# Urgent samples in clinical laboratories: stochastic batching to minimize patient turnaround time

## TL;DR

This paper improves hospital lab efficiency by optimizing sample batching to reduce turnaround time for urgent patient tests.

## Contribution

A novel stochastic mixed-integer quadratic programming model integrated with discrete-event simulation for urgent sample batching.

## Key findings

- Incorporating transport time distributions reduces median TAT for vital samples by 4.9 minutes.
- The 0.95 quantile of TAT for vital samples is reduced by 9.7 minutes.
- The results are nearly optimal when compared to a perfect-knowledge offline algorithm.

## 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.