Development of a Model to Measure Emergency Department Staffing Limitations. Issue 9 (September 2016)

• Record Type: Journal Article
• Authors: Michelson, Kenneth A.; Stack, Anne M.; Bachur, Richard G.
• Is Part Of: Pediatric emergency care. Volume 32:Issue 9(2016)
• Publication Date: 2016-09
• Subjects: staffing models -- crowding -- operations; Pediatric emergencies -- Periodicals; 618.92002505
• DOI: 10.1097/PEC.0000000000000892
• Languages: English
• ISSNs: 0749-5161

Abstract

Abstract : Background: The optimal staffing model for emergency departments (EDs) is not known. Improving staffing could lead to more timely, efficient, and effective care. We created a model of staffing to identify times of staffing limitation by provider type. Methods: We analyzed data from an academic pediatric ED with 60, 000 visits per year. Each 10-minute interval from January 1, 2011, through December 31, 2012, was categorized as nonlimited (no staffing limitation), space limited (≥2 patients in the waiting room with wait times > 30 minutes and ≥ 80% ED bed occupancy), nurse limited (≥2 patients in the waiting room with wait times > 30 min and < 80% ED bed occupancy), or physician limited (≥2 patients in examination rooms who have waited > 30 minutes for a physician) using computer modeling. We calculated the percentage of time each type of limitation was in effect and the median lengths of stay for patients presenting during times of each category of limitation. Results: The ED was space limited 5.0% of the time, nurse limited 16.1% of the time, and physician limited 0.1% of the time. In nonlimited times, length of stay was 201 minutes (interquartile range, 128–301), whereas patients presenting during space-limited, nurse-limited, and physician-limited times had statistically significantly higher LOS of 265 (187-360), 244 (169-337), and 247 (174-334) minutes, respectively. Conclusions: Times identified as space and staffing limited were associated with longer LOS. This computer model could be used to rapidly identify targeted staffing needs and then measure the effect of modifying staffing.