ArticleJournal of the American Medical Informatics Association : JAMIA2023
A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Develop and validate a fair machine learning model to identify patients with high data-continuity in electronic health records data.JAMIA open · 2026Article
- A Practical Approach to Assessing the Completeness of Electronic Health Records for Medical Research: Data Quality Study.JMIR medical informatics · 2026Article
- Long COVID Persistence and Surveillance Gaps Across 58 US Hospitals.JAMA network open · 2026Article
- The age paradox in post-infectious sequelae: physiological reserve outweighs chronological age in Long COVID susceptibility.medRxiv : the preprint server for health sciences · 2026Article
- Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19.Med (New York, N.Y.) · 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
- Towards cross-application model-agnostic federated cohort discovery.Journal of the American Medical Informatics Association : JAMIA · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
objectivePatients who receive most care within a single healthcare system (colloquially called a "loyalty cohort" since they typically return to the same providers) have mostly complete data within that organization's electronic health record (EHR). Loyalty cohorts have low data missingness, which can unintentionally bias research results. Using proxies of routine care and healthcare utilization metrics, we compute a per-patient score that identifies a loyalty cohort. MATERIALS AND
methodsWe implemented a computable program for the widely adopted i2b2 platform that identifies loyalty cohorts in EHRs based on a machine-learning model, which was previously validated using linked claims data. We developed a novel validation approach, which tests, using only EHR data, whether patients returned to the same healthcare system after the training period. We evaluated these tools at 3 institutions using data from 2017 to 2019.
resultsLoyalty cohort calculations to identify patients who returned during a 1-year follow-up yielded a mean area under the receiver operating characteristic curve of 0.77 using the original model and 0.80 after calibrating the model at individual sites. Factors such as multiple medications or visits contributed significantly at all sites. Screening tests' contributions (eg, colonoscopy) varied across sites, likely due to coding and population differences. DISCUSSION: This open-source implementation of a "loyalty score" algorithm had good predictive power. Enriching research cohorts by utilizing these low-missingness patients is a way to obtain the data completeness necessary for accurate causal analysis.
conclusioni2b2 sites can use this approach to select cohorts with mostly complete EHR data.
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