Social Networks of Staff in an Emergency Department

Objective: To measure problem-solving, medication advice-seeking and socialising networks in an emergency department (ED). Background: Good communication is core to high quality patient care and dependent upon effective information networks connecting clinical staff. Despite their central role in safe health care provision, ED information networks have rarely been studied. Clinical information systems are designed to support information flow and decision-making. Yet evidence is emerging that such systems may disrupt communication, contributing to errors. To identify changes it is necessary to compare communication before and after system introduction. This paper reports the use of a social network approach to examine networks in an Australian hospital ED before introduction of an electronic medication management system (e-MMS). Methods: A social network questionnaire was completed by 94% of staff (including doctors, nurses, allied health) who worked in the ED at a teaching hospital (n=109). Survey data were analysed using social network measures. Sociograms were produced to display the networks connecting staff in ED. Results: With few exceptions, members of staff tend to rely on colleagues from their own profession for help to solve problems, for medication advice, and for socialising. However, in each network key individuals provide help and medication advice to members from all professional groups. Overall, the number of individuals with whom people interact, and the average frequency of interaction are quite low across all networks studied. Staff were more likely to interact to solve problems and for medication advice than they were to socialise. Discussion: Given the relatively limited extent to which professionals working in the ED interact currently regarding medication tasks, information and communication technologies have the potential for improving access to up-to-date and relevant decision-making information which should improve the safety of medication tasks. Detailed and systematic analyses of information networks provide valuable data for use in the design, improvement and monitoring of the effectiveness of such interventions.