Millions of Americans undergo surgery each year. After surgery, preventing complications like pneumonia, blood clots and infections can be the difference between a successful recovery and a prolonged, painful hospital stay – or worse. More than 10% of surgical patients experience such complications, which can lead to longer stays in the intensive care unit (ICU), higher mortality rates and increased health care costs. Early identification of at-risk patients is crucial, but predicting these risks accurately remains a challenge.
New advancements in artificial intelligence (AI), particularly large language models (LLMs), now offer a promising solution. A recent study led by Chenyang Lu , the Fullgraf Professor in computer science & engineering in the McKelvey School of Engineering and director of the AI for Health Institute (AIHealth) at Washington University in St. Louis, explores the potential of LLMs to predict postoperative complications by analyzing preoperative assessments and clinical notes. The work, published online Feb. 11 in npj Digital Medicine , shows that specialized LLMs can significantly outperform traditional machine learning methods in forecasting postoperative risks.
"Surgery carries significant risks and costs, yet clinical notes hold a wealth of valuable insights from the surgical team," Lu said. "Our large language model, tailored specifically for surgical notes, enables early and accurate prediction of postoperative complications. By identifying risks proactively, clinicians can intervene sooner, improving patient safety and outcomes."
Traditional risk prediction models have primarily relied on structured data, such as lab test results, patient demographics, and surgical details like procedure duration or the surgeon's experience. While this information is undoubtedly valuable, it often lacks the nuance of a patient's unique clinical narrative, which is captured in the detailed text of clinical notes. These notes contain personalized accounts of the patient's medical history, current condition, and other factors that influence the likelihood of complications.
