Predicting the causes of unplanned reoperations using an AI-based multimodal system: a multicentre, multidepartmental...

Unplanned reoperations are widely recognised as key indicators of surgical quality and patient safety. Analysing the underlying causes of such reoperations is critical for guiding clinical decision-making and optimising patient management. Most existing studies relied on electronic medical records (EMRs) for simple retrospective analyses and focused exclusively on single-disease cohorts, limiting their generalisability. Tools capable of predicting the causes of unplanned reoperations using multimodal EMRs across diverse clinical settings remain scarce. This study aimed to develop an artificial intelligence (AI)-based multimodal system integrating structured and unstructured EMR data across institutions.

We developed the Multi-modal Prediction System for Causes of Unplanned Reoperation (MPSUR), an AI-based framework trained on a retrospective cohort of 2922 cases collected from 15 departments across 8 hospitals (2015-2024). The system integrates structured series data (such as age and sex) and clinical text (such as diagnoses and procedures) using the Graph Convolutional Network and Time and Frequency Recurrent Neural Network. Modality-specific and fused predictions were generated using linear classifiers.

In the clinical reader study, the MPSUR outperformed surgeons across all departments (mean accuracy=60.27%, 95% CI 58.27% to 62.26%). Feature ablation showed that variables such as procedure and department were the most predictive, with their removal significantly reducing performance (-3.05%, p<0.01) for the internal dataset and (-3.42%, p<0.005) for the external dataset. MPSUR achieved 62.03% and 56.41% accuracy on internal and external datasets, respectively, outperforming classical baselines by up to 8%. The SD across cross-validation folds was minimal, underscoring model stability. It also remained robust when only text or series data were available.

The MPSUR provides an automatic, accurate, interpretable and generalisable tool for predicting the causes of unplanned reoperations using multimodal EMRs. Its strong performance across internal and external datasets, as well as its superiority over clinicians, supports its clinical utility as a decision-support system to enhance surgical safety and patient outcomes.