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

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是高效培训医生，确保高质量的患者护理，并为美国提供一支能力雄厚的医生队伍。目前的评估做法要求主治医生和外科医生审查数十到数百个数据点，将它们从临床活动中删除。将机器学习模型集成到现有的住院医师评估系统中以预测绩效，可以满足学员的学习需求，并在几个月前识别出优秀、有能力和努力的住院医师。这对患者护理至关重要：与当前以人为基础的半年一次的流程相比，及早确定受训人员的表现可以更快地使患者护理受益。改进对能力的跟踪和记录可能会引起多个利益相关者的兴趣，包括患者、医院、第三方付款人，如保险公司或联邦医疗保险和医疗补助服务中心，以及住院医师资格认证实体研究生医学教育认证委员会。使用现有学员数据的改进的自动化评估模型有助于面临越来越大的文档负担的培训计划，以及对减少他们服务的患者的不良健康事件感兴趣的医院和第三方付款人。这个小型企业创新研究(SBIR)第一阶段项目将集成人工智能模型，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，这是美国从事时变培训的四个培训项目之一，是创建、测试和评估该模型有效性的有效方法。创建的机器学习模型将在住院医生培训期间的不同时间点评估其预测能力，并与主治医生对学员技能的评估进行比较。这样的模型使时变培训变得可行，从而能够对各种教育轮换进行适应性的、基于需求的安排。这还有一个额外的好处，那就是让住院医生全身心地投入到培训中，并更快地将教职医生送回临床护理，提高工作满意度，降低职业倦怠的风险。最终，时变培训和相关机器学习模型的使用将降低医学研究生教育的直接和间接成本；加快新的、完全胜任的医生进入劳动力大军；并留住有价值的医生教育工作者。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.