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

CISE-MSI：RCBP-RF：SCH：挖掘移动群智以优化社区健康诊所管理

• 批准号: 2131100
• 负责人: Muztaba Fuad
• 金额: $ 29.97万
• 依托单位: Winston-Salem State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131100&HistoricalAwards=false
• 关键词: CISE; MSI; RCBP; RF; SCH

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.

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。造成美国医疗保健系统浪费的原因有很多；这种浪费的一部分与效率低下有关。在无偿医疗诊所，需求往往超过供应，资源往往更有限，实习医生往往在培训中提供服务，效率低下往往影响所提供服务的质量和数量。在公益诊所，没有直接的财政动力来提高效率。然而，通过优化医疗服务，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，更好地解决健康差距问题。对效率的关注集中在优化富有成效的患者护理时间，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，例如在整个诊所内移动、获取设备和设备、与其他医疗保健提供者通信等。监测效率还允许监督物理治疗师在学生学习过程中确定何时需要学生的临床表现支持和纠正。为了解决医疗保健中的低效率和提高护理及时性，已经提出了多种解决方案，其中之一是优化诊所布局。然而，缺乏对如何优化流动性的理解，特别是在无偿诊所，以提高效率/生产力，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。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。移动众测是一项功能强大但价格实惠的技术，用于普遍感知有价值的数据，为各种现实世界的问题提供解决方案。从社区诊所的角度来看，可以利用从业人员的机会性人群感知数据，使从业人员更有效地使用诊所的设施，提高从业人员的能力。这些移动传感应用程序可以感知的数据包括医生在诊所内的运动及其上下文信息，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境化的位置和移动数据，可以利用适当的大数据分析和可视化来提取智能，并通过解决几个紧迫的需求来改善以人为本的服务交付实例。这些需求可能包括合并不同的诊所布局以改善患者体验，寻找最有效的方式利用资源以改善患者接触时间，使执业医师在提供患者护理方面富有成效，以及改善他们的学习体验。该项目设想在室内空间中结合静止和静止的移动设备，通过使用基于蓝牙接近度的方法来检测位置和运动，这种方法用于covid -19接触者追踪。本项目旨在设计和开发一个具有可视化和分析功能的移动众感应用程序，以帮助社区诊所优化从业者的移动和操作空间，提高其效率。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Sensing the walking velocity of a person by using mobile devices

使用移动设备感测人的行走速度

• 发表时间: 2022
• 期刊: The Twenty-Sixth ACM Annual Consortium for Computing Sciences in Colleges Northeastern Conference
• 作者: Anthony Smith, Muztaba Fuad

Using RSSI to Form Path in an Indoor Space

使用 RSSI 在室内空间形成路径

• DOI: 10.1109/icccn54977.2022.9868912
• 发表时间: 2022
• 期刊: 2022 International Conference on Computer Communications and Networks (ICCCN
• 作者: Fuad, Muztaba;Deb, Debzani;Panlaqui, Brixx-John G.;Mickle, Charles F.
• 通讯作者: Mickle, Charles F.