Abstract
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 in this study, the
approach may be generalizable to other provider populations and scenarios in which
accurate patient attribution is desirable.
Keywords
secondary use - patient–provider - data mining - workflow - medical education
