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Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital

INTRODUCTION: A no-show appointment occurs when a patient does not attend a previously booked appointment. This situation can cause other problems, such as discontinuity of patient treatments as well as a waste of both human and financial resources. One of the latest approaches to address this issue...

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Autores principales: Ahmad Hamdan, Abdullah Fahim, Abu Bakar, Azuraliza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Penerbit Universiti Sains Malaysia 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10624443/
https://www.ncbi.nlm.nih.gov/pubmed/37928795
http://dx.doi.org/10.21315/mjms2023.30.5.14
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author Ahmad Hamdan, Abdullah Fahim
Abu Bakar, Azuraliza
author_facet Ahmad Hamdan, Abdullah Fahim
Abu Bakar, Azuraliza
author_sort Ahmad Hamdan, Abdullah Fahim
collection PubMed
description INTRODUCTION: A no-show appointment occurs when a patient does not attend a previously booked appointment. This situation can cause other problems, such as discontinuity of patient treatments as well as a waste of both human and financial resources. One of the latest approaches to address this issue is predicting no-shows using machine learning techniques. This study aims to propose a predictive analytical approach for developing a patient no-show appointment model in Hospital Kuala Lumpur (HKL) using machine learning algorithms. METHODS: This study uses outpatient data from the HKL’s Patient Management System (SPP) throughout 2019. The final data set has 246,943 appointment records with 13 attributes used for both descriptive and predictive analyses. The predictive analysis was carried out using seven machine learning algorithms, namely, logistic regression (LR), decision tree (DT), k-near neighbours (k-NN), Naïve Bayes (NB), random forest (RF), gradient boosting (GB) and multilayer perceptron (MLP). RESULTS: The descriptive analysis showed that the no-show rate was 28%, and attributes such as the month of the appointment and the gender of the patient seem to influence the possibility of a patient not showing up. Evaluation of the predictive model found that the GB model had the highest accuracy of 78%, F1 score of 0.76 and area under the curve (AUC) value of 0.65. CONCLUSION: The predictive model could be used to formulate intervention steps to reduce no-shows, improving patient care quality.
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spelling pubmed-106244432023-11-04 Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital Ahmad Hamdan, Abdullah Fahim Abu Bakar, Azuraliza Malays J Med Sci Original Article INTRODUCTION: A no-show appointment occurs when a patient does not attend a previously booked appointment. This situation can cause other problems, such as discontinuity of patient treatments as well as a waste of both human and financial resources. One of the latest approaches to address this issue is predicting no-shows using machine learning techniques. This study aims to propose a predictive analytical approach for developing a patient no-show appointment model in Hospital Kuala Lumpur (HKL) using machine learning algorithms. METHODS: This study uses outpatient data from the HKL’s Patient Management System (SPP) throughout 2019. The final data set has 246,943 appointment records with 13 attributes used for both descriptive and predictive analyses. The predictive analysis was carried out using seven machine learning algorithms, namely, logistic regression (LR), decision tree (DT), k-near neighbours (k-NN), Naïve Bayes (NB), random forest (RF), gradient boosting (GB) and multilayer perceptron (MLP). RESULTS: The descriptive analysis showed that the no-show rate was 28%, and attributes such as the month of the appointment and the gender of the patient seem to influence the possibility of a patient not showing up. Evaluation of the predictive model found that the GB model had the highest accuracy of 78%, F1 score of 0.76 and area under the curve (AUC) value of 0.65. CONCLUSION: The predictive model could be used to formulate intervention steps to reduce no-shows, improving patient care quality. Penerbit Universiti Sains Malaysia 2023-10 2023-10-30 /pmc/articles/PMC10624443/ /pubmed/37928795 http://dx.doi.org/10.21315/mjms2023.30.5.14 Text en © Penerbit Universiti Sains Malaysia, 2023 https://creativecommons.org/licenses/by/4.0/This work is licensed under the terms of the Creative Commons Attribution (CC BY) (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Original Article
Ahmad Hamdan, Abdullah Fahim
Abu Bakar, Azuraliza
Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital
title Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital
title_full Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital
title_fullStr Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital
title_full_unstemmed Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital
title_short Machine Learning Predictions on Outpatient No-Show Appointments in a Malaysia Major Tertiary Hospital
title_sort machine learning predictions on outpatient no-show appointments in a malaysia major tertiary hospital
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10624443/
https://www.ncbi.nlm.nih.gov/pubmed/37928795
http://dx.doi.org/10.21315/mjms2023.30.5.14
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