Using automated speech processing to improve identification of risk for hospitalizations and emergency department visits in home healthcare

项目摘要 每年，美国约有11，000家家庭医疗保健（HHC）机构为更多的人提供护理。 超过500万老年人。目前，大约三分之一的HHC患者住院或急诊 这些事件中有高达40%是可以通过适当和及时的护理预防的。但是，在这方面， 尽管国家和地方努力提高质量，但这些数字在过去十年中没有改善。 数据科学的一个子领域-自动语音处理-的最新进展已经解锁了一个 未开发的丰富数据流，可以通过分析护士-患者口头 通信这项拟议中的研究汇集了一个跨学科的家庭保健专家团队 护理，自动语音处理，自然语言处理和风险模型开发，以探索 自动语音处理是否可以改善家庭医疗中风险患者的及时识别 并可能减少他们的住院和艾德就诊。 具体来说，本研究的目的是：目标1：完善和完成自动语音处理系统 确定住院和艾德访视的风险因素在病人-护士的口头沟通。目标2：探索 从患者-护士沟通中提取的数据在多大程度上可以改善住院风险预测 和艾德就诊，与仅基于电子健康记录数据的风险模型进行比较。 本研究将建立一个首次住院和艾德就诊风险模型， 数据来自患者-护士口头交流。在今后的工作中，可以将这种风险模型融入到家庭 医疗保健临床工作流程，以触发有关患者趋势的及时和个性化警报， 反过来，将启动适当和及时的护理，以防止可避免的住院治疗和HHC的艾德访视。

PROJECT SUMMARY Every year, about 11,000 home healthcare (HHC) agencies across the United States provide care to more than 5 million older adults. Currently, about one in three HHC patients are hospitalized or visit an emergency department (ED) and up to 40% of these events are preventable with appropriate and timely care. However, these numbers have not improved over the last decade, despite national and local quality improvement efforts. Recent advances in a subfield of data science—automated speech processing—have unlocked an untapped rich data stream that can improve risk identification by analyzing nurse-patient verbal communication. The proposed study brings together an interdisciplinary team of experts in home healthcare nursing, automated speech processing, natural language processing, and risk model development to explore whether automated speech processing can improve timely identification of patients at risk in home healthcare and potentially reduce their hospitalizations and ED visits. Specifically, the aims of this study are: Aim 1: Refine and finalize an automated speech processing system to identify hospitalization and ED visit risk factors in patient-nurse verbal communications. Aim 2: Explore to what extent data extracted from patient-nurse communications can improve risk prediction for hospitalizations and ED visits, when compared against the risk model based on electronic health record data only. This study will build a first-of-a-kind hospitalization and ED visit risk model that automatically incorporates data from patient-nurse verbal communication. In future work, this risk model can be integrated into home healthcare clinical workflows to trigger timely and personalized alerts about concerning patient trends, which will in turn activate appropriate and timely care to prevent avoidable hospitalizations and ED visits from HHC.