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Proactive Risk Assessment of Surgical Site Infections in Ambulatory Surgery Centers

Bish, Ebru K. PhD*; Azadeh-Fard, Nasibeh MS*; Steighner, Laura A. PhD; Hall, Kendall K. MD; Slonim, Anthony D. MD§

doi: 10.1097/PTS.0000000000000119
Original Articles
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Background The Socio-Technical Probabilistic Risk Assessment, a proactive risk assessment tool imported from high-risk industries, was used to identify risks for surgical site infections (SSIs) associated with the ambulatory surgery center setting and to guide improvement efforts.

Objectives This study had 2 primary objectives: (1) to identify the critical risk factors associated with SSIs resulting from procedures performed at ambulatory surgery centers and (2) to design an intervention to mitigate the probability of SSI for the highest risk factors identified.

Methods Inputs included quantitative and qualitative data sources from the evidence-based literature and from health care providers. The Socio-Technical Probabilistic Risk Assessment ranked the failure points (events) on the basis of their contribution to an SSI. The event, entitled “Failure to protect the patient effectively,” which included several failure points, was the most critical unique event with the highest contribution to SSI risk.

Results A total of 51.87% of SSIs in this setting were caused by this failure. Consequently, we proposed an intervention aimed at all 5 major components of this failure.

Conclusions The intervention targets improvements in skin preparation; proper administration of antibiotics; staff training in infection control principles, including practices for the prevention of glove punctures; and procedures to ensure the removal of watches, jewelry, and artificial nails.

From the *College of Engineering, Virginia Tech, Blacksburg, Virginia; †American Institutes for Research, Washington DC; ‡Agency for Healthcare Research and Quality, Rockville, MD; and §Barnabas Health and SAMIKE Consulting, West Orange, NJ.

Correspondence: Ebru K. Bish, PhD, Grado Department of Industrial and Systems Engineering, Virginia Tech, 250 Durham Hall, Blacksburg, VA 24061-0118 (e-mail: ebru@vt.edu).

The authors disclose no conflict of interest.

This project was funded under contract number HHSA290200600019I Task Order 12 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services.

The opinions expressed in this document are those of the authors and do not reflect the official position of the AHRQ or the U.S. Department of Health and Human Services.

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