Mobility functional status ascertainment in electronic health records using large language models.

With global aging, assessing functional status is vital for precision medicine. Electronic Health Records (EHRs), particularly unstructured data, hold abundant information on patient mobility. This study explores using Large Language Models (LLMs) to extract and standardize mobility status from unstructured EHR data (i.e., clinical notes). We annotated 600 clinical notes from three healthcare institutions located in southeastern Minnesota and west-central Wisconsin, focusing on expressions of mobility and associated impairment. Leveraging the open-source Llama 3 model, we tested various prompting strategies, including zero-shot, few-shot, and task decomposition, and evaluated their performance. Error analysis showed that while the model sometimes inferred impairments without explicit evidence, most errors were clinically reasonable, often reflecting borderline or ambiguous cases. Our final model achieved a patient-level micro-average F1-score of 0.876 [95% CI 0.858-0.894] for Mobility Extraction and 0.897 [95% CI 0.878-0.917] for Impairment Classification. A secondary analysis counting "clinically reasonable inferences" as correct, performed to assess clinical plausibility, yielded F1-scores of 0.962 [95% CI 0.952-0.971] and 0.948 [95% CI 0.936-0.960], respectively. A local, deterministic setup improved trustworthiness by ensuring consistent outputs, safeguarding privacy, and demonstrating cross-institution generalizability. These findings highlight the feasibility of LLM-based solutions for extracting mobility functional status from unstructured EHR data, supporting both clinical applications and research.