Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing

Abstract Complex health problems require multi-strategy, multi-target interventions. We present a method that uses machine learning techniques to choose optimal interventions from a set of possible interventions within a case study aiming to increase General Practitioner (GP) discussions of physical...

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Autores principales:	S. Allender, J. Hayward, S. Gupta, A. Sanigorski, S. Rana, H. Seward, S. Jacobs, S. Venkatesh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/b6b0855aabc046839faaf63965a08e6c
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spelling	oai:doaj.org-article:b6b0855aabc046839faaf63965a08e6c2021-12-02T14:29:12ZBayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing10.1038/s41746-019-0205-y2398-6352https://doaj.org/article/b6b0855aabc046839faaf63965a08e6c2020-01-01T00:00:00Zhttps://doi.org/10.1038/s41746-019-0205-yhttps://doaj.org/toc/2398-6352Abstract Complex health problems require multi-strategy, multi-target interventions. We present a method that uses machine learning techniques to choose optimal interventions from a set of possible interventions within a case study aiming to increase General Practitioner (GP) discussions of physical activity (PA) with their patients. Interventions were developed based on a causal loop diagram with 26 GPs across 13 clinics in Geelong, Australia. GPs prioritised eight from more than 80 potential interventions to increase GP discussion of PA with patients. Following a 2-week baseline, a multi-arm bandit algorithm was used to assign optimal strategies to GP clinics with the target outcome being GP PA discussion rates. The algorithm was updated weekly and the process iterated until the more promising strategies emerged (a duration of seven weeks). The top three performing strategies were continued for 3 weeks to improve the power of the hypothesis test of effectiveness for each strategy compared to baseline. GPs recorded a total of 11,176 conversations about PA. GPs identified 15 factors affecting GP PA discussion rates with patients including GP skills and awareness, fragmentation of care and fear of adverse outcomes. The two most effective strategies were correctly identified within seven weeks of the algorithm-based assignment of strategies. These were clinic reception staff providing PA information to patients at check in and PA screening questionnaires completed in the waiting room. This study demonstrates an efficient way to test and identify optimal strategies from multiple possible solutions.S. AllenderJ. HaywardS. GuptaA. SanigorskiS. RanaH. SewardS. JacobsS. VenkateshNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 S. Allender J. Hayward S. Gupta A. Sanigorski S. Rana H. Seward S. Jacobs S. Venkatesh Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing
description	Abstract Complex health problems require multi-strategy, multi-target interventions. We present a method that uses machine learning techniques to choose optimal interventions from a set of possible interventions within a case study aiming to increase General Practitioner (GP) discussions of physical activity (PA) with their patients. Interventions were developed based on a causal loop diagram with 26 GPs across 13 clinics in Geelong, Australia. GPs prioritised eight from more than 80 potential interventions to increase GP discussion of PA with patients. Following a 2-week baseline, a multi-arm bandit algorithm was used to assign optimal strategies to GP clinics with the target outcome being GP PA discussion rates. The algorithm was updated weekly and the process iterated until the more promising strategies emerged (a duration of seven weeks). The top three performing strategies were continued for 3 weeks to improve the power of the hypothesis test of effectiveness for each strategy compared to baseline. GPs recorded a total of 11,176 conversations about PA. GPs identified 15 factors affecting GP PA discussion rates with patients including GP skills and awareness, fragmentation of care and fear of adverse outcomes. The two most effective strategies were correctly identified within seven weeks of the algorithm-based assignment of strategies. These were clinic reception staff providing PA information to patients at check in and PA screening questionnaires completed in the waiting room. This study demonstrates an efficient way to test and identify optimal strategies from multiple possible solutions.
format	article
author	S. Allender J. Hayward S. Gupta A. Sanigorski S. Rana H. Seward S. Jacobs S. Venkatesh
author_facet	S. Allender J. Hayward S. Gupta A. Sanigorski S. Rana H. Seward S. Jacobs S. Venkatesh
author_sort	S. Allender
title	Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing
title_short	Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing
title_full	Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing
title_fullStr	Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing
title_full_unstemmed	Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing
title_sort	bayesian strategy selection identifies optimal solutions to complex problems using an example from gp prescribing
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/b6b0855aabc046839faaf63965a08e6c
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Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing

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