Enhancing LLMs with Smart Preprocessing for EHR Analysis

Large Language Models (LLMs) have demonstrated remarkable proficiency in natural language processing; however, their application in sensitive domains such as healthcare, especially in processing Electronic Health Records (EHRs), is constrained by limited computational resources and privacy concerns. This paper introduces a compact LLM framework optimized for local deployment in environments with stringent privacy requirements and restricted access to high-performance GPUs. Our approach leverages simple yet powerful preprocessing techniques, including regular expressions (regex) and Retrieval-Augmented Generation (RAG), to extract and highlight critical information from clinical notes. By pre-filtering long, unstructured text, we enhance the performance of smaller LLMs on EHR-related tasks. Our framework is evaluated using zero-shot and few-shot learning paradigms on both private and publicly available datasets (MIMIC-IV), with additional comparisons against fine-tuned LLMs on MIMIC-IV. Experimental results demonstrate that our preprocessing strategy significantly supercharges the performance of smaller LLMs, making them well-suited for privacy-sensitive and resource-constrained applications. This study offers valuable insights into optimizing LLM performance for local, secure, and efficient healthcare applications. It provides practical guidance for real-world deployment for LLMs while tackling challenges related to privacy, computational feasibility, and clinical applicability.