Cookies?
Library Header Image
LSE Research Online LSE Library Services

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

Aufegger, Lisa, Bicknell, Colin, Soane, Emma, Ashrafian, Hutan and Darzi, Ara (2019) Understanding health management and safety decisions using signal processing and machine learning. BMC Medical Research Methodology, 19 (1). ISSN 1471-2288

[img] Text (Understanding health management and safety decisions using signal processing and machine learning) - Accepted Version
Available under License Creative Commons Attribution.

Download (1MB)

Identification Number: 10.1186/s12874-019-0756-2

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 team working 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 Recurrence Quantification Analysis (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.

Item Type: Article
Additional Information: © 2019 The Authors
Divisions: Management
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Date Deposited: 26 Jun 2019 23:10
Last Modified: 14 Sep 2019 23:11
URI: http://eprints.lse.ac.uk/id/eprint/101073

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics