SBIR Phase II: An adaptive machine learning-based platform to improve surgical quality and patient outcomes

SBIR II 期：基于自适应机器学习的平台，可提高手术质量和患者治疗效果

• 批准号: 1926924
• 负责人: Bora Chang
• 金额: $ 69.39万
• 依托单位: kelaHealth Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926924&HistoricalAwards=false
• 关键词: SBIR; Phase; II; adaptive; machine

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be to help usher in personalized and tailored surgical care within a shifting healthcare context toward value-based care. Hospitals and surgeons are seeking solutions that will enable them to target, as opposed to generalizing, improvements in surgical quality for enhanced patient outcomes and effective use of resources. By proactively identifying surgical risks and matching patients to interventions most appropriate for these risk strata, the proposed technology is designed to support hospitals in meeting their value-based care objectives. The larger vision is to apply this paradigm in all of medicine by leveraging Artificial Intelligence and Machine Learning for prediction, proactive intervention, and outcomes tracking in a closed feedback loop. Demonstrating this in a high-cost, high-risk specialty like surgery provides a path for expanding the technology into other medical specialties and serving a greater domestic and international market. Ultimately, the lessons learned from the wide-spread use of this technology will allow society to derive key kernels of knowledge in applied data science, preventative medicine, and technical scalability of hospital enterprise solutions. This project is an interdisciplinary representation of crucial activities needed to drive the tipping point of medical technology. This Small Business Innovation Research (SBIR) Phase II project builds upon the results of Phase I, which included predictive engine development, scalable data processing pipeline development, and hospital stakeholder engagement activities. Phase II efforts focus on further developing the technology to facilitate its commercial use and integration in clinical settings. Key objectives for the Phase II project are as follows: (1) development of an Application Programming Interface (API) to deliver tailored machine learning models to broad users across varying needs, (2) expansion of a clinical intervention library supported by clinical evidence across multiple surgical specialties, and (3) development of an outcomes dashboard to display postoperative patient outcomes from automated extraction of electronic health records. The result of this project will be a closed-loop clinical and technical infrastructure that is agile to the needs of a diverse range of surgical customers to enable quality improvement across an entire surgical ecosystem.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.

这项小型企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力将是在转变的医疗保健环境中向基于价值的护理转移的个性化和量身定制的手术护理。医院和外科医生正在寻求解决方案，使他们能够针对手术质量的改善，以提高患者预后和有效利用资源。通过主动识别手术风险并将患者与最适合这些风险层的干预措施相匹配，该技术旨在支持医院实现其基于价值的护理目标。更大的愿景是通过利用人工智能和机器学习来预测，主动干预以及在封闭的反馈循环中跟踪结果，将此范式应用于所有医学中。在高成本的高风险专业（例如手术）中证明这一点为将技术扩展到其他医学专业并为更大的国内和国际市场提供服务提供了途径。最终，从该技术的广泛使用中学到的经验教训将使社会能够在应用数据科学，预防医学和医院企业解决方案的技术可扩展性方面得出重要的知识知识。该项目是推动医疗技术临界点所需的关键活动的跨学科代表。这项小型企业创新研究（SBIR）II期项目基于第一阶段的结果，其中包括预测性引擎开发，可扩展的数据处理管道开发以及医院利益相关者的参与活动。第二阶段的工作着重于进一步开发该技术，以促进其在临床环境中的商业用途和集成。第二阶段项目的关键目标如下：（1）开发应用程序编程接口（API），以向跨不同需求的广泛用户提供量身定制的机器学习模型，（2）扩展临床干预库，并在跨越临床证据支持的临床干预库中多个手术专业和（3）开发结果仪表板，以显示电子健康记录自动提取的术后患者结果。该项目的结果将是一个闭环临床和技术基础设施，它敏捷了各种各样的手术客户的需求，以在整个手术生态系统中进行质量改进。这一奖项反映了NSF的法定任务，并被认为值得。通过基金会的智力优点和更广泛的影响评估标准通过评估来支持。