmed_authors>Walker A</pubmed_authors><pubmed_authors>Wiedemann M</pubmed_authors><pubmed_authors>Butler-Cole BFC</pubmed_authors><pubmed_authors>Smeeth L</pubmed_authors><pubmed_authors>MacKenna B</pubmed_authors><pubmed_authors>Andrews CD</pubmed_authors><pubmed_authors>Hulme W</pubmed_authors><pubmed_authors>Curtis H</pubmed_authors><pubmed_authors>Morley J</pubmed_authors><pubmed_authors>Nab L</pubmed_authors><pubmed_authors>Green A</pubmed_authors><pubmed_authors>Hester F</pubmed_authors><pubmed_authors>Rentsch CT</pubmed_authors><pubmed_authors>Stokes P</pubmed_authors><pubmed_authors>Evans D</pubmed_authors><pubmed_authors>Hart L</pubmed_authors><pubmed_authors>Mathur R</pubmed_authors><pubmed_authors>Inglesby P</pubmed_authors><pubmed_authors>Schultze A</pubmed_authors><pubmed_authors>O'Dwyer T</pubmed_authors><pubmed_authors>Mehrkar A</pubmed_authors><pubmed_authors>Bates C</pubmed_authors><pubmed_authors>Fisher L</pubmed_authors><pubmed_authors>Cockburn J</pubmed_authors><pubmed_authors>Bacon S</pubmed_authors><pubmed_authors>Hickman G</pubmed_authors></additional><is_claimable>false</is_claimable><name>OpenSAFELY: A platform for analysing electronic health records designed for reproducible research.</name><description>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 re</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Jun</publication><modification>2026-06-04T16:24:34.787Z</modification><creation>2025-04-06T01:09:57.071Z</creation></dates><accession>S-EPMC7616137</accession><cross_references><pubmed>38783412</pubmed><doi>10.1002/pds.5815</doi></cross_references></HashMap>