Sentiment in Clinical Notes: A Predictor for Length of Stay?

BackgroundLength of stay (LOS) is a critical metric for hospital operational efficiency. While structured clinical data is widely used to predict LOS, unstructured admission notes contain latent prognostic information regarding diagnostic uncertainty and disease complexity. This study evaluates the efficacy of extracting sentiment and direct LOS estimates from admission notes to predict patient hospitalization duration. MethodsWe conducted a retrospective study of 4,503 adult patients admitted with community-acquired pneumonia between 2013 and 2023. Admission history and physical notes were preprocessed and filtered to extract physician-generated narratives. We evaluated four natural language processing models, VADER, TextBlob, Longformer, and an open-source large language model (GPT-oss-20B), to generate zero-shot sentiment scores. Additionally, GPT-oss-20B was prompted to directly estimate LOS. Model outputs were correlated with actual LOS using linear regression and Pearson correlation coefficients. ResultsSentiment models demonstrated statistically significant, albeit weak, correlations with actual LOS. Longformer achieved the highest variance explained among sentiment classifiers (R2 = 0.019). Direct LOS estimation by the LLM outperformed sentiment-based approaches, demonstrating the strongest correlation with actual hospital duration (r = -0.218, p < 0.001). Model agreement was generally poor (ICC = 0.059), and computational time varied drastically, from 2.6 seconds per 100 notes (TextBlob) to over 370 seconds (GPT-oss-20B). ConclusionZero-shot sentiment analysis of clinical notes yields a small but measurable correlation with LOS, limited primarily by the objective, non-evaluative nature of clinical documentation. Direct LLM estimation of clinical outcomes outperforms emotional sentiment extraction. Future predictive systems should integrate computationally efficient NLP models capable of capturing latent clinical complexity alongside established structured data variables.