A data-driven decision support tool to improve hospital bed cleaning logistics using discrete event simulation considering operators’ behaviour

Beds are a critical resource for hospitals, requiring effective management to ensure the quality of care for patients. Beds operate in a closed-loop circuit and must be thoroughly cleaned between patients’ arrivals to prevent infections. Hospitals must implement efficient logistics systems to collect, transport, store, and clean unclean beds from discharged patients. These systems must be robust and efficient to meet the varying bed supply needs, given the available resources such as beds, staff and machines. This study aims to develop a decision support tool to optimise bed cleaning logistics and ensure the availability of sterile beds for incoming patients at all times. The study is based on the bed flow and cleaning organisation of a Danish public hospital. A discrete event simulation model (DES) of the back-end bed flow has been developed. The paper also presents a tension level indicator to reflect the behaviour of cleaning staff when facing variations in demand and bed stock. Using the organisational set-up (staff schedules, policies, and bed fleet size), the DES model: 1) evaluates the ability to provide sterile beds in a reasonable time, 2) measures the stress on cleaning staff, and 3) visualises resource usage. This study illustrates how to incorporate the staff’s perceived workload and resulting behaviour into a DES model to capture the behavioural aspect of staff’s decision-making.