ArticleClinical pharmacology and therapeutics2022
Longitudinal Data Discontinuity in Electronic Health Records and Consequences for Medication Effectiveness Studies.
Article in Clinical pharmacology and therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Deploying the REACHnet Multi-State EHR-Based Network for Disease Surveillance (MENDS) node: lessons learned and tools utilized.JAMIA open · 2026Article
- Assessing the quality of electronic health record data and the claims linked data for target trial emulation studies.JAMIA open · 2026Article
- Beyond Missingness: Systematizing Methods for Comprehensive Data Fitness Assessment in Clinical Research.Journal of medical Internet research · 2026Article
- High-dimensional multiple imputation for partially observed confounders including natural language processing-derived auxiliary covariates.American journal of epidemiology · 2026Article
- Leveraging Multi-National Observational Study in Post-Marketing Safety Assessment: Challenges and Strategies.Therapeutic innovation & regulatory science · 2025Article
- Establishing a Validation Framework of Treatment Discontinuation in Claims Data Using Natural Language Processing and Electronic Health Records.Clinical pharmacology and therapeutics · 2025Article
- Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.Journal of medical Internet research · 2025Article
- The Impact of Low EHR-Continuity on Effect Estimates: Evidence from Two EHR-Medicare Linked Databases.Clinical epidemiology · 2025Article
- Major adverse cardiovascular events' reduction and their association with glucose-lowering medications and glycemic control among patients with type 2 diabetes: A retrospective cohort study using electronic health records.Journal of diabetes · 2024Article
- Informing evidence-based medicine for opioid use disorder using pharmacoeconomic studies.Expert review of pharmacoeconomics & outcomes research · 2024Review
- The transition from genomics to phenomics in personalized population health.Nature reviews. Genetics · 2024Review
- Risk of Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) Among Patients with Type 2 Diabetes Mellitus on Anti-Hyperglycemic Medications.Clinical epidemiology · 2024Article
- The Impact of Longitudinal Data-Completeness of Electronic Health Record Data on the Prediction Performance of Clinical Risk Scores.Clinical pharmacology and therapeutics · 2023Article
- MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network.BMC bioinformatics · 2022Article
- An algorithm to predict data completeness in oncology electronic medical records for comparative effectiveness research.Annals of epidemiology · 2022Article
- Diabetes medications and associations with Covid-19 outcomes in the N3C database: A national retrospective cohort study.PloS one · 2022Article
- Effectiveness and Safety of Reduced and Standard Daily Doses of Direct Oral Anticoagulants in Patients with Nonvalvular Atrial Fibrillation: A Cohort Study Using National Database Representing the Japanese Population.Clinical epidemiology · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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 recorded outside of the study EHR. Our study cohort included 77,288 patients in system 1 and 60,309 in system 2. We found the subcohort in the lowest quartile of EHR-continuity captured 72-81% of the short-term and only 21-31% of the long-term outcome events, leading to %bias of 6-99% for the short-term and 62-112% for the long-term outcome examples. This trend appeared to be more pronounced in the example using a nonuser comparison rather than an active comparison. We did not find significant treatment effect heterogeneity by EHR-continuity for most subgroups across empirical examples. In EHR-based RWE studies, investigators may consider excluding patients with low algorithm-predicted EHR-continuity as the EHR data capture relatively few of their actual outcomes, and treatment effect estimates in these patients may be unreliable.
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