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

点击翻译按钮获取中文摘要

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.