Novel Electronic Health Record Strategies to Identify Frailty Among Hospitalized Older Adults with Multiple Chronic Conditions.
Abstract
A growing number of aging adults are living with multiple chronic conditions (MCC). Older adults living with MCC are predisposed to developing frailty, a state of decreased physiologic reserve that increases risk for geriatric syndromes and associated morbidity and mortality. The electronic frailty index (eFI) is computed passively using structured EHR data and can aid in prospective screening. Unfortunately, certain diagnoses, such as functional status assessments in unstructured documentation, are less likely captured by eFI, potentially underestimating the degree of frailty. Here, we discuss current gaps for using eFI to identify frail older adults living with MCC, and artificial intelligence (AI) approaches to enhance eFI accuracy. Accurate and routine frailty assessment can aid the generalist providing care to older adults living with MCC across multiple care settings to optimize physiologic reserve for these vulnerable patients.
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Research summary
The paper addresses the identification of frailty in older adults with multiple chronic conditions, which is relevant to aging research as it seeks to improve the assessment of a condition that significantly impacts the health and longevity of older populations. However, the focus on enhancing electronic health record strategies and AI approaches for frailty assessment represents a solid but limited contribution to the field, as it primarily deals with improving diagnostic methods rather than addressing the root causes of aging or frailty itself.
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