PC3: Collaborative Research: Foundation for Trusted and Scalable Mobile Healthcare

该奖项为达特茅斯学院、莱斯大学和新德里印度理工学院之间的一个合作项目提供资金。不久，移动健康技术(MHealth)将使个人能够佩戴一个或多个传感设备，以更好地监测他们的健康状况，或者使农村卫生工作者能够定期访问可能无法获得常规医疗服务的村庄。该项目的研究人员正在为mHealth组件的模块化套件开发科学基础--便携、廉价，患者或受过有限培训的医护人员可以使用--可以组装成各种组合，用于不同的环境或医疗保健目的。从科学上讲，他们正在解决两个基本问题：(1)如何构建安全、自我感知的传感器，以证明传感器数据的来源及其背景；(2)如何设计一种计算分诊系统，可以向患者提供实时现场反馈，从而避免每个患者就诊、每个数据点都需要由熟练的卫生专业人员进行检查。该项目的智力价值在于(1)开发了一种新型的便携式医疗传感器，具有识别和安全地证明数据来源和质量的智能，以及(2)开发了计算分诊算法，当测试显示潜在健康问题的可能性很高时，该算法可以引导个体受试者前往医疗机构。这项研究应该产生更广泛的影响，包括(1)可以从根本上改善预防健康的技术，(2)在更广泛的全球背景下对学生进行医疗保健技术培训，以及(3)将具有医疗保健以外的应用的技术，例如在关键基础设施监测方面。该项目是普适通信和计算协作(PC3)计划的一部分。

This award provides funding for a collaborative project between Dartmouth College, Rice University and the Indian Institute of Technology in New Delhi. Soon, technology for mobile health (mHealth) will enable individuals to wear one or more sensing devices to better monitor their health conditions, or enable rural health workers to make periodic visits to villages that may not have access to routine healthcare services. The investigators in this project are developing the scientific foundations for a modular kit of mHealth components -- portable, inexpensive, and usable by patients or healthcare workers with limited training -- that can be assembled into a variety of combinations for different circumstances or healthcare purposes. Scientifically, they are addressing two fundamental questions: (1) how to construct secure, self-aware sensors that can attest to the provenance of the sensor data and its context; (2) how to design a system for computational triage that can provide real-time on-site feedback to the patient, avoiding the need for every patient visit, every data point, to be examined by skilled health professionals. The intellectual merit of this project is in (1) developing a new breed of portable medical sensors with the intelligence to identify and securely attest to the origin and quality of the data, and (2) developing computational triage algorithms that guide individual subjects to a medical facility when tests reveal a high chance of potential health problems. The research should result in broader impacts including (1) technology that could radically improve preventive health, (2) students trained on healthcare technologies in a wider global context, and (3) technologies that will have applications beyond healthcare, such as in critical infrastructure monitoring. This project is part of the Pervasive Communications and Computing Collaboration (PC3) initiative.