Using electronic health record audit logs to identify pediatric intensive care unit teams

Teamwork influences patient outcomes, but electronic health records (EHRs) do not reliably capture patient care team members, limiting its study. EHR audit logs provide insights into healthcare workers' workflows and may help identify patients' care team members.

To develop, validate, and compare algorithms utilizing EHR audit logs to identify patient-centric primary teams (bedside nurse, frontline clinician, and attending physician) in pediatric intensive care units (ICUs).

We observed rounds for 1931 patient days (development = 678; validation = 1253) across pediatric (PICU), neonatal (NICU) and cardiovascular (CVICU) ICUs at a quaternary children's hospital and documented each patient's daytime care team. We developed 2 algorithms that leveraged EHR audit logs to identify team members: (1) clinically informed heuristics, and (2) a Longitudinal Contribution Score (LCS). Accuracy was computed for each role and algorithm. 95% confidence intervals (CIs) and algorithm comparisons (bootstrap P values) were computed via patient-day bootstrap resampling.

In the development cohort (PICU), the LCS demonstrated greater accuracy than the heuristics for all roles: nurse (LCS 92.9% [95% CI, 90.9-94.8]; heuristic 67.6% [64.0-71.1]), frontline (83.3% [80.5-86.1]; 77.9% [74.6-81.0]), and attending (66.5% [62.7-70.2]; 61.9% [58.3-65.6]); all P < .005. In the validation cohort (PICU, NICU, and CVICU), only the nurse advantage was statistically replicated (LCS 91.2% [89.5-92.8], heuristic 74.2% [71.7-76.4]; P < .001); with frontline (83.0% [80.9-85.1], 81.2% [79.1-83.4]; P = .110) and attending (69.0 [66.5-71.6] vs 70.0% [67.4-72.4]; P = .326) roles showing comparable accuracy.

EHR audit log data can accurately identify patients' bedside nurses in pediatric ICUs. For frontline clinicians and attending physicians whose clinical coproduction and interdependence blurs individual EHR signals, identification is less accurate and supplementary data sources may be needed. Both algorithms provide a scalable foundation for team dynamics research, although the LCS offering greater generalizability than predefined heuristics.