SHB: Type II (INT): Collaborative Research: Algorithmic Approaches to Personalized Health Care

美国医疗保健系统将主要资源花在医院环境中的急性疾病治疗上，而不是专注于预防和让患者远离医院。尽管各方没有就潜在的解决方案达成广泛共识，但结构性改革已经迫在眉睫。有意义地使用电子健康记录(EHR)被视为提高效率的关键。包括微型植入式医疗设备、联网家庭监控设备以及智能手机无处不在的使用在内的技术解决方案正开始使人们能够在患者日常生活中对他们进行持续实时监控。来自这些设备的丰富数据形成了电子个人健康记录(PHR)，可以在比EHR更精细的时间尺度上捕获患者的健康。然而，卫生保健系统没有做好充分的准备，无法从即将到来的个性化卫生相关数据的洪流中受益，以便改善卫生结果和降低成本。这项建议提出了一种全面和系统的方法来智能处理这些数据，旨在防止住院，使患者能够积极参与管理他们的健康，评估护理质量，并在新兴的数据丰富的环境中促进具有成本效益的流行病学。在拟议的框架中，早期风险评估从挖掘EHR和PHR数据的算法开始，根据患者患上需要住院和/或产生巨额费用的急性疾病的风险对患者进行分类。我们的方法产生的风险分层引发了一系列行动，包括检查、额外的和更密集的监测以及医生的参与。作为这一动态健康管理进程的一部分，患者可以使用工具，使他们能够积极参与糖尿病等慢性疾病的日常管理。我们的计划包括开发利用专家的算法？评估医疗质量的意见，以及开发适合于新兴环境的分布式流行病学方法，其中每个患者的大量数据分布在许多不同的地点。拟议的工作有可能实现医疗质量的革命性改善。风险评估与慢性疾病的智能管理相结合，可以预防急性健康事件，并显著改善健康结果。拥有严格和可扩展的医疗质量评估方法，有可能减少医疗差错，改善医疗保健提供者之间的协调。在教育方面，计划包括开设新课程，培养多样化的研究生，让本科生参与进来，积极与医生合作，并通过私人投资机构支持的现有计划接触高中生。传播计划包括利用BU传感器网络联盟和组织一次重要的医学信息学研讨会。

The US health care system spends major resources on the treatment of acute conditions in a hospital setting rather than focusing on prevention and keeping patients out of the hospital. While there is no broad agreement on the potential solutions, structural reform is on the horizon. The meaningful use of Electronic Health Records (EHRs) is seen as a key to improving efficiency. Technology solutions including miniaturized implantable medical devices, networked home monitoring devices, and the ubiquitous use of smart phones are starting to enable continuous real-time monitoring of patients as they go about their daily lives. Rich data from these devices form an electronic Personal Health Record (PHR) that captures patient health in a much finer time-scale than the EHR. The health care system, however, is not well equipped to benefit from the impending deluge of personalized health-related data in order to improve health outcomes and reduce costs. This proposal puts forth a comprehensive and systematic approach to intelligently process such data aiming at preventing hospitalization, empowering patients to actively participate in managing their health, assessing quality of care, and facilitating cost-effective epidemiology in the emerging data-rich environment. In the proposed framework, early risk assessment starts with algorithms for mining EHR and PHR data to classify patients in terms of the risk they have for developing an acute condition that would require hospitalization and/or incur large costs. Risk stratification produced by our approach triggers a set of actions, including tests, additional and more intensive monitoring, and physician involvement. As part of this dynamic health management process, patients can have access to tools that enable their active participation in the daily management of chronic conditions, such as diabetes. Our plans include the development of algorithms that leverage experts? opinions to assess quality of care and the development of a distributed epidemiology approach suitable for the emerging landscape where lots of data about each patient are distributed among many different locations.The proposed work has the potential to achieve revolutionary improvements in the quality of health care. Risk assessment combined with intelligent management of chronic conditions can prevent acute health episodes and dramatically improve health outcomes. Having rigorous and scalable ways of assessing the quality of care has the potential to reduce medical errors and improve coordination among health care providers. On the educational front, plans include new courses, training a diverse set of graduate students, involving undergraduate students, actively collaborating with medical doctors, and reaching out to high school students through existing programs embraced by the PIs. Dissemination plans include capitalizing on the BU Sensor Network Consortium and organizing a major medical informatics workshop.