Most clinical environments resemble a paradigmatic complex system with its dynamic and interactive collaborative work, non-linear and interdependent activities, and uncertainty. Addition of new organizational and systemic interventions, such as health IT, can cause considerable cascading effects in the clinical processes, workflow, and consequently, on throughput and efficiency. A 2011 IOM report [1] called for a socio-technical approach for designing and incorporating health IT in clinical settings. One of the critical aspects of a socio-technical approach is to understand the progression and evolution of human interactions within a socio-technical context. In this instructional workshop, we will discuss a set of convergent methodologies for analyzing human interactive behavior both with technology and with other humans or artifacts. These methodologies can help in capturing underlying patterns of human interactive behavior, and provide a mechanism to develop integrative, longitudinal metrics for clinical activities for sustained interactive episodes that evolve over time (for e.g., metrics related to performance, or errors). Such analysis of interactive behavior can also provide significant input to patient safety outcomes through the design of safe and efficient health IT. Specifically, we will (a) identify challenges to capturing and analyzing human interaction from complex clinical contexts; (b) discuss new approaches for capturing and analyzing sequences of human interaction in clinical settings using sequential analysis and network-theoretic, time-series based and probabilistic methods; (c) utilize one or more of these techniques to demonstrate their effectiveness as a viable mechanism for developing insights on clinical work activities through hands-on sessions; (d) provide participants hands-on experience in using data collection and data analysis tools; and (e) discuss the implications of these techniques for the design of health IT and patient safety initiatives.

Learning Objective 1: Identify new approaches, such as computational ethnography, for capturing and analyzing behavioral data including underlying patterns of sequential behavior, network-theoretic, and time-series characteristics

Learning Objective 2: Identify the challenges related to capturing and analyzing human interaction in clinical contexts


Thomas Kannampallil (Presenter)
University of Illinois

Kai Zheng (Presenter)
University of California at Irvine

Vimla Patel (Presenter)
The New York Academy of Medicine

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