ArticleJournal of the American Medical Informatics Association : JAMIA2023
Clinical encounter heterogeneity and methods for resolving in networked EHR data: a study from N3C and RECOVER programs.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- A Systematic Evaluation of Cohort Selection Criteria and Their Impact on Machine Learning Model Performance and Demographic Disparities in COVID-19 Outcomes: Cohort Study.JMIR formative research · 2026Article
- Epidemiological characteristics of paediatric COVID-19 and influenza co-infections in the United States, 2020-2024.Epidemiology and infection · 2026Article
- Extending the Observational Medical Outcomes Partnership Common Data Model to Support Observational Peripheral Vascular Disease Research.The Journal of surgical research · 2026Observational
- Article
- An LLM-assisted framework for accelerated and verifiable clinical hypothesis testing from electronic health records.medRxiv : the preprint server for health sciences · 2026Article
- A case-crossover analysis of short-term PMCommunications medicine · 2025Article
- Article
- Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative.Journal of medical Internet research · 2025Article
- A common longitudinal intensive care unit data format (CLIF) for critical illness research.Intensive care medicine · 2025Article
- Unveiling sub-populations in critical care settings: a real-world data approach in COVID-19.Frontiers in public health · 2025Article
- A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research.medRxiv : the preprint server for health sciences · 2024Article
- MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.JAMIA open · 2024Article
- Which decisions affect cohort distribution in COVID-19 data analytics?AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- MENDS-on-FHIR: Leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.medRxiv : the preprint server for health sciences · 2023Article
- Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C).BMC medical research methodology · 2023Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
objectiveClinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite "macrovisits." MATERIALS AND
methodsEncounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits.
resultsAtomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay (LOS) and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, LOS and measurement variance decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability. DISCUSSION: Encounters are a complex and heterogeneous component of EHR data and native data issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations, and developments are necessary to realize the full potential of modern real-world data.
conclusionThis article presents method developments to manipulate and resolve EHR encounter data issues in a generalizable way as a foundation for future research and analysis.
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