Histopathology laboratory operations analysis and improvement

Abstract

Histopathology laboratories aim to deliver high quality diagnoses based on patient tissue samples. Indicators for quality are the accuracy of the diagnoses and the diagnostic turnaround times. However, challenges exist regarding employee workload and turnaround times in the histopathology laboratory. This paper proposes a decomposed planning and scheduling method for the histopathology laboratory using (mixed) integer linear programming ((M)ILP) to improve the spread of workload and reduce the diagnostic turnaround times. First, the batching problem is considered, in which batch completion times are equally divided over the day to spread the workload. This reduces the peaks of physical work available in the laboratory. Thereafter, the remaining processes are scheduled to minimize the tardiness of orders. Preliminary results show that using this decomposition method, the peaks in histopathology workload in UMC Utrecht, a large university medical center in the Netherlands, are potentially reduced with up to 50% by better spreading the workload over the day. Furthermore, turnaround times are potentially reduced with up to 20% compared to current practices.