Academic Journal
Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs
| العنوان: | Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs |
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| المؤلفون: | Mai, Mark V., Orenstein, Evan W., Manning, John D., Luberti, Anthony A., Dziorny, Adam C. |
| المساهمون: | Association of Pediatric Program Directors |
| المصدر: | Applied Clinical Informatics ; volume 11, issue 03, page 442-451 ; ISSN 1869-0327 |
| بيانات النشر: | Georg Thieme Verlag KG |
| سنة النشر: | 2020 |
| الوصف: | Objective 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). Methods 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. Results 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: OR = 2.95, 95% CI: 2.23–3.97). Primary care clinic and ED specific models had c-statistic values > 0.98, while the inpatient-specific model had greater variability (c-statistic = 0.89). Of 366 inpatient rPPIs, residents provided rationales for 90.1%, which were focused on direct involvement in a patient's admission or transfer, or care as the front-line ordering clinician (55.6%). Conclusion Classification models based on routinely collected EHR data predict resident-defined rPPIs across care settings. While specific to pediatric residents ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1055/s-0040-1713133 |
| DOI: | 10.1055/s-0040-1713133.pdf |
| الاتاحة: | http://dx.doi.org/10.1055/s-0040-1713133 http://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0040-1713133.pdf |
| رقم الانضمام: | edsbas.A16EB083 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1055/s-0040-1713133 |
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