A multi-site benchmarking framework for scalable extraction of geriatric care constructs from electronic health records
TL;DR
Findings highlight key trade-offs among symbolic, generative, and instruction-tuned approaches for CGA and 4Ms phenotyping, suggesting that hybrid heuristic-LLM methods offer interpretability and stability, whereas large language models provide greater adaptability for complex clinical narratives.
Abstract
Current Natural Language Processing (NLP) algorithms for detecting geriatric conditions are largely limited to domain-specific models that fail to capture the interdependent, multidimensional nature of comprehensive geriatric assessment. This study aimed to develop and evaluate a comprehensive, scalable, and robust information extraction framework to identify Comprehensive Geriatric Assessment (CGA) and Age-Friendly Health Systems (AFHS) 4Ms-related data elements from unstructured electronic health record (EHR) text across multiple health systems. Using a team science approach grounded in the TRUST framework, we annotated pooled clinical notes from four health systems to produce a gold-standard dataset of 41 CGA- and 4Ms-related geriatric care data elements. Three information extraction approaches were implemented and evaluated: an in-context learning generative large language model (GPT-4o), a hybrid heuristic-LLM model (MedAgingIE), and an instruction-tuned open-source lightweight model (Qwen2-7B-Instruct). Performance was assessed on a blinded test set using macro- and micro-averaged metrics. GPT-4o achieved a macro F1-score of 0.56 and micro F1-score of 0.87; MedAgingIE achieved 0.55 and 0.92; and Qwen2-7B-Instruct achieved 0.30 and 0.81, respectively. MedAgingIE demonstrated the strongest consistency between precision and recall, while GPT-4o showed superior sensitivity for diverse, context-rich geriatric concepts. These findings highlight key trade-offs among symbolic, generative, and instruction-tuned approaches for CGA and 4Ms phenotyping, suggesting that hybrid heuristic-LLM methods offer interpretability and stability, whereas large language models provide greater adaptability for complex clinical narratives.
