Improving postoperative length of stay forecasting with retrieval-augmented prediction.

Abstract

Objective: The objective of this study is to evaluate retrieval-augmented prediction for forecasting hospital length of stay (LOS) following surgery compared to traditional machine learning (ML), standalone large language models (LLMs), and retrieval-augmented generation (RAG) approaches.

Materials and methods: Spine surgery cases were extracted from electronic health records. Structured features and operative notes were concatenated into natural language patient representations, embedded using Sentence-Bidirectional Encoder Representations from Transformer, and stored in a vector database. Eight predictive models were implemented, including a baseline model, standalone ML with embeddings, standalone LLM (Gemma 3:27B), and combinations of these with retrieval-augmented prediction or generation. The retrieval-augmented prediction model computed a similarity-weighted average LOS from nearest neighbors. Performance was assessed using R2, mean absolute value (MAE), and root mean square error (RMSE).

Results: Retrieval-augmented prediction alone outperformed standalone ML and LLM models (R2 = 0.39, MAE = 4.47). Combining ML or LLM outputs with retrieval-augmented prediction further improved performance. The best performing model was a neural network blended with retrieval-augmented prediction (R2 = 0.52, MAE = 4.16). LLM-RAG alone reached R2 = 0.19, which improved to 0.47 when combined with retrieval-augmented predictions. Retrieval-augmented prediction consistently reduced MAE and RMSE by up to 32% and 38%, respectively.

Discussion: Retrieval-augmented prediction offers interpretable and resource-efficient forecasting by semantically leveraging prior patient cases without generative modeling. It consistently outperformed RAG and ML across metrics, approximating clinical reasoning via similarity-based inference.

Conclusion: Retrieval-augmented prediction significantly enhances LOS prediction accuracy over standard ML and LLM models. Its interpretability and scalability make it a promising solution for integrating predictive analytics into clinical workflows.