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 called for a sociotechnical approach for designing and incorporating health IT in clinical settings. One of the critical aspects of a sociotechnical approach is to understand the progression and evolution of human interactions within a sociotechnical 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 (e.g., metrics related to performance, or errors) for clinical activities for sustained interactive episodes that evolve over time. In addition, such analysis of interactive behavior can also provide significant input for patient safety outcomes through the design of safer and more efficient health IT. In this workshop, we will (a) identify challenges to studying human interactive behavior in complex clinical contexts; (b) discuss new approaches for capturing and analyzing sequences of human interaction using sequential analysis and network-theoretic, time-series based 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; (e) discuss how research on human interaction informs the cognitive design of clinical information systems to improve their usability and support for collaborative team work; and (f) discuss the implications of these techniques for new care delivery models and patient safety initiatives.
Learning Objective: After participating in this session, the learner should be better able to:
(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;
(2) Identify the challenges related to capturing and analyzing human interaction in clinical contexts;
(3) Develop a preliminary understanding of using off-the-shelf applications to perform these analyses;
(4) Understand the implication of using these techniques for health IT design and patient safety endeavors;
(5) Structure their data into formats that are amenable to these analyses: for researchers, healthcare professionals, and hospital administrators.
Kai Zheng (Presenter)
University of California, Irvine
Thomas Kannampallil (Presenter)
Washington University in St Louis
Jan Horsky (Presenter)
Vimla Patel (Presenter)
The New York Academy of Medicine