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Understanding health management and safety decisions using signal processing and machine learning

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Title: Understanding health management and safety decisions using signal processing and machine learning
Authors: Aufegger, L
Bicknell, C
Soane, E
Ashrafian, H
Darzi, A
Item Type: Journal Article
Abstract: Background Small group research in healthcare is important because it deals with interaction and decision-making processes that can help to identify and improve safer patient treatment and care. However, the number of studies is limited due to time- and resource-intensive data processing. The aim of this study was to examine the feasibility of using signal processing and machine learning techniques to understand teamwork and behaviour related to healthcare management and patient safety, and to contribute to literature and research of teamwork in healthcare. Methods Clinical and non-clinical healthcare professionals organised into 28 teams took part in a video- and audio-recorded role-play exercise that represented a fictional healthcare system, and included the opportunity to discuss and improve healthcare management and patient safety. Group interactions were analysed using the recurrence quantification analysis (RQA; Knight et al., 2016), a signal processing method that examines stability, determinism, and complexity of group interactions. Data were benchmarked against self-reported quality of team participation and social support. Transcripts of group conversations were explored using the topic modelling approach (Blei et al., 2003), a machine learning method that helps to identify emerging themes within large corpora of qualitative data. Results Groups exhibited stable group interactions that were positively correlated with perceived social support, and negatively correlated with predictive behaviour. Data processing of the qualitative data revealed conversations focused on: (1) the management of patient incidents; (2) the responsibilities among team members; (3) the importance of a good internal team environment; and (4) the hospital culture. Conclusions This study has shed new light on small group research using signal processing and machine learning methods. Future studies are encouraged to use these methods in the healthcare context, and to conduct further research on how the nature of group interaction and communication processes contribute to the quality of team and task decision-making.
Issue Date: 13-Jun-2019
Date of Acceptance: 21-May-2019
URI: http://hdl.handle.net/10044/1/70647
DOI: https://dx.doi.org/10.1186/s12874-019-0756-2
ISSN: 1471-2288
Publisher: BioMed Central
Journal / Book Title: BMC Medical Research Methodology
Volume: 19
Copyright Statement: © 2019 The Author(s). Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Sponsor/Funder: National Institute of Health Research
National Institute for Health Research
Funder's Grant Number: n/a
Keywords: General & Internal Medicine
1117 Public Health and Health Services
Publication Status: Published
Article Number: 121
Appears in Collections:Department of Surgery and Cancer
Institute of Global Health Innovation