Operating room coordination with the eWhiteboard: the fine line between successful and challenged technology adoption

With the goal to achieve a better understanding of the challenges to communication technology adoption in the clinical setting, we conducted intensive observations of activities of clinical and administrative staff in a large teaching hospital’s surgical unit prior to and 8-months post-implementation of an electronic whiteboard communication tool (eWhiteboard). The hospital IT department developed the e-Whiteboard for the support of inter-team coordination of patient status within the surgical flow. After the system had been integrated into the work process for 8 months, we conducted another round of intensive observations. The RATE data collection platform was utilized in both studies. Data were coded and analyzed quantitatively. Qualitative observational notes complemented the statistical results. We compared the pre- and post-implementation observational data with regard to communication load, types of coordination breakdowns, triggers, and consequences. Additionally, a questionnaire on perceived usefulness of the eWhiteboard was administered. We found that the introduction of the electronic whiteboard reduced the communication load associated with patient care. Participants reported the application useful and the overall satisfaction was rated very high. However, the observational data indicated an increase in coordination breakdowns related to patient status and the rise of an unanticipated communication load enabler and interruptions source – the lack of trust in other humans. We conclude that the types of metrics used to evaluate technology adoption may very well influence the outcomes reported. In the environment we studied, we identified a socio-technical issue, lack of trust in other humans, as the principle challenge to what would have otherwise been termed successful adoption. One implication is the development of a predictive model of user experience, including trust, based on strong candidate predictors derived from meta-analysis of existing related studies.

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