Abstract: Surgical technology continues to improve patient outcomes, at the cost of increasing complexity. This increasing complexity of surgical technology may lead to increases in deviations from the expected operation progression, or flow disruptions (FD) (1-3). FDs indicate mismatches between work demands and the configuration of the supporting work system, and have the potential to negatively impact surgical outcomes, such as patient mortality, procedure duration, and surgical errors. FDs also result in negative effects on operating room teams, such as member stress (4) and increased perceived workload (5). In some situations, they may concatenate to trigger safety incidents.
Understanding of longitudinal patterns in FDs, in order to inform system improvements, requires application of quantitative tools to carefully collected data. The changepoint analysis allows us to identify the specific periods of time where the rate of FD is increased relative to a baseline or a desired operating range. To visualize the data, we use R/Shiny framework to develop an application for visualization of time stamped data. The Research and Exploratory Analysis Driven Time-data Visualization (READ-TV) application allows for user- friendly mining for longitudinal patterns in data. READ-TV is built specifically for FD analysis, but is easily adaptable to other clinical use cases, as we allow for the use of general metadata on events and cases. We have demonstrated the READ-TV application to the team of the AHRQ-funded Human Factors and Systems Integration in High Technology Surgery (HF-SIgHTS) study. The ability to visualize and perform quantitative analysis of the study data was received with unanimous positive feedback and enthusiasm. We continue READ-TV development focusing on (1) increased user-friendliness using the HF-SIgHTS as our focus group, (2) increased functionality, and (3) use of more general localization terminology to allow for other applications. The ability to visualize FD data will allow the HF-SIgHTS study team to incorporate the corresponding visualizations in developing interventions and training for surgical teams to increase their awareness and understand the sources of disruptions that affect surgical quality and team efficiency.
Describe the new knowledge and additional skills the participant will gain after attending your presentation.: The background information on importance of workflow disruptions (FD) in surgical quality will precede formal presentation of a tool that allows for visual and interactive analysis of disruption data. The learner will understand the application of the Poisson stochastic processes in analysis of FD data and how gained insights may lead to development of team interventions aimed at improving performance and ultimately safety.
John Del Gaizo (Presenter)
Medical University of South Carolina
Ken Catchpole, Medical University of South Carolina
Alexander Alekseyenko, Medical University of South Carolina