Using Process Flow Disruption Analysis to Guide Quality Improvement.

BACKGROUND: Process flow describes the efficiency and consistency with which a process functions. Disruptions in surgical flow have been shown to be associated with an increase in error. Despite this, little experience exists in using surgical flow analysis to guide quality improvement (QI). STUDY DESIGN: In a 900-bed teaching hospital with an annual surgical volume of 24,000 cases, a 4-month observational study of process flow was done by experts in complex system evaluation. Identified flow disruptions were used to guide QI. Statistical analysis included descriptive and bivariate techniques. RESULTS: More than 200 unique process data points were evaluated. There was a high degree of variability in completion of 79 individual intraoperative data elements. Lack of completion of all elements of the time out was associated with number of times the operating room door opened during case (19, 11-27; p = 0.01). Flow disruptions were used to direct surgical QI. One example was a disruption affecting the use of Sugammadex. Resolving this flow disruption resulted in a 59% reduction in the incidence of postoperative respiratory failure (p < 0.01) and a direct and variable cost savings of $447,200 and $313,160, respectively, in the first 12 months. CONCLUSIONS: The use of process flow analysis to direct surgical quality initiatives is a novel approach that emphasizes system-level strategy. Resolving flow disruptions can lead to effective QI that embraces reliability by focusing attention on common processes rather than adverse events that may be unique and therefore difficult to apply broadly.

Learning to live with complexity.

Business life has always featured the unpredictable, the surprising, and the unexpected. But in today's hyperconnected world, complexity is the norm. Systems that used to be separate are now intertwined and interdependent, and knowing the starting conditions is no guide to predicting outcomes; too many continuously changing interactive elements are in play. Managers looking to navigate these difficulties need to adopt new approaches. They should drop outmoded forecasting tools-for example, ones that rely on averages, which are often less important than outliers. Instead, they should use models that simulate the behavior of the system. They should also make sure that their data include a good amount of future-oriented information. Risk mitigation is crucial as well. Managers should minimize the need to rely on predictions-for instance, they can give users a say in product design. They can decouple elements in a system and build in redundancy to minimize the consequences of a partial system failure, and turn to outside partners to extend their own company's capabilities. They can complement hard analysis with "soft" methods such as storytelling to make potentially important future possibilities more real. And they can make trade-offs that keep early failures small and provide the diversity of thought needed in a nimble organization faced with complexity on virtually every front.