A Comparative Study of Structured and Narrative EHR Data for 30-Day Readmission Risk Assessment

Hospital readmissions within 30 days of discharge are a key metric of healthcare quality and a major driver of cost. Accurate risk stratification enables targeted home care interventions, such as remote monitoring, timely nurse follow-up, and medication adherence programs, designed to mitigate preventable readmissions. Thus, we conducted a systematic comparison of structured electronic health record (EHR) data and unstructured discharge summaries for predicting 30-day unplanned readmissions. Using the MIMIC-IV database, we integrated admissions, emergency department records, laboratory values, and discharge notes, and we restricted the cohort to ED-based readmissions. Following rigorous preprocessing, we balanced the dataset and split it into training, validation, and test sets at the patient level. Structured features, including the LACE score (length of stay, acuity of admission, comorbidities, and ED utilization) and triage vitals, were modeled with logistic regression, random forest, XGBoost, and LightGBM. Discharge notes were analyzed with ClinicalLongformer to generate contextual embeddings and with Qwen2.5-7B-Instruct for few-shot classification. ClinicalLongformer achieved the best discrimination (AUROC = 0.72, F1 = 0.68), outperforming classical ML baselines (AUROC ≈ 0.65–0.67, F1 ≈ 0.64–0.65). The LLM (Qwen2.5-7B-Instruct) yielded moderate discrimination (AUROC = 0.66, F1 = 0.65) while providing interpretable chain-of-thought rationales. These findings highlight the value of narrative data for risk stratification and suggest that transformer-based language models have the potential to enable scalable, explainable early-warning systems to guide home care and prevent avoidable readmissions.