Outpatient Diversion using Real-Time Length-of-Stay Predictions

TL;DR

This paper proposes a real-time LOS prediction-based outpatient diversion system to optimize patient flow and resource utilization across multiple health centers, demonstrated through simulation in an Indian district.

Contribution

It introduces a novel real-time LOS prediction and diversion framework for outpatient care, improving resource utilization and equity across facilities.

Findings

Diversion based on LOS predictions reduces congestion disparities.

Simulation shows improved resource utilization and patient flow balance.

Framework is applicable to similar healthcare settings globally.

Abstract

In this work, we show how real-time length-of-stay (LOS) predictions can be used to divert outpatients from their assigned facility to other facilities with lesser congestion. We illustrate the implementation of this diversion mechanism for two primary health centers (PHCs), wherein we divert patients from their assigned PHC to the other PHC based on their predicted LOSs in both facilities. We develop a discrete-event simulation model of patient flow operations at these two PHCs in an Indian district and observe significantly longer LOSs at one of the PHCs due to disparities in the patient loads across both PHCs. We first determine the expected LOS of the patient at the point in time at which they are expected to arrive at a PHC using system state information recorded at the current time at the PHC in question. The real-time LOS predictions are generated by estimating patient wait times on a real-time basis at the queueing subsystems within the PHC. We then divert the patient to the appropriate PHC on the basis of the predicted LOS estimates at both PHCs, and show through simulation that the proposed framework leads to more equitable utilization of resources involved in provision of outpatient care.