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.

这个小企业创新研究（SBIR）第一阶段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，大约七分之一的重大外科手术与医疗并发症有关，每年的并发症总数超过400万例，费用高达800亿美元。更多的并发症发生在非手术环境中。该项目将使用机器学习将联合收割机成熟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，护理设施和患者的特定风险（考虑从糖尿病到健康的社会决定因素等高影响风险因素）。这项技术将以一种新颖的方式，用具有成本效益的定制化、面向过程的工具来增强现有的标准化、以结果为导向的质量改进工具，预计将在美国约5,100家社区医院中初步应用。 并发症的1%的适度改善将每年减少近10亿美元的成本，并将挽救4，500多人的生命。这个小企业创新研究（SBIR）第一阶段项目将使用基于系统的方法来检查医疗保健中的流程级风险。医疗保健中的大数据结构不一致，并且没有优化以直接改善患者的结果。大多数手术的大型数据集仅提供了关于风险的高级别结论;它们没有指出高风险提供者工作流程中的具体步骤或外部因素的作用，例如合并症或设施年龄。该项目将确定在经验丰富的临床医生的监督下使用机器学习的可行性，以使用失效模式和效应分析的原则评估风险。该项目将开发一个概念验证机器学习系统，该系统使用专有的风险分类和修改器来联合收割机结合国家、州、设施和精算数据集，为服务线的每个步骤生成风险优先级数字。将该系统应用于冠状动脉旁路移植术，以评估其有效性和临床价值。Monte Carlo模拟和临床医生焦点小组使用李克特量表将确定结果的重要性。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。