Abstract
Background
Care coordination involves multiple participants (healthcare providers and patients) working together to ensure the appropriate delivery of care. Face-to-face interactions between individuals is a critical component of care coordination in clinical settings. An accurate and detailed description and quantification of contacts provide key information about how providers interact with patients and for intervention development to improve care coordination. Using proximity data collected from Radio-Frequency IDentification(RFID) wearable sensors, this study aimed to understand network structure and interaction patterns in relation to different positional roles in care coordination.
Methods and Findings
Social network analysis was used to analyze a secondary dataset collected from RFID sensors that detect close-range face-to-face interactions between individuals in a geriatric unit of a university hospital in France. Two questions were explored: (1) how do interaction patterns differ related to different roles (nursing staff, doctors, patients and administrative staff)? And (2) how do the interaction patterns differ within the same roles on the geriatric unit? Over 4 days and nights, interaction patterns and network measures, such as centrality and cohesion, among providers (n = 46) and patients (n = 29) varied greatly depending on the shifts of day and different clinical roles.
Conclusion
Wearable sensors provide a novel way to objectively capture network data to understand ego-centric and system-level interaction patterns in care coordination. Real-time longitudinal measurement of face-to-face interactions can provide important information on structural mechanisms in relation to different role responsibilities and functions in the context of care coordination in geriatric clinical care settings.