ArticleJournal of critical care2022
Characterizing intensive care unit rounding teams using meta-data from the electronic health record.
Article in Journal of critical care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Using electronic health record audit logs to identify pediatric intensive care unit teams.JAMIA open · 2026Article
- Algorithmic Identification of the Daily Primary Frontline Clinician during Inpatient Medicine Encounters Using EHR Audit Log Data.Applied clinical informatics · 2026Article
- A Scoping Review of Multilevel Patient-Sharing Network Measures in Health Services Research.Medical care research and review : MCRR · 2025Article
- Variation in Corticosteroid Prescribing Practices for Patients With Septic Shock.Critical care explorations · 2025Article
- An electronic health record metadata-mining approach to identifying patient-level interprofessional clinician teams in the intensive care unit.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Article
- Using Electronic Health Records to Identify the Daily Primary Provider During Hospitalization.Critical care explorations · 2024Article
- Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional-patient interaction intensity: a cohort study.BMJ health & care informatics · 2024Article
- Shift-Level Team Familiarity Is Associated with Improved Outcomes in Mechanically Ventilated Adults.American journal of respiratory and critical care medicine · 2024Article
- The Role of Data Science in Closing the Implementation Gap.Critical care clinics · 2023Review
- Intensivist physician-to-patient ratios and mortality in the intensive care unit.Intensive care medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeTeamwork is an important determinant of outcomes in the intensive care unit (ICU), yet the nature of individual ICU teams remains poorly understood. We examined whether meta-data in the form of digital signatures in the electronic health record (EHR) could be used to identify and characterize ICU teams.
methodsWe analyzed EHR data from 27 ICUs over one year. We linked intensivist physicians, nurses, and respiratory therapists to individual patients based on selected EHR meta-data. We then characterized ICU teams by their members' overall past experience and shared past experience; and used network analysis to characterize ICUs by their network's density and centralization.
resultsWe identified 2327 unique providers and 30,892 unique care teams. Teams varied based on their average team member experience (median and total range: 262.2 shifts, 9.0-706.3) and average shared experience (median and total range: 13.2 shared shifts, 1.0-99.3). ICUs varied based on their network's density (median and total range: 0.12, 0.07-0.23), degree centralization (0.50, 0.35-0.65) and closeness centralization (0.45, 0.11-0.60). In a regression analysis, this variation was only partially explained by readily observable ICU characteristics.
conclusionsEHR meta-data can assist in the characterization of ICU teams, potentially providing novel insight into strategies to measure and improve team function in critical care.
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