Optimized staff allocation for inpatient phlebotomy and electrocardiography services via mathematical modelling in an acute regional and teaching hospital

Adhering to pre‐defined service routes that cover a fixed set of wards in a shift, the inpatient phlebotomy service provides 24‐hour coverage for a 27‐storey, 1,400‐bed hospital. We present an application of mathematical optimization to improve its service efficiency without injecting additional resources. A mixed integer programming model was implemented to revamp the service route configuration to minimize workload discrepancies among service routes, limit maximum daily workload per route and restrict routes to span a maximum number of floor levels, while taking into consideration the ward‐specific demand for each duty (i.e. daytime, evening, and night time) throughout the day. This data‐driven and evidence‐based approach has facilitated an overhaul of the existing route configuration of the inpatient phlebotomy service, which resulted in a more effective and contented workforce, as well as a more efficient service with an evened‐out workload among phlebotomists and increased time spent on direct patient care by phlebotomists. Subsequent scenario analysis revealed that more manpower on a micro‐level is not necessarily better and highlighted the importance to strategically design duty hours and allocate manpower across different duties on a system level.