Health care improvement efforts often focus on changing the behavior of individuals while the interdependencies among individuals are overlooked. The application of complex adaptive systems approach to studying healthcare delivery changes the focus of improvement efforts from the individual to the relationships and interdependencies among individuals in the system. Sensemaking and improvising are social activities that take place in the context of relationships between individuals. We explore the impact of sensemaking and improvising on patient outcomes in healthcare settings. We conducted an in-depth observational study of relationships in inpatient general medicine teams. Data collection focused on examining associations between team relationship behaviors of sensemaking and improvising and patient outcomes. Data analysis revealed that these activities were positively associated with patient outcomes. Based on these observational data and findings, we developed an agent-based model to further explore and clarify the relationships between sensemaking and improvising on physician teams and patient outcomes. Specifically, we used the agent-based model to simulate the impact of variation in physicians' sensemaking and improvisation abilities on patient length of stay, the likelihood of a patient worsening or developing complications, the need for transfer to a higher level of care, and mortality. Our in-depth study indicates that systematic differences in patient outcomes are associated with differences in the capacity of physician teams to effectively make sense and improvise in the dynamic conditions inherent in healthcare systems. The results of our simulation demonstrate that an agent-based modeling approach is feasible and useful for exploring the impact of physician team behaviors on patient outcomes. This finding suggests the need for new tools and approaches to improve sensemaking and improvisation in physician care teams as strategies to improve patient outcomes.
['Luci K. Leykum', 'Pradeep Kumar', 'Michael L. Parchman', 'Reuben R. McDaniel', 'Holly Jordan Lanham', 'Michael Agar']
Use of an agent-based model to understand clinical systems