STTR Phase I: Novel Medical Equipment Utilization Tracking System for Improved Patient Safety and Hospital Efficiency

STTR 第一阶段：新型医疗设备使用跟踪系统，以提高患者安全和医院效率

• 批准号: 2321886
• 负责人: Connor Roberts
• 金额: $ 27.5万
• 依托单位: OPAL HTM INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321886&HistoricalAwards=false
• 关键词: STTR; Phase; Novel; Medical; Equipment

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project relates to the development of a novel system capable of measuring medical equipment utilization with high accuracy and scalability. This innovation will arm healthcare technology managers with the insights needed to optimize inventory size and composition according to actual patient needs, thereby saving hospitals an estimated $23.3 billion annually in equipment-related costs, in addition to making possible usage-based predictive maintenance that can effectively prevent dangerous equipment failures. Beyond these core value propositions, comprehensive medical equipment utilization insights may be leveraged to facilitate strategic resource management in public health emergencies, increase energy efficiency of healthcare facilities, and improve regulatory surveillance of emerging equipment safety issues. The results of this project will form the basis for a hardware-enabled service and clear the path towards development of deployable products, clinical pilots, and early sales. Through commercialization under a sustainable business model, the envisioned product will substantially increase the economic competitiveness of US hospitals, which comprises one of the largest sectors of the American economy. The project will also advance the health and welfare of the American public through improved medical device safety and management. This Small Business Technology Transfer (STTR) Phase I project will establish technical and commercial feasibility for an innovative, asset-agnostic, medical equipment utilization tracking system which will integrate state-of-the-art techniques for non-intrusive load monitoring, deep learning, and edge computing in order to overcome previously insurmountable asset monitoring challenges posed by the heterogeneity and churn of hospital equipment inventories. Key technical hurdles to be addressed relate to the capture and characterization of medical equipment electrical load data, real-time translation of this data into accurate usage statistics suitable for hospital decision-making, and distributed implementation of this process through non-invasive sensor modules that are broadly compatible with sundry medical equipment. The proposed research will overcome these hurdles through (i) systematic collection and analysis of power consumption data from a representative group of medical equipment under various operational states, (ii) formulation, training, and validation of adaptive artificial neural networks that predict usage from power data, (iii) construction of a proof-of-concept intelligent sensor module, and (iv) system performance testing in a simulated clinical environment. Through completion of these objectives, this project will advance knowledge in the fields of hospital asset management and industrial Internet-of-Things.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.

该小企业技术转让（STTR）第一阶段项目的更广泛影响/商业潜力涉及开发一种能够以高精度和可扩展性测量医疗设备利用率的新型系统。这项创新将为医疗技术管理人员提供根据实际患者需求优化库存规模和组成所需的洞察力，从而每年为医院节省约233亿美元的设备相关成本，此外还可以实现基于使用的预测性维护，从而有效防止危险的设备故障。除了这些核心价值主张之外，全面的医疗设备利用见解还可用于促进公共卫生紧急情况下的战略资源管理，提高医疗保健设施的能源效率，并改善对新兴设备安全问题的监管监督。该项目的成果将为硬件支持的服务奠定基础，并为开发可部署产品、临床试点和早期销售扫清道路。通过可持续商业模式下的商业化，设想的产品将大大提高美国医院的经济竞争力，这是美国经济中最大的部门之一。 该项目还将通过改善医疗器械安全和管理来促进美国公众的健康和福利。该小型企业技术转让（STTR）第一阶段项目将为创新的、资产不可知的医疗设备利用跟踪系统建立技术和商业可行性，该系统将集成最先进的非侵入式负载监控、深度学习和边缘计算技术，以克服医院设备库存的异质性和流失所带来的以前无法克服的资产监控挑战。需要解决的关键技术障碍涉及医疗设备电气负载数据的捕获和表征，将此数据实时转换为适合医院决策的准确使用统计数据，以及通过与医疗设备广泛兼容的非侵入式传感器模块分布式实现此过程。拟议的研究将通过以下方式克服这些障碍：（i）系统收集和分析各种操作状态下代表性医疗设备组的功耗数据，（ii）制定，训练和验证自适应人工神经网络，从功率数据预测使用情况，（iii）构建概念验证智能传感器模块，以及（iv）在模拟临床环境中的系统性能测试。通过完成这些目标，该项目将推进医院资产管理和工业物联网领域的知识。该奖项反映了NSF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。