Automatic detection of surgical site infections from a clinical data warehouse

TL;DR

This paper presents an automatic machine learning-based method to detect surgical site infections from hospital data, aiming to improve surveillance efficiency and accuracy compared to manual methods.

Contribution

It introduces two approaches for SSI detection using hospital data, demonstrating promising results and paving the way for semi-automated infection surveillance.

Findings

Achieved detection of all SSIs with 20-26 false positives

Compared multi-source and text-based approaches

Results support semi-automated infection monitoring

Abstract

Reducing the incidence of surgical site infections (SSIs) is one of the objectives of the French nosocomial infection control program. Manual monitoring of SSIs is carried out each year by the hospital hygiene team and surgeons at the University Hospital of Bordeaux. Our goal was to develop an automatic detection algorithm based on hospital information system data. Three years (2015, 2016 and 2017) of manual spine surgery monitoring have been used as a gold standard to extract features and train machine learning algorithms. The dataset contained 22 SSIs out of 2133 spine surgeries. Two different approaches were compared. The first used several data sources and achieved the best performance but is difficult to generalize to other institutions. The second was based on free text only with semiautomatic extraction of discriminant terms. The algorithms managed to identify all the SSIs with 20 and 26 false positives respectively on the dataset. Another evaluation is underway. These results are encouraging for the development of semi-automated surveillance methods.