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Developing and validating a multivariable prediction model for predicting the cost of colon surgery

Hospitals are burdened with predicting, calculating, and managing various cost-affecting parameters regarding patients and their treatments. Accuracy in cost prediction is further affected when a patient suffers from other health issues that hinder the traditional prognosis. This can lead to an unav...

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Autores principales: Taha, Anas, Taha-Mehlitz, Stephanie, Ochs, Vincent, Enodien, Bassey, Honaker, Michael D., Frey, Daniel M., Cattin, Philippe C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676240/
https://www.ncbi.nlm.nih.gov/pubmed/36420401
http://dx.doi.org/10.3389/fsurg.2022.939079
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author Taha, Anas
Taha-Mehlitz, Stephanie
Ochs, Vincent
Enodien, Bassey
Honaker, Michael D.
Frey, Daniel M.
Cattin, Philippe C.
author_facet Taha, Anas
Taha-Mehlitz, Stephanie
Ochs, Vincent
Enodien, Bassey
Honaker, Michael D.
Frey, Daniel M.
Cattin, Philippe C.
author_sort Taha, Anas
collection PubMed
description Hospitals are burdened with predicting, calculating, and managing various cost-affecting parameters regarding patients and their treatments. Accuracy in cost prediction is further affected when a patient suffers from other health issues that hinder the traditional prognosis. This can lead to an unavoidable deficit in the final revenue of medical centers. This study aims to determine whether machine learning (ML) algorithms can predict cost factors based on patients undergoing colon surgery. For the forecasting, multiple predictors will be taken into the model to provide a tool that can be helpful for hospitals to manage their costs, ultimately leading to operating more cost-efficiently. This proof of principle will lay the groundwork for an efficient ML-based prediction tool based on multicenter data from a range of international centers in the subsequent phases of the study. With a mean absolute percentage error result of 18%–25.6%, our model's prediction showed decent results in forecasting the costs regarding various diagnosed factors and surgical approaches. There is an urgent need for further studies on predicting cost factors, especially for cases with anastomotic leakage, to minimize unnecessary hospital costs.
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spelling pubmed-96762402022-11-22 Developing and validating a multivariable prediction model for predicting the cost of colon surgery Taha, Anas Taha-Mehlitz, Stephanie Ochs, Vincent Enodien, Bassey Honaker, Michael D. Frey, Daniel M. Cattin, Philippe C. Front Surg Surgery Hospitals are burdened with predicting, calculating, and managing various cost-affecting parameters regarding patients and their treatments. Accuracy in cost prediction is further affected when a patient suffers from other health issues that hinder the traditional prognosis. This can lead to an unavoidable deficit in the final revenue of medical centers. This study aims to determine whether machine learning (ML) algorithms can predict cost factors based on patients undergoing colon surgery. For the forecasting, multiple predictors will be taken into the model to provide a tool that can be helpful for hospitals to manage their costs, ultimately leading to operating more cost-efficiently. This proof of principle will lay the groundwork for an efficient ML-based prediction tool based on multicenter data from a range of international centers in the subsequent phases of the study. With a mean absolute percentage error result of 18%–25.6%, our model's prediction showed decent results in forecasting the costs regarding various diagnosed factors and surgical approaches. There is an urgent need for further studies on predicting cost factors, especially for cases with anastomotic leakage, to minimize unnecessary hospital costs. Frontiers Media S.A. 2022-11-07 /pmc/articles/PMC9676240/ /pubmed/36420401 http://dx.doi.org/10.3389/fsurg.2022.939079 Text en © 2022 Taha, Taha-Mehlitz, Ochs, Enodien, Honaker, Frey and Cattin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Surgery
Taha, Anas
Taha-Mehlitz, Stephanie
Ochs, Vincent
Enodien, Bassey
Honaker, Michael D.
Frey, Daniel M.
Cattin, Philippe C.
Developing and validating a multivariable prediction model for predicting the cost of colon surgery
title Developing and validating a multivariable prediction model for predicting the cost of colon surgery
title_full Developing and validating a multivariable prediction model for predicting the cost of colon surgery
title_fullStr Developing and validating a multivariable prediction model for predicting the cost of colon surgery
title_full_unstemmed Developing and validating a multivariable prediction model for predicting the cost of colon surgery
title_short Developing and validating a multivariable prediction model for predicting the cost of colon surgery
title_sort developing and validating a multivariable prediction model for predicting the cost of colon surgery
topic Surgery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676240/
https://www.ncbi.nlm.nih.gov/pubmed/36420401
http://dx.doi.org/10.3389/fsurg.2022.939079
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