{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Joshua Lin K"],"funding":["NLM NIH HHS","National Institutes of Health"],"pagination":["243-251"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8678205"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["111(1)"],"pubmed_abstract":["Electronic health record (EHR) discontinuity (i.e., receiving care outside of the study EHR system), can lead to information bias in EHR-based real-world evidence (RWE) studies. An algorithm has been previously developed to identify patients with high EHR-continuity. We sought to assess whether applying this algorithm to patient selection for inclusion can reduce bias caused by data-discontinuity in four RWE examples. Among Medicare beneficiaries aged >=65 years from 2007 to 2014, we established 4 cohorts assessing drug effects on short-term or long-term outcomes, respectively. We linked claims data with two US EHR systems and calculated %bias of the multivariable-adjusted effect estimates based on only EHR vs. linked EHR-claims data because the linked data capture medical information reco"],"journal":["Clinical pharmacology and therapeutics"],"pubmed_title":["Longitudinal Data Discontinuity in Electronic Health Records and Consequences for Medication Effectiveness Studies."],"pmcid":["PMC8678205"],"funding_grant_id":["R01LM012594","R01 LM012594"],"pubmed_authors":["Schneeweiss S","Gagne J","Murphy SN","Tong A","Glynn RJ","Jin Y","Joshua Lin K"],"additional_accession":[]},"is_claimable":false,"name":"Longitudinal Data Discontinuity in Electronic Health Records and Consequences for Medication Effectiveness Studies.","description":"Electronic health record (EHR) discontinuity (i.e., receiving care outside of the study EHR system), can lead to information bias in EHR-based real-world evidence (RWE) studies. An algorithm has been previously developed to identify patients with high EHR-continuity. We sought to assess whether applying this algorithm to patient selection for inclusion can reduce bias caused by data-discontinuity in four RWE examples. Among Medicare beneficiaries aged >=65 years from 2007 to 2014, we established 4 cohorts assessing drug effects on short-term or long-term outcomes, respectively. We linked claims data with two US EHR systems and calculated %bias of the multivariable-adjusted effect estimates based on only EHR vs. linked EHR-claims data because the linked data capture medical information reco","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2025-04-26T13:18:59.703Z","creation":"2025-04-06T14:15:17.245Z"},"accession":"S-EPMC8678205","cross_references":{"pubmed":["34424534"],"doi":["10.1002/cpt.2400"]}}