Monitoring Perioperative Services Using 3D Multi-Objective Performance Frontiers.

The Acute Care Surgery model has been widely adopted by hospitals across the United States, with Acute Care Surgery services managing Emergency General Surgery patients that were previously being treated by General Surgery. In this analysis, we evaluate the impact of an Acute Care Surgery service model on General Surgery at the University of Vermont Medical Center using three metrics: under-utilized time, spillover time, and a financial ratio of work Relative Value Units over clinical Full Time Equivalents. These metrics are evaluated and used to identify three-dimensional Pareto optimality of General Surgery prior to and after the October 2015 tactical allocation to the Acute Care Surgery model. Our analysis was further substantiated using a Markov Chain Monte Carlo model for Bayesian Inference. We applied multi-objective Pareto and Bayesian breakpoint analysis to three operating room metrics to assess the impact of new operating room management decisions. In the two-dimensional space of Fig. 2, panel a), the post-tactical allocation front lies closer to the origin representing more optimal solutions for productivity and under-utilized time. The post-tactical allocation front is also closer to the origin for productivity and spillover time as shown in the two-dimensional space of Fig. 2, panel b). The results of the three-dimensional multi-objective analysis of Fig. 3 illustrate that the GS post-tactical allocation Pareto-surface is contained within a much smaller volume of space than the GS pre-tactical allocation Pareto-surface. The post-tactical allocation Pareto-surface is slightly lower along the z-axis, representing lower productivity than the pre-tactical allocation surface. This methodology might contribute to the external benchmarking and monitoring of perioperative services by visualizing the operational implications following tactical decisions in operating room management.

The Power Law in Operating Room Management.

The Acute Care Surgery model has been implemented by many hospitals in the United States. As complex adaptive systems, healthcare systems are composed of many interacting elements that respond to intrinsic and extrinsic inputs. Systems level analysis may reveal the underlying organizational structure of tactical block allocations like the Acute Care Surgery model. The purpose of this study is to demonstrate one method to identify a key characteristic of complex adaptive systems in the perioperative services. Start and end times for all surgeries performed at the University of Vermont Medical Center OR1 were extracted for two years prior to the transition to an Acute Care Surgery service and two years following the transition. Histograms were plotted for the inter-event times calculated from the difference between surgical cases. A power law distribution was fit to the post-transition histogram. The Kolmogorov-Smirnov test for goodness-of-fit at 95% level of significance shows the histogram plotted from post-transition inter-event times follows a power law distribution (K-S = 0.088, p = 0.068), indicating a Complex Adaptive System. Our analysis demonstrates that the strategic decision to create an Acute Care Surgery service has direct implications on tactical and operational processes in the perioperative services. Elements of complex adaptive systems can be represented by a power law distributions and similar methods may be applied to identify other processes that operate as complex adaptive systems in perioperative care. To make sustained improvements in the perioperative services, focus on manufacturing-based interventions such as Lean Six Sigma should instead be shifted towards the complex interventions that modify system-specific behaviors described by complex adaptive system principles when power law relationships are present.