176. Better use of scarce nursing staff through implementation of bed census forecasting
Contributed abstract in session MD-3: Integrated Planning in Healthcare /1, stream Regular talks.
Monday, 13:50-15:00Room: Room S2
Authors (first author is the speaker)
| 1. | Rob Vromans
|
| Behavioural, Management and Social sciences, University of Twente | |
| 2. | Richard Boucherie
|
| Stochastich Operations Research, University of Twente | |
| 3. | Aleida Braaksma
|
| University of Twente |
Abstract
Background: Hospitals face shortages of available nursing staff, especially during the COVID-19 pandemic. At the same time the number of nurses present is not in line with the demand for care, which results in inefficient use of available nurse capacity. This holds also for the Dutch hospital Rijnstate. Meanwhile, literature contains models that have shown to help hospitals align staff to patients.
Objective: Improve the use of nurse capacity in surgical wards while decreasing peak workloads by aligning the nurse shift schedules with the number of patients.
Methods: Based on historical data of 2018 and 2019 we model the relation between the OR-schedule and the number of required beds. For this purpose we use a statistical model (vanBerkel, 2011) to calculate the distribution of the required beds per hour based on the future OR schedule. This bed census is converted into staffing requirements with nurse-to-patient ratios by department heads in a Tactical Planning Meeting.
Impact: Before COVID-19 started, Rijnstate was able to reduce the number of nurse shifts per week, while maintaining its surgical capacity. During COVID-19, Rijnstate scheduled more OR-sessions than expected in the remaining beds for surgical patients. After COVID-19, Rijnstate increased the number of simultaneous OR-sessions while maintaining the reduced staff schedule. This study shows the potential impact of the application of (vanBerkel, 2011) in a Dutch hospital.
Keywords
- Integrated planning of health services
- Patient flow
- Operating room planning and scheduling
Status: accepted
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