ArticleAnnals of thoracic surgery short reports2026
A Cardiac Surgery-Specific Artificial Intelligence Model to Automate Risk Stratification for 30-day Readmissions Using Discharge Summaries.
Article in Annals of thoracic surgery short reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Background: Readmission risk can be assessed with risk calculators but typically requires manual chart review. We aimed to develop an artificial intelligence (AI) large language model (LLM) to predict 30-day readmission risk by directly analyzing electronic discharge summaries of cardiac surgery patients. Methods: We developed a cardiac surgery-specific LLM by fine-tuning a general medical LLM to predict 30-day all-cause readmissions directly from unaltered text in discharge summaries of 5244 adult cardiac surgery patients (2014-2021). Strict temporal validation was implemented in a test cohort (2021-2024; n = 3031). Model predictions were compared with readmission length of stay. Results: The model achieved an area under the curve of 0.71, with sensitivities and specificities of 78.6% and 50.2%, respectively. Performance showed temporal degradation from 2022 to 2024 (area under the curve, 0.78 to 0.67), mitigated through recalibration. Patients in the highest vs lowest predicted risk score quintile had a greater mean readmission length of stay (7.1 vs 3.7 days; Conclusions: A cardiac surgery-specific LLM showed high sensitivity, moderate specificity, and modest ability to discriminate 30-day readmission risk. Temporal trends in documentation influenced model performance, underscoring the need for dynamic recalibration to update AI models. This study suggests that AI-automated screening to assess readmission risk at the time of discharge is possible and may help inform readmission prevention strategies.
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