Prescriptive Analytics for Laboratory Workflow Optimisation: A Discrete-Event Simulation Approach to Reducing Diagnostic Bottlenecks in a Sub-Saharan African District Hospital

Clinical laboratory turnaround time (TAT) is a critical determinant of patient safety and clinical decision-making, yet prescriptive analytics approaches that recommend specific corrective actions remain underutilised in laboratory management. This study develops and evaluates a discrete-event simulation (DES)-based prescriptive analytics framework to identify and resolve workflow bottlenecks in a clinical chemistry laboratory at a district-level hospital in Ghana. A digital twin of the Liver Function Test (LFT) workflow was constructed using the R simmer package, parameterised from 1,615 LFT specimen records extracted over a five-month observation period, and validated against historical performance data. Baseline simulation revealed a compounded “Master Bottleneck” at the analytical stage, where 100% analyser utilisation drove the 95th-percentile (P95) TAT to 847.03 minutes and produced zero service level agreement (SLA) compliance. Single-factor resource interventions proved insufficient due to a serial bottleneck structure, confirmed by Cohen’s d effect sizes ranging from 0.00 to 32.22. A multi-factor prescriptive model comprising three phlebotomists, two analysers, and two technicians reduced P95 TAT by 17.52% (148.42 minutes) and transformed process stability, reducing the coefficient of variation from 543.8% to 0.003. The findings demonstrate that synchronised, multi-resource prescriptive optimisation substantially outperforms single-resource interventions, and that simulation-based decision support system (DSS) tools are feasible in resource-constrained district hospital settings.