VA Palo Alto Health Care System, Menlo Park, CA, USA.
Stanford University School of Medicine, Stanford, CA, USA.
Med Care Res Rev. 2022 Oct;79(5):676-686. doi: 10.1177/10775587211062403. Epub 2021 Dec 14.
This article examines the relative merit of augmenting an electronic health record (EHR)-derived predictive model of institutional long-term care (LTC) use with patient-reported measures not commonly found in EHRs. We used survey and administrative data from 3,478 high-risk Veterans aged ≥65 in the U.S. Department of Veterans Affairs, comparing a model based on a Veterans Health Administration (VA) geriatrics dashboard, a model with additional EHR-derived variables, and a model that added survey-based measures (i.e., activities of daily living [ADL] limitations, social support, and finances). Model performance was assessed via Akaike information criteria, C-statistics, sensitivity, and specificity. Age, a dementia diagnosis, Nosos risk score, social support, and ADL limitations were consistent predictors of institutional LTC use. Survey-based variables significantly improved model performance. Although demographic and clinical characteristics found in many EHRs are predictive of institutional LTC, patient-reported function and partnership status improve identification of patients who may benefit from home- and community-based services.
本文探讨了在电子健康记录 (EHR) 中常见的预测模型中增加患者报告的测量指标，以提高机构长期护理 (LTC) 使用预测模型的相对优势。我们使用了美国退伍军人事务部 3478 名≥65 岁高风险退伍军人的调查和管理数据，比较了基于退伍军人健康管理局 (VA) 老年医学仪表板的模型、具有额外 EHR 衍生变量的模型和添加基于调查的测量指标的模型（即日常生活活动 [ADL] 受限、社会支持和财务状况）。通过赤池信息量准则、C 统计量、敏感性和特异性评估模型性能。年龄、痴呆诊断、NOSOS 风险评分、社会支持和 ADL 限制是机构 LTC 使用的一致预测因素。基于调查的变量显著提高了模型性能。尽管许多 EHR 中发现的人口统计学和临床特征可以预测机构 LTC，但患者报告的功能和伙伴关系状况可提高识别可能受益于家庭和社区服务的患者的能力。
