The application of Lean Six Sigma methodology to reduce the risk of healthcare–associated infections in surgery departments

Abstract

Rationale: Nowadays, the monitoring and prevention of healthcare–associated infections (HAIs) is a priority for the healthcare sector. Aims and objectives: In this article, we report on the application of the Lean Six Sigma (LSS) methodology to reduce the number of patients affected by sentinel bacterial infections who are at risk of HAI. Methods: The LSS methodology was applied in the general surgery department by using a multidisciplinary team of both physicians and academics. Data on more than 20 000 patients who underwent a wide range of surgical procedures between January 2011 and December 2014 were collected to conduct the study using the departmental information system. The most prevalent sentinel bacteria were determined among the infected patients. The preintervention (January 2011 to December 2012) and postintervention (January 2013 to December 2014) phases were compared to analyze the effects of the methodology implemented. The methodology allowed the identification of variables that influenced the risk of HAIs and the implementation of corrective actions to improve the care process, thereby reducing the percentage of infected patients. Results: The improved process resulted in a 20% reduction in the average number of hospitalization days between preintervention and control phases, and a decrease in the mean (SD) number of days of hospitalization amounted to 36 (15.68), with a data distribution around 3 σ. The LSS is a helpful strategy that ensures a significant decrease in the number of HAIs in patients undergoing surgical interventions. The implementation of this intervention in the general surgery departments resulted in a significant reduction in both the number of hospitalization days and the number of patients affected by HAIs. Conclusions: This approach, together with other tools for reducing the risk of infection (surveillance, epidemiological guidelines, and training of healthcare personnel), could be applied to redesign and improve a wide range of healthcare processes.