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

Allender, Steven, Hayward, Joshua, Gupta, Sunil, Sanigorski, Andrew, Rana, Santu, Seward, Hugh, Jacobs, Struan and Venkatesh, Svetha 2020, Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing, npj Digital Medicine, vol. 3, no. 1, pp. 1-8, doi: 10.1038/s41746-019-0205-y.

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Title Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing
Author(s) Allender, StevenORCID iD for Allender, Steven orcid.org/0000-0002-4842-3294
Hayward, JoshuaORCID iD for Hayward, Joshua orcid.org/0000-0001-8484-9930
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Sanigorski, AndrewORCID iD for Sanigorski, Andrew orcid.org/0000-0002-2858-4621
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Seward, Hugh
Jacobs, Struan
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name npj Digital Medicine
Volume number 3
Issue number 1
Article ID 7
Start page 1
End page 8
Total pages 8
Publisher Nature Publishing Group
Place of publication New York, N.Y.
Publication date 2020-01-20
ISSN 2398-6352
2398-6352
Keyword(s) Science & Technology
Life Sciences & Biomedicine
Health Care Sciences & Services
Medical Informatics
decision making
lifestyle modification
Summary 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.
Language eng
DOI 10.1038/s41746-019-0205-y
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30133729

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.