USING RFID-BASED REAL-TIME LOCATION SYSTEMS TO DESCRIBE AND UNDERSTAND SOCIAL NETWORKS IN THE OUTPATIENT SETTING

Using RFID-based Real-time location systems to describe and understand social networks in the outpatient setting

Purpose: All technology change is culture change. In order to better understand and facilitate change in health care we need to better understand the complex social structure in clinical systems. Social network analysis can help us do this. Here we analyze social networks in outpatient clinics.

Methods: Time, location and co-location data was drawn from an RFID-based real-time location system deployed in several outpatient clinics from 2008-present. In these clinics patient and staff wear Radiofrequency (RFID) based transponders that record their location every 10 seconds. This allows us to map geographic paths and record episodes of co-location between staff. It is assumed co-location is associated with social interaction. Social network analysis was used to examine two types of medical clinic networks: 1) each clinic employee as a network node with undirected links between them representing co-location for a period of 45 seconds and 2) each location as a network node with directed links representing movement of a person from one location to the next location in time. Network measures including degree, betweeness, closeness, and structural holes. These measures help determine the centrality of the individual in the network and the most commonly traveled paths. Software: Pajek 3.01 32-bit version.

Results: Data reported below is for a one week sample of interactions in clinic designated Beta (n = 22 staff) in June 2011. MD2 had the widest range of connectedness with the rest of the staff, had the highest betweenness score and was the most central to the network as a whole. The Medical director was next highest in betweenenss.  MD6, Admin6 and MD7 were edge nodes connecting several social domains. The table below reports the nodes with the most interactions.

Conclusions: This feasibility trial of social network analysis in an outpatient clinic helps us identify key influencers in a complex clinical system. This in turn should help facilitate promoting change when necessary and resilience when needed in these clinical systems.