AI system significantly improves surgical case length predictions

An artificial intelligence system was developed for predicting surgical case lengths, significantly outperforming current electronic health record estimates. Analyzed data from 125,493 inpatient elective surgical cases revealed the categoricalboost regressor using clinical text embeddings achieved a mean absolute error of 46.4 minutes, compared to 120.0 minutes for existing records (p < 0.001). The AI model accurately estimated case lengths within ±20% in 48% of cases, drastically enhancing prediction accuracy and operational efficiency.