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A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers
Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, which, in turn, could lead to patient dissatisfactio...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7277622/ https://www.ncbi.nlm.nih.gov/pubmed/32456329 http://dx.doi.org/10.3390/ijerph17103703 |
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author | Srinivas, Sharan |
author_facet | Srinivas, Sharan |
author_sort | Srinivas, Sharan |
collection | PubMed |
description | Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, which, in turn, could lead to patient dissatisfaction, reduced care quality, and physician burnout. However, rescheduling late arrivals could delay access to care. This paper aims to predict the patient-specific risk of late arrival using machine learning (ML) models. Data from two different ambulatory care facilities are extracted, and a comprehensive list of predictor variables is identified or derived from the electronic medical records. A comparative analysis of four ML algorithms (logistic regression, random forests, gradient boosting machine, and artificial neural networks) that differ in their training mechanism is conducted. The results indicate that ML algorithms can accurately predict patient lateness, but a single model cannot perform best with respect to predictive performance, training time, and interpretability. Prior history of late arrivals, age, and afternoon appointments are identified as critical predictors by all the models. The ML-based approach presented in this research can serve as a decision support tool and could be integrated into the appointment system for effectively managing and mitigating tardy arrivals. |
format | Online Article Text |
id | pubmed-7277622 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72776222020-06-12 A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers Srinivas, Sharan Int J Environ Res Public Health Article Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, which, in turn, could lead to patient dissatisfaction, reduced care quality, and physician burnout. However, rescheduling late arrivals could delay access to care. This paper aims to predict the patient-specific risk of late arrival using machine learning (ML) models. Data from two different ambulatory care facilities are extracted, and a comprehensive list of predictor variables is identified or derived from the electronic medical records. A comparative analysis of four ML algorithms (logistic regression, random forests, gradient boosting machine, and artificial neural networks) that differ in their training mechanism is conducted. The results indicate that ML algorithms can accurately predict patient lateness, but a single model cannot perform best with respect to predictive performance, training time, and interpretability. Prior history of late arrivals, age, and afternoon appointments are identified as critical predictors by all the models. The ML-based approach presented in this research can serve as a decision support tool and could be integrated into the appointment system for effectively managing and mitigating tardy arrivals. MDPI 2020-05-24 2020-05 /pmc/articles/PMC7277622/ /pubmed/32456329 http://dx.doi.org/10.3390/ijerph17103703 Text en © 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Srinivas, Sharan A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers |
title | A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers |
title_full | A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers |
title_fullStr | A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers |
title_full_unstemmed | A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers |
title_short | A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers |
title_sort | machine learning-based approach for predicting patient punctuality in ambulatory care centers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7277622/ https://www.ncbi.nlm.nih.gov/pubmed/32456329 http://dx.doi.org/10.3390/ijerph17103703 |
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