Urgent Samples in Clinical Laboratories: Stochastic Batching to Minimize Patient Turnaround Time

TL;DR

This paper develops stochastic batching strategies for clinical lab samples to reduce patient turnaround time, especially for vital samples, using models and simulations based on real hospital data.

Contribution

It introduces novel online and offline methods, including a stochastic mixed-integer quadratic programming model, to optimize centrifuge loading and improve TAT for vital samples.

Findings

Reducing median patient TAT for vital samples by 4.9 minutes.

Lowering the 0.95 quantile of TAT by 9.7 minutes.

Incorporating transport time distribution knowledge enhances batching decisions.

Abstract

This paper addresses the problem of batching laboratory samples in hospital laboratories where samples of different priorities are received continuously with uncertain transportation times. The focus is on optimizing the control strategy for loading a centrifuge to minimize patient turnaround time (TAT). While focusing on samples of patients in life-threatening situations (i.e., vital samples), we propose several online and offline methods, including a stochastic mixed-integer quadratic programming model integrated within a discrete-event system simulation. This paper aims to enhance patient care by providing timely laboratory results through improved batching strategies. The case study, which uses real data from a university hospital, demonstrates that incorporating distributional knowledge of transport times into our decision policy can reduce the median patient TAT of vital samples by 4.9 minutes and the 0.95 quantile by 9.7 minutes, but has no significant effect on low-priority samples. In addition, we show that this is essentially an optimal result by comparison with the upper bound obtained by a perfect-knowledge offline algorithm.