Implementing a Semi-Automated Method for Surgical Site Infections Monitoring in a Limited Setting: The SPICMI Method...

Area of Science:

• Healthcare Epidemiology
• Infectious Disease Surveillance
• Medical Informatics

Background:

• Surgical Site Infections (SSIs) are a significant component of Healthcare-Associated Infections (HAIs).
• National surveillance programs like SPICMI in France aim to monitor SSIs using medico-administrative and microbiological data.
• Manual SSI surveillance at Martinique University Hospital (MUH) is labor-intensive due to data integration challenges.

Purpose of the Study:

• To implement and evaluate a semi-automated method for detecting suspected SSIs at MUH.
• To adapt the SPICMI surveillance protocol for a semi-automated approach.
• To improve the efficiency and effectiveness of SSI monitoring at MUH.

Main Methods:

• Development and application of a semi-automated algorithm based on the SPICMI protocol.
• Utilizing hospital medico-administrative and microbiological data for case detection.
• Manual review of algorithm-identified suspected SSI cases for confirmation.

Main Results:

• The algorithm identified 85 high and 36 moderate suspicion cases of SSI.
• Manual review confirmed 27 high and 3 moderate SSI cases.
• The semi-automated method demonstrated potential for enhanced SSI detection.

Conclusions:

• The implemented semi-automated SSI detection method significantly improved monitoring efficiency at MUH.
• This approach helps align local surveillance practices with national standards for HAI monitoring.
• Semi-automated tools offer a promising solution for resource-intensive surveillance tasks.