Infection Control in Home Care and Predictive Risk Modeling

项目摘要 家庭医疗保健（HHC）是美国增长最快的医疗保健行业，发挥着重要 在急性期护理中的作用。2011年，超过470万名患者接受了HHC治疗，其中一半以上的患者住院 就在HHC入院前。感染在家庭护理中很普遍;在以前NIH资助的工作中，我们 发现HHC患者住院的前五个原因中有三个与感染有关。在 事实上，联合委员会将感染控制列为HHC的国家患者安全目标之一。 这表明，在国家努力中， 减少再次住院。实施任何感染控制干预措施之前的关键步骤包括 识别潜在感染高风险的患者，并描述当前感染控制的特征 在HHC的实践。临床决策往往受到缺乏复杂的系统评估的限制。 患者及其环境的需求;特别是在HHC环境中，对工作人员感染知之甚少 控制措施以及如何识别高风险患者。 为了应对这些挑战，本项目的目标是：1）开发和测试预测风险模型 确定HHC环境中感染风险高的患者，以及2）评估当前的感染控制实践， 的HHC设置，并探讨如何预测风险模型可以在HHC设置的临床实践中使用。 本计划将在纽约探访护士服务处（VNSNY）进行，纽约探访护士服务处是美国最大的 对于目标1，将在70%的样本中开发预测风险模型 然后在剩下的30%的样本中进行验证。预测风险模型决策还将 经HHC感染控制专家的临床判断验证。对于目标2，混合方法包括 将采用观察、调查、半结构化访谈和焦点小组等方法， 了解当前的感染控制实践和潜在的障碍。我们的研究团队拥有大量 在护理，决策分析，建模，流行病学，混合方法，传染病，和 感染控制和访问来自哥伦比亚大学医学中心和 因此，VNSNY的居家照护政策与研究中心是进行本研究的理想人选。 研究结果是未来开发有效风险模型的关键第一步， HHC环境中的感染控制干预。

PROJECT SUMMARY Home health care (HHC), the most rapidly growing health care sector in the U.S., plays a significant role in post-acute care. Over 4.7 million patients received HHC in 2011, and over half had an inpatient stay immediately before HHC admission. Infection is prevalent in home care; in previous NIH-funded work, we found that three of the top five reasons for hospitalization among HHC patients were related to infections. In fact, infection control was listed by The Joint Commission as one of its national patient safety goals for HHC. This indicates the importance of focusing on infection control and prevention in HHC in national efforts to reduce re-hospitalization. Critical steps before implementation of any infection control intervention include identifying patients who are potentially at high risk for infections, and characterizing current infection control practices in HHC. Clinical decision making is often limited by a lack of systematic assessment of the complex needs of patients and their environments; specifically in the HHC setting, little is known about staff infection control practices and how high risk patients are identified. To address these challenges, the aims of this project are to 1) develop and test a predictive risk model to identify patients at high risk of infection in HHC setting, and 2) assess current infection control practices in the HHC setting and explore how predictive risk modeling can be utilized in clinical practice in HHC setting. The proposed project will be conducted in the Visiting Nurse Service of New York (VNSNY), the largest not-for-profit HHC agency in the U.S. For Aim 1, a predictive risk model will be developed in 70% of the sample and then validated in the remaining 30% of the sample. The predictive risk model decision will also be validated with HHC infection control specialists’ clinical judgment. For Aim 2, mixed methods including observation, survey, semi-structured interviews and focus groups will be used to provide a comprehensive understanding of current infection control practices and potential barriers. Our research team has substantial expertise in nursing, decision analysis, modeling, epidemiology, mixed methods, infectious disease, and infection control and access to the rich statistical resources from Columbia University Medical Center and the Center for Home Care Policy and Research in the VNSNY, therefore is ideally qualified to conduct this study. The study findings are the critical first step toward future development of an efficient risk model guided infection control intervention in the HHC setting.