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.