Journal of the American College of Emergency Physicians Open (Feb 2022)

Measuring without a ruler: Limited data to characterize the relationship between physician assistant/nurse practitioner staffing and emergency department performance

  • Elizabeth S. Temin,
  • Margaret E. Samuels‐Kalow,
  • Rebecca E. Cash,
  • Krislyn M. Boggs,
  • Carlos A. Camargo Jr.,
  • Kori S. Zachrison

DOI
https://doi.org/10.1002/emp2.12617
Journal volume & issue
Vol. 3, no. 1
pp. n/a – n/a

Abstract

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Abstract Objective Physician assistant (PA) and nurse practitioner (NP) staffing is increasingly common in emergency departments (EDs), with variable physician supervision. We examined the feasibility of using publicly reported metrics as a measure of ED performance by staffing model. Methods We classified a convenience sample of 915 EDs by staffing model using the National Emergency Department Inventory‐USA 2016 and a follow‐up survey. Staffing models included 24/7 attending coverage with PAs/NPs, 24/7 attending coverage without PAs/NPs, and PAs/NPs without 24/7 attending coverage. We linked EDs with Hospital Compare data to examine availability of metrics and compared metric performance by staffing model. We used regression modeling to examine the independent relationship between staffing model and ED performance after adjusting for ED characteristics. Results Of 915 EDs surveyed, 767 (83%) responded and 436 (48%) had complete staffing data and any Hospital Compare data. The 216 EDs without any Hospital Compare data more frequently had no 24/7 attending coverage, were smaller, and were more often rural. Of 5 clinical metrics, 3 had data from < 100 EDs (range: 2%–21%), and 2 had data from 0 EDs. Of the 5 clinical metrics, only median time‐to‐ECG had enough data for analysis and found no difference among staffing models. Among the 3 process metrics, only time to discharge was significantly faster in EDs with any PA/NP staffing. Conclusion Many EDs in our national sample lacked sufficient Hospital Compare data to evaluate performance, likely because of lower patient volumes for condition‐specific metrics. Alternative strategies to measure quality of care delivery in these settings should be developed.

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