Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs.

Patient 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). We 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. We 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 Classification 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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Georg Thieme Verlag KG Stuttgart · New York.

Déclaration de conflit d'intérêts

A.C.D. and M.V.M. are the principal investigators for the Special Project Award from the Association of Pediatric Program Directors, which supported this work. They hold a pending institutional technology disclosure based on this work. E.W.O. is a cofounder of and has equity in Phrase Health, a clinical decision support analytics company which was not involved, directly or indirectly, in this work. He receives no direct revenue from this relationship. J.D.M. is a member and has equity in ArchiveCore, a health care credentialing company which was not involved, directly or indirectly, in this work. A.A.L. has no conflicts of interest to disclose.