An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool

Evans, Luke, Acton, Jennifer H. ORCID: https://orcid.org/0000-0002-0347-7651, Hiscott, Carla and Gartner, Daniel ORCID: https://orcid.org/0000-0003-4361-8559 2023. An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool. Scientific Reports 13 , 553. 10.1038/s41598-022-26755-1

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

Inefficient management of resources and waiting lists for high-risk ophthalmology patients can contribute to sight loss. The aim was to develop a decision support tool which determines an optimal patient schedule for ophthalmology patients. Our approach considers available booking slots as well as patient-specific factors. Using standard software (Microsoft Excel and OpenSolver), an operations research approach was used to formulate a mathematical model. Given a set of patients and clinic capacities, the model objective was to schedule patients efficiently depending on eyecare measure risk factors, referral-to-treatment times and targets, patient locations and slot availabilities over a pre-defined planning horizon. Our decision support tool can feedback whether or not a patient is scheduled. If a patient is scheduled, the tool determines the optimal date and location to book the patients’ appointments, with a score provided to show the associated value of the decisions made. Our dataset from 519 patients showed optimal prioritization based on location, risk of serious vision loss/damage and the referral-to-treatment time. Given the constraints of available slots, managers can input hospital-specific parameters such as demand and capacity into our model. The model can be applied and implemented immediately, without the need for additional software, to generate an optimized patient schedule.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Mathematics Optometry and Vision Sciences
Publisher:	Nature Research
ISSN:	2045-2322
Date of First Compliant Deposit:	10 January 2023
Date of Acceptance:	20 December 2022
Last Modified:	20 May 2023 06:18
URI:	https://orca.cardiff.ac.uk/id/eprint/155621

Actions (repository staff only)

Edit Item