ArticleApplied clinical informatics2020
Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs.
Article in Applied clinical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Uses of Electronic Health Record Data to Measure the Clinical Learning Environment of Graduate Medical Education Trainees: A Systematic Review.Academic medicine : journal of the Association of American Medical Colleges · 2023Pooled it
- Using electronic health record audit logs to identify pediatric intensive care unit teams.JAMIA open · 2026Article
- Education Research: Educational Informatics: Characterizing Child Neurology Resident Clinical Experiences Using Electronic Health Record Data.Neurology. Education · 2026Article
- Algorithmic Identification of the Daily Primary Frontline Clinician during Inpatient Medicine Encounters Using EHR Audit Log Data.Applied clinical informatics · 2026Article
- A Log-Level Data-Driven Precision Education Tool for Pediatrics Trainees: Human-Centered Development and Validation Study.JMIR human factors · 2026Article
- Digital supervision in the clinical learning environment: Characterizing teamwork in the electronic health record.Journal of hospital medicine · 2025Article
- Emerging Domains for Measuring Health Care Delivery With Electronic Health Record Metadata.Journal of medical Internet research · 2025Review
- An unclear partnership: key questions about physician and advanced practice provider collaboration in primary care.Health affairs scholar · 2025Article
- Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- A Model for Work Intensity in a Pediatric Training Program.Journal of graduate medical education · 2022Article
- Characterizing intensive care unit rounding teams using meta-data from the electronic health record.Journal of critical care · 2022Article
- Design, Usability, and Acceptability of a Needs-Based, Automated Dashboard to Provide Individualized Patient-Care Data to Pediatric Residents.Applied clinical informatics · 2022Article
- Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Predicting next-day discharge via electronic health record access logs.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Resident Physician Experience and Duration of Electronic Health Record Use.Applied clinical informatics · 2021Article
- Improving ACGME Compliance for Obstetric Anesthesiology Fellows Using an Automated Email Notification System.Applied clinical informatics · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePatient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider-patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs).
methodsWe prospectively surveyed residents in three care settings to collect self-reported rPPIs. Participants were surveyed at the end of primary care clinic, emergency department (ED), and inpatient shifts, shown a patient census list, asked to mark the patients with whom they interacted, and encouraged to provide a short rationale behind the marked interaction. We extracted routine EHR data elements, including audit logs, note contribution, order placement, care team assignment, and chart closure, and applied a logistic regression classifier to the data to predict rPPIs in each care setting. We also performed a comment analysis of the resident-reported rationales in the inpatient care setting to explore perceived patient interactions in a complicated workflow.
resultsWe surveyed 81 residents over 111 shifts and identified 579 patient interactions. Among EHR extracted data, time-in-chart was the best predictor in all three care settings (primary care clinic: odds ratio [OR] = 19.36, 95% confidence interval [CI]: 4.19-278.56; ED: OR = 19.06, 95% CI: 9.53-41.65' inpatient
conclusionClassification models based on routinely collected EHR data predict resident-defined rPPIs across care settings. While specific to pediatric residents in this study, the approach may be generalizable to other provider populations and scenarios in which accurate patient attribution is desirable.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.