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Improving healthcare access management by predicting patient no-show behaviour

Improving healthcare access management by predicting patient no-show behaviour
Improving healthcare access management by predicting patient no-show behaviour
Low attendance levels in medical appointments have been associated with poor health outcomes and efficiency problems for service providers. To address this problem, healthcare managers could aim at improving attendance levels or minimizing the operational impact of no-shows by adapting resource allocation policies. However, given the uncertainty of patient behaviour, generating relevant information regarding no-show probabilities could support the decision-making process for both approaches. In this context many researchers have used multiple regression models to identify patient and appointment characteristics than can be used as good predictors for no-show probabilities. This work develops a Decision Support System (DSS) to support the implementation of strategies to encourage attendance, for a preventive care program targeted at underserved communities in Bogotá, Colombia. Our contribution to literature is threefold. Firstly, we assess the effectiveness of different machine learning approaches to improve the accuracy of regression models. In particular, Random Forest and Neural Networks are used to model the problem accounting for non-linearity and variable interactions. Secondly, we propose a novel use of Layer-wise Relevance Propagation in order to improve the explainability of neural network predictions and obtain insights from the modelling step. Thirdly, we identify variables explaining no-show probabilities in a developing context and study its policy implications and potential for improving healthcare access. In addition to quantifying relationships reported in previous studies, we find that income and neighbourhood crime statistics affect no-show probabilities. Our results will support patient prioritization in a pilot behavioural intervention and will inform appointment planning decisions.
Analytics; No-show prediction; Healthcare access; Design science research
0167-9236
Barrera Ferro, David
a2a88e6f-4a2f-4fc2-9b8a-9b380732e47e
Brailsford, Sally
634585ff-c828-46ca-b33d-7ac017dda04f
Bravo, Christian
78a3b4d4-829a-46c4-bc10-30d7a7e7cbb6
Smith, Honora
1eaef6a6-4b9c-4997-9163-137b956c06b5
Barrera Ferro, David
a2a88e6f-4a2f-4fc2-9b8a-9b380732e47e
Brailsford, Sally
634585ff-c828-46ca-b33d-7ac017dda04f
Bravo, Christian
78a3b4d4-829a-46c4-bc10-30d7a7e7cbb6
Smith, Honora
1eaef6a6-4b9c-4997-9163-137b956c06b5

Barrera Ferro, David, Brailsford, Sally, Bravo, Christian and Smith, Honora (2020) Improving healthcare access management by predicting patient no-show behaviour. Decision Support Systems, 138, [113398]. (doi:10.1016/j.dss.2020.113398).

Record type: Article

Abstract

Low attendance levels in medical appointments have been associated with poor health outcomes and efficiency problems for service providers. To address this problem, healthcare managers could aim at improving attendance levels or minimizing the operational impact of no-shows by adapting resource allocation policies. However, given the uncertainty of patient behaviour, generating relevant information regarding no-show probabilities could support the decision-making process for both approaches. In this context many researchers have used multiple regression models to identify patient and appointment characteristics than can be used as good predictors for no-show probabilities. This work develops a Decision Support System (DSS) to support the implementation of strategies to encourage attendance, for a preventive care program targeted at underserved communities in Bogotá, Colombia. Our contribution to literature is threefold. Firstly, we assess the effectiveness of different machine learning approaches to improve the accuracy of regression models. In particular, Random Forest and Neural Networks are used to model the problem accounting for non-linearity and variable interactions. Secondly, we propose a novel use of Layer-wise Relevance Propagation in order to improve the explainability of neural network predictions and obtain insights from the modelling step. Thirdly, we identify variables explaining no-show probabilities in a developing context and study its policy implications and potential for improving healthcare access. In addition to quantifying relationships reported in previous studies, we find that income and neighbourhood crime statistics affect no-show probabilities. Our results will support patient prioritization in a pilot behavioural intervention and will inform appointment planning decisions.

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Accepted/In Press date: 19 August 2020
e-pub ahead of print date: 25 August 2020
Published date: November 2020
Additional Information: Funding Information: The first author's research is partially funded by a PhD scholarship from the healthcare research stream of the program Colombia Científica – Pasaporte a la Ciencia , granted by the Colombian Institute for Educational Technical Studies Abroad (Instituto Colombiano de Crédito Educativo y Estudios Técnicos en el Exterior, ICETEX). The third author acknowledges this research was undertaken, in part, thanks to funding from the Canada Research Chairs program. Publisher Copyright: © 2020 Elsevier B.V.
Keywords: Analytics; No-show prediction; Healthcare access; Design science research

Identifiers

Local EPrints ID: 443576
URI: http://eprints.soton.ac.uk/id/eprint/443576
ISSN: 0167-9236
PURE UUID: f2bf4ec1-0ecf-4751-a572-857a8dd4f2ad
ORCID for Sally Brailsford: ORCID iD orcid.org/0000-0002-6665-8230
ORCID for Honora Smith: ORCID iD orcid.org/0000-0002-4974-3011

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Date deposited: 03 Sep 2020 01:47
Last modified: 29 Oct 2022 04:03

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Contributors

Author: David Barrera Ferro
Author: Christian Bravo
Author: Honora Smith ORCID iD

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