Performance of statistical process control methods for regional surgical site infection surveillance: a 10-year multicentre pilot study. Issue 8 (24th November 2017)
- Record Type:
- Journal Article
- Title:
- Performance of statistical process control methods for regional surgical site infection surveillance: a 10-year multicentre pilot study. Issue 8 (24th November 2017)
- Main Title:
- Performance of statistical process control methods for regional surgical site infection surveillance: a 10-year multicentre pilot study
- Authors:
- Baker, Arthur W
Haridy, Salah
Salem, Joseph
Ilieş, Iulian
Ergai, Awatef O
Samareh, Aven
Andrianas, Nicholas
Benneyan, James C
Sexton, Daniel J
Anderson, Deverick J - Abstract:
- Abstract : 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 chartsAbstract : 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. … (more)
- Is Part Of:
- BMJ quality & safety. Volume 27:Issue 8(2018)
- Journal:
- BMJ quality & safety
- Issue:
- Volume 27:Issue 8(2018)
- Issue Display:
- Volume 27, Issue 8 (2018)
- Year:
- 2018
- Volume:
- 27
- Issue:
- 8
- Issue Sort Value:
- 2018-0027-0008-0000
- Page Start:
- 600
- Page End:
- 610
- Publication Date:
- 2017-11-24
- Subjects:
- statistical process control -- adverse events, epidemiology and detection -- healthcare quality improvement -- infection control
Medical care -- Quality control -- Periodicals
Health facilities -- Risk management -- Periodicals
Medical errors -- Prevention -- Periodicals
362.106805 - Journal URLs:
- http://www.bmj.com/archive ↗
http://qualitysafety.bmj.com/ ↗ - DOI:
- 10.1136/bmjqs-2017-006474 ↗
- Languages:
- English
- ISSNs:
- 2044-5415
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
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