SBIR Phase I: Artificial Intelligence for Competency-Based Medical Training

SBIR 第一阶段：基于能力的医疗培训的人工智能

• 批准号: 2112208
• 负责人: Carisa Cooney
• 金额: $ 25.6万
• 依托单位: EDUMD, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112208&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; Intelligence; Competency

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to train physicians efficiently, assure high-quality patient care, and provide the United States with a robustly competent physician workforce. Current assessment practices require attending physicians and surgeons to review tens-to-hundreds of data points, removing them from clinical activities. Integrating a machine learning model in an existing resident assessment system to predict performance can address trainees’ learning needs and identify excelling, competent, and struggling residents months earlier. This is vital to patient care: earlier identification of trainee performance can benefit patient care faster than the current human-based, semiannual process. Improved tracking and documentation of competence may be of interest to multiple stakeholders including patients, hospitals, third-party payers such as insurance companies or the Centers for Medicare and Medicaid Services, and the residency accreditation entity, the Accreditation Council for Graduate Medical Education. Improved, automated assessment models using existing trainee data help training programs facing increasing documentation burden, as well as hospitals and third-party payers interested in reducing adverse health events for the patients they serve.This Small Business Innovation Research (SBIR) Phase I project will integrate an artificial intelligence model to support resident physician training programs in customizing training based on individual learners’ needs. Starting with plastic and reconstructive surgery and one of the four training programs in the United States engaged in time-variable training is an efficient way to create, test, and assess the model’s efficacy. The created machine learning model will be assessed for its predictive ability at different points during resident physicians’ training and compared with attending physicians’ assessments of trainees’ skills. Such models make time-variable training feasible enabling adaptive, needs-based scheduling of various educational rotations. This has the added advantages of keeping residents fully engaged in their training and returning faculty physicians to clinical care faster, improving job satisfaction and reducing risk of burnout. Ultimately, time-variable training and use of their associated machine learning models will reduce the direct and indirect costs of graduate medical education; accelerate the entry of new, fully competent physicians into the workforce; and retain valuable physician educators in the workforce.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/商业潜力是有效地培训医生，确保高质量的患者护理，并为美国提供一支强大的有能力的医生队伍。目前的评估实践要求主治医师和外科医生审查数十至数百个数据点，将其从临床活动中移除。在现有的住院医师评估系统中集成机器学习模型来预测绩效，可以满足实习生的学习需求，并在几个月前识别出优秀、有能力和挣扎的住院医师。这对患者护理至关重要：与目前以人为基础的半年一次的流程相比，早期识别培训生的表现可以更快地受益于患者护理。改进对能力的跟踪和记录可能对包括患者、医院、第三方支付方（如保险公司或医疗保险和医疗补助服务中心）以及住院医师认证实体（研究生医学教育认证委员会）在内的多个利益攸关方感兴趣。使用现有受训人员数据的改进的自动化评估模型有助于培训项目应对日益增加的文件负担，也有助于医院和第三方付款人减少他们所服务的患者的不良健康事件。这个小企业创新研究（SBIR）第一阶段项目将整合一个人工智能模型，以支持住院医师培训计划，根据个人学习者的需求定制培训。从整形和重建手术开始，美国有四个培训项目，其中之一是进行时变培训，这是一种有效的方法来创建、测试和评估模型的功效。创建的机器学习模型将在住院医生培训的不同阶段评估其预测能力，并与主治医生对学员技能的评估进行比较。这样的模型使时变训练可行，使各种教育轮换的自适应、基于需求的调度成为可能。这样做还有额外的好处，可以让住院医生充分参与培训，让实习医生更快地回到临床护理岗位，提高工作满意度，降低职业倦怠的风险。最终，时变训练和相关机器学习模型的使用将降低研究生医学教育的直接和间接成本；加快新的、完全有能力的医生进入劳动力市场；留住有价值的医师教育工作者。该奖项反映了美国国家科学基金会的法定使命，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。