CISE-MSI: RCBP-RF: SCH: Mining Mobile Crowdsensing to Optimize Community Health Clinic Management

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。在美国医疗体系中，造成浪费的原因很多，其中一部分与效率低下有关。在公益医疗诊所，往往供不应求，资源往往更加有限，学生临床医生经常在培训中提供服务，效率低下往往会影响提供的医疗服务的质量和数量。在公益诊所，没有直接的财政驱动来提高效率。然而，通过优化护理服务来提高效率，这些诊所可以为社区中更多有需要的人提供服务，优化服务，并更好地解决健康差距。对效率的关注集中于优化高效的患者护理时间，同时最大限度地减少花费在非生产性、非计费任务上的时间，例如在诊所中移动、获取设备和设备、与其他医疗保健提供者沟通等。监控效率还允许监督物理治疗师在学生学习时确定何时需要学生支持和纠正临床表现。已经提出了多种解决方案来解决医疗保健中的低效率和提高护理的及时性，其中之一是诊所布局优化。然而，缺乏对如何优化移动性的了解，特别是在公益诊所，以提高效率/生产力，以最大限度地延长与患者的接触时间，改善患者和提供者的体验，并加强学生的学习。其目标是在优化时间和资源的同时，提供满足患者和社区需求的高质量护理。移动众感是一种强大但负担得起的技术，用于普及感知有价值的数据，为各种现实世界问题提供解决方案。从社区诊所的角度来看，可以利用从业者的机会主义众感数据，使从业者能够更有效地使用诊所的设施，并提高从业者的能力。这类移动传感应用可以感知的数据包括医生在诊所内的移动及其上下文信息，如位置、身体位置、设备分析和运动模式。通过在诊所内结合这种情景位置和移动数据，可以利用适当的大数据分析和可视化来提取情报，并通过解决几个紧迫需求来改进以人为中心的服务交付。这些需求可能包括整合不同的诊所布局以改善患者体验、找到最有效的方式来利用资源来改善患者接触时间、使从业者在提供患者护理方面更有效率，以及改善他们的学习体验。该项目设想在室内空间内结合静止和固定的移动设备，通过使用基于蓝牙接近的方法来检测位置和运动，就像在COVD-19接触者追踪中所使用的那样。该项目旨在设计和开发一个具有可视化和分析功能的移动人群感知应用程序，以帮助社区诊所优化从业者的活动和操作空间，以提高其效率。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).There are numerous causes of waste in the US healthcare system; a portion of this waste is associated with inefficiency. In pro bono healthcare clinics, where demand often exceeds supply, resources are often more limited, and student clinicians often provide services in training, inefficiency often impacts both the quality and quantity of care provided. In pro bono clinics, there is no direct financial drive for efficiency. However, by focusing on increased efficiency through optimizing care delivery, such clinics can serve more people of need in the community, optimize services, and better address health disparities. The focus on efficiency is centered on optimizing productive patient care time while minimizing the time spent on unproductive, non-billable tasks such as movement throughout the clinic, obtaining equipment and devices, communicating with other healthcare providers, etc. Monitoring efficiency also allows supervising physical therapists to identify when student supports and corrections in clinical performance are needed as students learn. There have been multiple proposed solutions to address inefficiency and improve the timeliness of care in healthcare, one of which is clinic layout optimization. However, there is a lack of understanding of how mobility is optimized, particularly in the pro bono clinics, to increase efficiency/productivity to maximize contact time with patients, improve the patient and provider experience, and enhance student learning. The goal is to render high-quality care that meets patients' and communities' needs while optimizing time and resources.Mobile crowdsensing is a powerful but affordable technology for the pervasive sensing of valuable data that provides solutions to various real-world problems. From a community clinic's perspective, opportunistic crowdsensing data from the practitioners can be leveraged to allow practitioners to use the clinic's facilities more efficiently and enhance the practitioner's capability. Data that such mobile sensing apps can sense include the practitioner's movement within the clinic and their contextual information, such as location, body position, device analytics, and locomotion mode. By combining such contextualized location and movement data within the clinic, appropriate big-data analytics and visualization can be utilized to extract intelligence and improve this instance of human-centric service delivery by addressing several pressing needs. These needs may include incorporating different clinic layouts to improve patient experience, finding the most efficient way to utilize resources to improve patient contact time, making the practitioner productive in providing patient care, and improving their learning experience. This project envisions combining motionless and stationary mobile devices within an indoor space to detect location and motion by using Bluetooth proximity-based approach as utilized in COVD-19 contact tracing. This project aims to design and develop a mobile crowdsensing application with visualization and analytics to help the community clinic optimize practitioners' movement and operating space to increase its efficiency.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.