Background: Geriatric syndromes, including frailty, are common in older adults and associated with adverse outcomes. We compared patients described in clinical notes as "frail" to other older adults with respect to geriatric syndrome burden and healthcare utilization.
Methods: We conducted a retrospective cohort study on 18,341 Medicare Advantage enrollees aged 65+ (members of a large nonprofit medical group in Massachusetts), analyzing up to three years of administrative claims and structured and unstructured electronic health record (EHR) data. We determined the presence of ten geriatric syndromes (falls, malnutrition, dementia, severe urinary control issues, absence of fecal control, visual impairment, walking difficulty, pressure ulcers, lack of social support, and weight loss) from claims and EHR data, and the presence of frailty descriptions in clinical notes with a pattern-matching natural language processing (NLP) algorithm.
Results: Of the 18,341 patients, we found that 2202 (12%) were described as "frail" in clinical notes. "Frail" patients were older (82.3 ± 6.8 vs 75.9 ± 5.9, p < .001) and had higher rates of healthcare utilization, including number of inpatient hospitalizations and emergency department visits, than the rest of the population (p < .001). "Frail" patients had on average 4.85 ± 1.72 of the ten geriatric syndromes studied, while non-frail patients had 2.35 ± 1.71 (p = .013). Falls, walking difficulty, malnutrition, weight loss, lack of social support and dementia were more highly correlated with frailty descriptions. The most common geriatric syndrome pattern among "frail" patients was a combination of walking difficulty, lack of social support, falls, and weight loss.
Conclusions: Patients identified as "frail" by providers in clinical notes have higher rates of healthcare utilization and more geriatric syndromes than other patients. Certain geriatric syndromes were more highly correlated with descriptions of frailty than others.
Keywords: Electronic health records; Frailty; Geriatric syndromes; Natural language processing; Unstructured data.