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

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 p...

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Autores principales: Evans, Luke, Acton, Jennifer H., Hiscott, Carla, Gartner, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832406/
https://www.ncbi.nlm.nih.gov/pubmed/36631506
http://dx.doi.org/10.1038/s41598-022-26755-1
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author Evans, Luke
Acton, Jennifer H.
Hiscott, Carla
Gartner, Daniel
author_facet Evans, Luke
Acton, Jennifer H.
Hiscott, Carla
Gartner, Daniel
author_sort Evans, Luke
collection PubMed
description 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.
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spelling pubmed-98324062023-01-11 An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool Evans, Luke Acton, Jennifer H. Hiscott, Carla Gartner, Daniel Sci Rep Article 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. Nature Publishing Group UK 2023-01-11 /pmc/articles/PMC9832406/ /pubmed/36631506 http://dx.doi.org/10.1038/s41598-022-26755-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Evans, Luke
Acton, Jennifer H.
Hiscott, Carla
Gartner, Daniel
An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool
title An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool
title_full An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool
title_fullStr An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool
title_full_unstemmed An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool
title_short An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool
title_sort operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832406/
https://www.ncbi.nlm.nih.gov/pubmed/36631506
http://dx.doi.org/10.1038/s41598-022-26755-1
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