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

R. F. M. Vromans (), N. Kortbeek, L. Schoonhoven, B. Bosch and M. Houdenhoven
Additional contact information
R. F. M. Vromans: University of Twente
N. Kortbeek: University of Twente
L. Schoonhoven: Sint Maartenskliniek
B. Bosch: Sint Maartenskliniek
M. Houdenhoven: Sint Maartenskliniek

A chapter in Handbook of Healthcare Logistics, 2021, pp 183-199 from Springer

Abstract: Abstract 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.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-60212-3_10

Ordering information: This item can be ordered from
http://www.springer.com/9783030602123

DOI: 10.1007/978-3-030-60212-3_10

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().