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用于识别患有多种慢性病的住院老年人虚弱状况的新型电子健康记录策略。

Novel Electronic Health Record Strategies to Identify Frailty Among Hospitalized Older Adults with Multiple Chronic Conditions.

作者信息

Loewenthal Julia V, Ernecoff Natalie C, Dalal Anuj K

机构信息

Division of Aging, Brigham and Women's Hospital, Boston, MA, USA.

Harvard Medical School, Boston, MA, USA.

出版信息

J Gen Intern Med. 2025 May;40(6):1275-1279. doi: 10.1007/s11606-024-09227-2. Epub 2024 Dec 4.

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.

摘要

越来越多的老年人患有多种慢性疾病(MCC)。患有MCC的老年人易患虚弱症,这是一种生理储备下降的状态,会增加老年综合征以及相关发病率和死亡率的风险。电子虚弱指数(eFI)通过结构化电子健康记录(EHR)数据被动计算得出,有助于进行前瞻性筛查。不幸的是,某些诊断,如非结构化文档中的功能状态评估,eFI不太可能捕捉到,这可能会低估虚弱程度。在此,我们讨论了使用eFI识别患有MCC的虚弱老年人时当前存在的差距,以及提高eFI准确性的人工智能(AI)方法。准确且常规的虚弱评估可以帮助全科医生在多个护理环境中为患有MCC的老年人提供护理,从而为这些脆弱患者优化生理储备。

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