Homecare-CONCERN: Building risk models for preventable hospitalizations and emergency department visits in homecare

项目摘要/摘要 每年，全美有11000多家家庭护理机构为500多万人提供护理 上了年纪的人。目前，约有三分之一的家庭护理患者住院或就诊。 部门(ED)在30-60天的家庭护理插曲期间。这些事件中高达40%是可以预防的 适当和及时的护理。在我们的试点工作中，我们开发了一个风险预测模型(称为Homecare- 关注)准确地识别了仅通过家庭护理就有可能入院和急诊室就诊的患者 使用NLP的临床记录。这项研究汇集了家庭护理、数据领域的跨学科专家团队 科学、护理和风险模型开发，以探索尖端数据科学方法是否可以 提高对居家护理中有风险的患者的及时识别。我们的具体目标是： 1.进一步开发和验证可预防住院或急诊就诊风险预测模型(Homecare- 关注)。我们将应用传统的(时变考克斯回归)和前沿的时间敏感型 风险模型的分析方法(深度生存分析和长短期记忆神经网络) 发展。 2.通过中试准备Homecare-Concern进行临床试验。我们将把以用户为中心的设计应用到 开发与家庭护理相关的临床决策支持工具，并初步测试该工具的临床有效性和 可接受性。 3.告知未来在家庭医疗中实施家庭医疗相关临床决策支持工具 布景。我们将检查是否可以将所有风险元素映射到数据标准(Fast Healthcare 互操作性资源-FHIR)，并对美国各地的主要信息提供人进行采访，了解当前 在家庭护理中采用此类工具的准备情况、障碍和促进者以及实施战略 布景。 该提案涉及AHRQ计划公告(PA-18-795)，以利用数据来改进 医疗质量和患者结果。这项研究将建立首个临床决策支持 系统触发有关患者趋势的及时和个性化警报，激活适当和 及时护理，防止可避免的住院和家庭护理急诊室就诊。

Project Summary/Abstract Every year, more than 11,000 homecare agencies across the United States provide care to more than 5 million older adults. Currently, about one in three homecare patients are hospitalized or visit an emergency department (ED) during the 30-60 day homecare episode. Up to 40% of these events are preventable with appropriate and timely care. In our pilot work, we developed a risk prediction model (called Homecare- CONCERN) that accurately identified patients at risk for hospital admission and ED visits solely from homecare clinical notes using NLP. This study brings together an interdisciplinary team of experts in homecare, data science, nursing and risk model development to explore whether cutting-edge data science approaches can improve timely identification of patients at risk in homecare. Our specific aims are to: 1. Further develop and validate a preventable hospitalization or ED visit risk prediction model (Homecare- CONCERN). We will apply traditional (time varying Cox regression) and cutting-edge time-sensitive analytical methods (Deep Survival Analysis and Long-Short Term Memory Neural Network) for risk model development. 2. Prepare Homecare-CONCERN for clinical trial via pilot testing. We will apply user centered design to develop Homecare-CONCERN clinical decision support tool and pilot test the tool for clinical validity and acceptability. 3. Inform the future implementation of Homecare-CONCERN clinical decision support tool in the homecare setting. We will examine if all risk elements can be mapped to a data standard (Fast Healthcare Interoperability Resources - FHIR) and conduct interviews with key informants across the US about current readiness, barriers and facilitators, and implementation strategies for adopting such tools in homecare setting. This proposal addresses the AHRQ program announcement (PA-18-795) to harness data to improve healthcare quality and patient outcomes. The study will build a first-of-a-kind clinical decision support system to trigger timely and personalized alerts about concerning patient trends that activate appropriate and timely care to prevent avoidable hospitalizations and ED visits from homecare.