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.

该小型企业技术转移（STTR）项目的更广泛的影响/商业潜力与能够以高准确性和可扩展性来测量医疗设备利用的新型系统的开发有关。这项创新将使医疗保健技术经理拥有根据实际患者需求优化库存规模和组成所需的见解，从而为医院提供估计每年233亿美元的与设备相关的成本，此外还可以提供基于用途的预测性维护，从而可以有效防止危险设备危险设备。除了这些核心价值主张外，还可以利用全面的医疗设备利用见解来促进公共卫生紧急情况下的战略资源管理，提高医疗机构的能源效率，并改善对新兴设备安全问题的监管监视。该项目的结果将构成支持硬件服务的基础，并清除开发可部署产品，临床飞行员和早期销售的道路。通过在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，该医院是美国经济最大的部门之一。 该项目还将通过改进医疗设备的安全和管理来提高美国公众的健康和福利。这项小型企业技术转移（STTR）I阶段项目将建立技术和商业可行性，用于创新的，资产的，敏捷的医疗设备利用率跟踪系统，该系统将整合最新的技术，用于非犯罪负载监控，深度学习和边缘计算，以克服以前不可避免的不可避免的资产监控，以前不可避免地监测了由医院设备发明的挑战。要解决的关键技术障碍与医疗设备电气负载数据的捕获和表征，将该数据的实时转换为适合医院决策的准确使用统计数据以及通过非侵入性传感器模块的分布式实施，这些模块与Sundry医疗设备广泛兼容。拟议的研究将通过（i）在各个操作状态下的代表性医疗设备组的系统收集和分析（ii）制定，培训和验证自适应人工神经网络的制定，培训和验证，这些障碍的适应性人工神经网络，这些神经网络可预测功率数据的用途，（iii）构造智能传感器模块和（iv）的临时性测试。通过完成这些目标，该项目将在医院资产管理和工业互联网领域中提高知识。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。