Artificial Intelligence Methods for Surgical Site Infection: Impacts on Detection, Monitoring, and Decision Making.

Background: There has been tremendous growth in the amount of new surgical site infection (SSI) data generated. Key challenges exist in understanding the data for robust clinical decision-support. Limitations of traditional methodologies to handle these data led to the emergence of artificial intelligence (AI). This article emphasizes the capabilities of AI to identify patterns of SSI data. Method: Artificial intelligence comprises various subfields that present potential solutions to identify patterns of SSI data. Discussions on opportunities, challenges, and limitations of applying these methods to derive accurate SSI prediction are provided. Results: Four main challenges in dealing with SSI data were defined: (1) complexities in using SSI data, (2) disease knowledge, (3) decision support, and (4) heterogeneity. The implications of some of the recent advances in AI methods to optimize clinical effectiveness were discussed. Conclusions: Artificial intelligence has the potential to provide insight in detecting and decision-support of SSI. As we turn SSI data into intelligence about the disease, we increase the possibility of improving surgical practice with the promise of a future optimized for the highest quality patient care.

Performance of statistical process control methods for regional surgical site infection surveillance: a 10-year multicentre pilot study.

BACKGROUND: Traditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal analysis with graphical presentation of data. METHODS: We performed a pilot study within a large network of community hospitals to evaluate performance of SPC methods for detecting SSI outbreaks. We applied conventional Shewhart and exponentially weighted moving average (EWMA) SPC charts to 10 previously investigated SSI outbreaks that occurred from 2003 to 2013. We compared the results of SPC surveillance to the results of traditional SSI surveillance methods. Then, we analysed the performance of modified SPC charts constructed with different outbreak detection rules, EWMA smoothing factors and baseline SSI rate calculations. RESULTS: Conventional Shewhart and EWMA SPC charts both detected 8 of the 10 SSI outbreaks analysed, in each case prior to the date of traditional detection. Among detected outbreaks, conventional Shewhart chart detection occurred a median of 12 months prior to outbreak onset and 22 months prior to traditional detection. Conventional EWMA chart detection occurred a median of 7months prior to outbreak onset and 14 months prior to traditional detection. Modified Shewhart and EWMA charts additionally detected several outbreaks earlier than conventional SPC charts. Shewhart and SPC charts had low false-positive rates when used to analyse separate control hospital SSI data. CONCLUSIONS: Our findings illustrate the potential usefulness and feasibility of real-time SPC surveillance of SSI to rapidly identify outbreaks and improve patient safety. Further study is needed to optimise SPC chart selection and calculation, statistical outbreak detection rules and the process for reacting to signals of potential outbreaks.