SBIR Phase I: Combining Machine Learning with Clinical Expertise to Assess and Mitigate Risk in Healthcare

SBIR 第一阶段：将机器学习与临床专业知识相结合，评估和降低医疗保健风险

• 批准号: 2208120
• 负责人: James Levett
• 金额: $ 25.6万
• 依托单位: PRESAJ, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208120&HistoricalAwards=false
• 关键词: SBIR; Phase; Combining; Machine; Learning

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will improve health care outcomes associated with complex procedures. Approximately 1 in 7 major surgical procedures in the US is associated with a medical complication, totaling more than 4 million complications and $80 billion in costs per year. Many more complications occur in non-surgical settings. This project will use machine learning to combine proven engineering principles with clinical expertise to identify and address specific risks for each procedure, care facility, and patient (accounting for high-impact risk factors ranging from diabetes to social determinants of health). This technology will augment existing standardized, outcome-oriented quality-improvement tools with cost-effective customized, process-oriented tools in a novel way, with an envisioned initial application for the ~5,100 community hospitals in the US. A modest improvement of 1% of complications would annually reduce costs by nearly $1 billion and will save 4,500+ lives.This Small Business Innovation Research (SBIR) Phase I project will use a systems-based approach to examine process-level risk in healthcare. Big data in healthcare is inconsistently structured and not optimized to directly improve patient outcomes. The large datasets for most procedures provide only high-level conclusions regarding risk; they do not pinpoint the specific steps in provider workflow with high risk or the role of external factors, such as comorbidities or facility age. This project will determine the feasibility of using machine learning supervised by experienced clinicians to assess risk using principles from Failure Modes and Effects Analysis. The project will develop a proof-of-concept machine-learning system that uses a proprietary risk taxonomy and modifiers to combine national, state, facility, and actuarial datasets to generate risk priority numbers for each step for a service line. This system will then be applied to coronary artery bypass graft surgery to assess its validity and clinical value. Monte Carlo simulations and clinician focus groups using a Likert scale will determine the significance of the results.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）I期项目的更广泛的影响/商业潜力将改善与复杂程序相关的医疗保健结果。 在美国，大约有7个主要的手术程序与医疗并发症有关，总计超过400万并发症和每年800亿美元的费用。在非手术环境中发生了更多并发症。该项目将使用机器学习将久经考验的工程原理与临床专业知识相结合，以识别和解决每个程序，护理机构和患者的特定风险（考虑了从糖尿病到健康的社会决定因素的高影响力风险因素）。这项技术将以新颖的方式使用具有成本效益的定制，以过程为导向的工具来增强现有的标准化，面向结果的质量改善工具，并设想了美国约5100家社区医院的初步应用。 每年1％的并发症的适度改善将使成本降低近10亿美元，并可以节省4,500多个生命。这项小型企业创新研究（SBIR）I阶段项目将使用基于系统的方法来检查医疗保健过程中的过程级别风险。医疗保健中的大数据结构不一致，并未优化以直接改善患者预后。大多数程序的大数据集仅提供有关风险的高级结论；他们没有指出具有高风险或外部因素（例如合并症或设施年龄）的提供商工作流程的特定步骤。该项目将确定使用经验丰富的临床医生监督的机器学习的可行性，以使用失败模式和效果分析中的原理评估风险。该项目将开发一个概念验证的机器学习系统，该系统使用专有风险分类法和修饰符将国家，州，设施和精算数据集相结合，以为服务线的每个步骤生成风险优先级。然后，该系统将应用于冠状动脉搭桥手术，以评估其有效性和临床价值。使用李克特量表的蒙特卡洛模拟和临床医生焦点小组将确定结果的重要性。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估。