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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Baker, AW; Haridy, S; Salem, J; IlieĊŸ, I; Ergai, AO; Samareh, A; Andrianas, N; Benneyan, JC; Sexton, DJ; Anderson, DJ

Published Date

  • August 2018

Published In

Volume / Issue

  • 27 / 8

Start / End Page

  • 600 - 610

PubMed ID

  • 29175853

Pubmed Central ID

  • 29175853

Electronic International Standard Serial Number (EISSN)

  • 2044-5423

Digital Object Identifier (DOI)

  • 10.1136/bmjqs-2017-006474

Language

  • eng

Conference Location

  • England