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Overbooking for physical examination considering late cancellation and set-resource relationship

BACKGROUND: Late cancellations of physical examination has severe impact on the operations of a physical examination center since it is often too late to fill vacancy. A booking control policy that considers overbooking is then one natural solution. Unlike appointment scheduling problems for clinics...

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Autores principales: Ho, Te-Wei, Kung, Ling-Chieh, Huang, Hsin-Ya, Lai, Jui-Fen, Chiu, Han-Mo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605579/
https://www.ncbi.nlm.nih.gov/pubmed/34801021
http://dx.doi.org/10.1186/s12913-021-07148-y
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author Ho, Te-Wei
Kung, Ling-Chieh
Huang, Hsin-Ya
Lai, Jui-Fen
Chiu, Han-Mo
author_facet Ho, Te-Wei
Kung, Ling-Chieh
Huang, Hsin-Ya
Lai, Jui-Fen
Chiu, Han-Mo
author_sort Ho, Te-Wei
collection PubMed
description BACKGROUND: Late cancellations of physical examination has severe impact on the operations of a physical examination center since it is often too late to fill vacancy. A booking control policy that considers overbooking is then one natural solution. Unlike appointment scheduling problems for clinics and hospitals, in which treating a patient mostly requires only one type of resource, a physical examination set typically requires multiple types of resources. Traditional methods that do not consider set-resource relationship thus may be inapplicable. METHODS: We formulate a stochastic mathematical programming model that maximizes the expected net reward, which is the examination revenue minus overage cost. A complete search algorithm and a greedy search algorithm are designed to search for optimal booking limits for all examination sets. To estimate the late cancellation probability for each individual consumer, we apply logistic regression to identify significant factors affecting the probability. After clustering is used to estimate individual probabilities, Monte Carlo simulation is conducted to generate probability distributions for the number of consumers without late cancellations. A discrete-event simulation is performance to evaluate the effectiveness of our proposed solution. RESULTS: We collaborate with a leading physical examination center to collect real data to evaluate our proposed overbooking policies. We show that the proposed overbooking policy may significantly increase the expected net reward. Our simulation results also help us understand the impact of overbooking on the expected number of customers and expected overage. A sensitivity analysis is conducted to demonstrate that the benefit of overbooking is insensitive to the accuracy of cost estimation. A Pareto efficiency analysis gives practitioners suggestions regarding policy determination considering multiple performance indications. CONCLUSIONS: Our proposed overbooking policies may greatly enhance the overall performance of a physical examination center.
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spelling pubmed-86055792021-11-22 Overbooking for physical examination considering late cancellation and set-resource relationship Ho, Te-Wei Kung, Ling-Chieh Huang, Hsin-Ya Lai, Jui-Fen Chiu, Han-Mo BMC Health Serv Res Research Article BACKGROUND: Late cancellations of physical examination has severe impact on the operations of a physical examination center since it is often too late to fill vacancy. A booking control policy that considers overbooking is then one natural solution. Unlike appointment scheduling problems for clinics and hospitals, in which treating a patient mostly requires only one type of resource, a physical examination set typically requires multiple types of resources. Traditional methods that do not consider set-resource relationship thus may be inapplicable. METHODS: We formulate a stochastic mathematical programming model that maximizes the expected net reward, which is the examination revenue minus overage cost. A complete search algorithm and a greedy search algorithm are designed to search for optimal booking limits for all examination sets. To estimate the late cancellation probability for each individual consumer, we apply logistic regression to identify significant factors affecting the probability. After clustering is used to estimate individual probabilities, Monte Carlo simulation is conducted to generate probability distributions for the number of consumers without late cancellations. A discrete-event simulation is performance to evaluate the effectiveness of our proposed solution. RESULTS: We collaborate with a leading physical examination center to collect real data to evaluate our proposed overbooking policies. We show that the proposed overbooking policy may significantly increase the expected net reward. Our simulation results also help us understand the impact of overbooking on the expected number of customers and expected overage. A sensitivity analysis is conducted to demonstrate that the benefit of overbooking is insensitive to the accuracy of cost estimation. A Pareto efficiency analysis gives practitioners suggestions regarding policy determination considering multiple performance indications. CONCLUSIONS: Our proposed overbooking policies may greatly enhance the overall performance of a physical examination center. BioMed Central 2021-11-20 /pmc/articles/PMC8605579/ /pubmed/34801021 http://dx.doi.org/10.1186/s12913-021-07148-y Text en © The Author(s) 2021 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Ho, Te-Wei
Kung, Ling-Chieh
Huang, Hsin-Ya
Lai, Jui-Fen
Chiu, Han-Mo
Overbooking for physical examination considering late cancellation and set-resource relationship
title Overbooking for physical examination considering late cancellation and set-resource relationship
title_full Overbooking for physical examination considering late cancellation and set-resource relationship
title_fullStr Overbooking for physical examination considering late cancellation and set-resource relationship
title_full_unstemmed Overbooking for physical examination considering late cancellation and set-resource relationship
title_short Overbooking for physical examination considering late cancellation and set-resource relationship
title_sort overbooking for physical examination considering late cancellation and set-resource relationship
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605579/
https://www.ncbi.nlm.nih.gov/pubmed/34801021
http://dx.doi.org/10.1186/s12913-021-07148-y
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