Mining medical narratives on geriatric falls to predict post-fall hospitalization via survival models and large language models

Timely admission to the emergency department is a crucial determinant of patient outcomes. Conversely, unnecessary hospital admissions can overburden health systems and induce anxiety and stress among patients, their families, and caregivers. This study examines these implications in the geriatric population by investigating the hypothesis that delay time, i.e. the interval between injury and hospital admission, is associated with patient outcomes post admission. As delay times are not typically captured in electronic medical records, we leverage a large database from an open challenge where short narratives describing the patient injuries and treatment were made publicly available. Accordingly, we developed prognostic survival models based on large-language models that predict time to an adverse outcome using features extracted from the textual narratives, as well as additional data provided by the challenge, e.g. data about patients’ baseline characteristics, conditions of their injuries. To this end, we found that models incorporating textual embeddings achieved a dynamic area under the curve (AUC) of 0.713-0.715, compared to 0.637–0.649 for models without textual features, when evaluated on an external cohort. This study provides preliminary evidence that the textual data collected at the time of patients triage can be useful for patient prognosis. Future studies will examine how the same data, collected right after fall events could be used to project patient’s recovery progress.