Surgical Site Infection (SSI) Identification Across Multiple Facilities and Surgery Types Using Multimodal Data and Deep Learning

Abstract

Surgical site infections (SSI), infections at the surgical site that occur after surgery, impact more than a hundred thousand patients a year in the United States. They increase the risk of death after surgery, lead to complications like cellulitis and sepsis, and incur significant healthcare costs. Surveillance of SSI can guide interventions to reduce SSI rates. The current mainstay of SSI surveillance is manual chart review, which is expensive and time consuming. Automated surveillance systems addressing these drawbacks typically rely on a limited number of data modalities from the electronic health record (EHR). They predominantly use rule-based approaches or conventional machine learning algorithms to retrospectively predict whether a surgical case resulted in an SSI. This limits the performance and domain adaptation capability of published gold standard automated surveillance systems. In contrast to previous state-of-the-art automated surveillance approaches, we employed a data-driven deep learning framework that integrated structured data, clinical text data, and temporal information from the EHRs of surgical cases to develop an automated surveillance system. Our primary findings demonstrated several key points: a purely data-driven deep learning approach using multimodal data outperformed previously published gold standard rule-based and conventional machine learning approaches for the task of surgical site infection (SSI) prediction; the data representation and modeling strategies we utilized enabled the construction of models capable of domain adaptation across a diverse set of domains; and large language models (LLMs), specifically generalist foundation models such as Llama 3, offered previously unrealized performance gains.