Abstract

Background In 2014, we recognised that the pace of admissions frequently exceeded our ability to assign a hospitalist. Long patient wait times occurred at admission, especially for patients arriving in the late afternoon when hospitalist day shifts were ending. Our purpose was to redesign hospitalist schedules, duties and method of distributing admissions to match demand.

Design We used administrative data to tabulate Hospital Medicine admission requests by time of day and identified mismatch between volume and capacity with the current staffing model. We determined that we needed to accommodate 29 admits per day with peak admission volume in the late afternoon and early evening. The current staffing model failed after 22 admits. To realign staffing around patient admissions, we organised a series of Lean quality improvements, starting with a 2-day event in July 2014, and followed by a series of Plan-Do-Study-Act (PDSA) cycles. The improvement team included hospitalists, residents and administrators, and each PDSA cycle involved collection of feedback from all affected providers.

Strategy At baseline, our hospitalist group had six daytime and two nighttime services, including teaching services and attending-only services. Four of eight services were available for admissions, while four were rounding-only. Admitting capacity (patients per day) was 22. Through three PDSA cycles, we successively adapted our staffing and admitting model until the final staffing model aligned with patient admissions. The final model included different shift start times, use of all 10 shifts for admissions and addition of an Advanced Registered Nurse Practitioner (ARNP) service.

Results Admitting capacity increased to 30. We confirmed success with follow-up data on patient wait times. Emergency department mean patient wait times for admission decreased 36% from 66 to 43 min (p<0.001).

Conclusion Quantifying admission demand by time of day, then designing work schedules and duties around meeting this demand was an effective approach to reduce patient wait times.