Imperial College London
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DrLisaAufegger

Faculty of Medicine, Institute of Global Health Innovation

Honorary Research Fellow
 
 
 
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Contact

 

l.aufegger

 
 
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Location

 

Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Aufegger:2019:10.1186/s12874-019-0756-2,
author = {Aufegger, L and Bicknell, C and Soane, E and Ashrafian, H and Darzi, A},
doi = {10.1186/s12874-019-0756-2},
journal = {BMC Medical Research Methodology},
title = {Understanding health management and safety decisions using signal processing and machine learning},
url = {http://dx.doi.org/10.1186/s12874-019-0756-2},
volume = {19},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundSmall 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.MethodsClinical 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.ResultsGroups 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.ConclusionsThis 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
AU  - Aufegger,L
AU  - Bicknell,C
AU  - Soane,E
AU  - Ashrafian,H
AU  - Darzi,A
DO  - 10.1186/s12874-019-0756-2
PY  - 2019///
SN  - 1471-2288
TI  - Understanding health management and safety decisions using signal processing and machine learning
T2  - BMC Medical Research Methodology
UR  - http://dx.doi.org/10.1186/s12874-019-0756-2
UR  - http://hdl.handle.net/10044/1/70647
VL  - 19
ER  -
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