Workload Forecasting and Demand-Driven Staffing: A Case Study for Post-operative Physiotherapy

Hospital departments face artificial demand variability when the demand for their care depends on the plans of other departments. This holds among others for ‘downstream’ departments like surgical wards and departments that treat patients without appointments (walk-in). Challenge: Variability in demand makes it hard for capacity planners to align capacity and demand at every moment. In addition, staffing levels typically need to be settled when the individual patients to care for are still unknown. Literature contains models to forecast artificial demand variability, but lacks implementation results. Method: This chapter describes the application of a forecast of demand for post-operative physiotherapy based on the surgical session roster. The forecast is used to determine staffing requirements on which the physiotherapists base their schedule. We test this approach in a case study at the Sint Maartenskliniek in the Netherlands, for physiotherapists who are part of the orthopaedic care chain. Results and Conclusion: Successful implementation of this methodology has shown positive effects for patients (12% more adherence to protocol), employees (twice the number of days were efficiently staffed), and the hospital (13% productivity increase). This demonstrates the potential of the methodology to help downstream hospital departments cope with artificial variability.