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

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合格医生队伍。目前的评估实践需要主治医生和外科医生审查数十到数百个数据点，将其从临床活动中删除。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，并在数月前识别出优秀、有能力和苦苦挣扎的住院医师。这对患者护理至关重要：早期识别受训者的表现可以比目前基于人工的半年一次的流程更快地使患者护理受益。多个利益相关者可能会对能力的改进跟踪和记录感兴趣，这些利益相关者包括患者、医院、第三方付款人（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理事会）。利用现有学员数据改进的自动化评估模型，可帮助面临日益增加的文档负担的培训项目，以及希望减少患者不良健康事件的医院和第三方付款人。这一小型企业创新研究（SBIR）第一阶段项目将整合人工智能模型，支持住院医师培训项目根据学员个人需求定制培训。从整形和重建手术开始，美国从事时变训练的四个培训项目之一是创建，测试和评估模型功效的有效方法。创建的机器学习模型将在住院医师培训期间的不同时间点评估其预测能力，并与主治医师对学员技能的评估进行比较。这种模式使时变培训可行，使各种教育轮换的适应性，基于需求的调度。这有额外的好处，保持居民充分参与他们的培训和返回教师医生临床护理更快，提高工作满意度和降低倦怠的风险。最终，随时间变化的培训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;加速新的、完全胜任的医生进入劳动力市场;该奖项反映了NSF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进行评估，被认为值得支持的搜索.

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.