Abstract
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 on how the nature of group interaction and communication processes contribute to the quality of team and task decision-making.
Highlights
Small group research in healthcare is important because it deals with interaction and decisionmaking processes that can help to identify and improve safer patient treatment and care
Context This study examined group interactions and communication processes related to healthcare management and patient safety using a role-play exercise
We examined whether the role-play exercise provides an environment that allows for a naturalistic group processes related to content addressing healthcare management and patient safety
Summary
Small group research in healthcare is important because it deals with interaction and decisionmaking processes that can help to identify and improve safer patient treatment and care. Small group research, which is embedded in the wider context of social psychology [8], focuses on such group dynamics In this setting, the term “groups” refers to Aufegger et al BMC Medical Research Methodology (2019) 19:121 social, collective entities that have a shared and common purpose. The term “groups” refers to Aufegger et al BMC Medical Research Methodology (2019) 19:121 social, collective entities that have a shared and common purpose This sense of shared purpose is expressed through coordinated task activities and within environments in which participants work together to accomplish a goal while experiencing a feeling of togetherness. Studies of small groups typically explore aspects of group related to formation and development, group structures, group communication and group decision-making [9, 10]
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