Use of an electronic administrative database to identify older community dwelling adults at high-risk for hospitalization or emergency department visits: The elders risk assessment index
Use of an electronic administrative database to identify older community dwelling adults at high-risk for hospitalization or emergency department visits: The elders risk assessment index
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DOI：
10.1186/1472-6963-10-338
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发表时间：
2010-12-13
影响因子：
2.8
通讯作者：
Takahashi, Paul Y.
中科院分区：
文献类型：
--
作者：
Crane, Sarah J.;Tung, Ericka E.;Takahashi, Paul Y.
背景资料：预防体弱老年人反复住院需要实施高强度干预措施，如病例管理。为了在实践和财务上可持续，这些计划需要一种方法来识别那些最有可能住院的患者，因此最有可能从干预中受益。本研究的目的是证明使用电子病历，以创建一个行政指数，这是能够风险分层这种异质population.Methods：我们进行了一项回顾性队列研究，在一个单一的三级保健设施，罗切斯特，明尼苏达州。患者包括2005年1月1日分配到初级保健内科提供者的所有12，650名60岁及以上的社区居民。在logistic回归模型中，对过去两年的患者风险因素（包括人口统计学特征、共病和住院情况）进行了显著性评估。主要结果是随后两年急诊室就诊和住院的总次数。风险因素根据其回归系数估计值和创建的总风险评分分配评分。该评分的敏感性和特异性进行了评估。结果：最终模型的主要结果的AUC为0.678。风险最高的10%的患者住院或急诊就诊的相对风险为9.5，随后两年住院的相对风险为13.3。结论：有可能创建一种筛查工具，该工具使用在一个医疗机构内容易获得的临床和管理数据来识别处于住院和急诊室入院高风险的老年人群。电子病历。
Background: The prevention of recurrent hospitalizations in the frail elderly requires the implementation of high-intensity interventions such as case management. In order to be practically and financially sustainable, these programs require a method of identifying those patients most at risk for hospitalization, and therefore most likely to benefit from an intervention. The goal of this study is to demonstrate the use of an electronic medical record to create an administrative index which is able to risk-stratify this heterogeneous population.Methods: We conducted a retrospective cohort study at a single tertiary care facility in Rochester, Minnesota. Patients included all 12,650 community-dwelling adults age 60 and older assigned to a primary care internal medicine provider on January 1, 2005. Patient risk factors over the previous two years, including demographic characteristics, comorbid diseases, and hospitalizations, were evaluated for significance in a logistic regression model. The primary outcome was the total number of emergency room visits and hospitalizations in the subsequent two years. Risk factors were assigned a score based on their regression coefficient estimate and a total risk score created. This score was evaluated for sensitivity and specificity.Results: The final model had an AUC of 0.678 for the primary outcome. Patients in the highest 10% of the risk group had a relative risk of 9.5 for either hospitalization or emergency room visits, and a relative risk of 13.3 for hospitalization in the subsequent two year period.Conclusions: It is possible to create a screening tool which identifies an elderly population at high risk for hospital and emergency room admission using clinical and administrative data readily available within an electronic medical record.