RAPID: Bridging the Health Care Skill Gap

该项目旨在满足国家提高能力的需求，以拥有更多合格的医疗保健提供者，以应对2019年冠状病毒病（COVID-19）的挑战。通过开发和部署基于web的工具，个人和雇主可以使用这些工具来探索与医疗保健相关的能力框架，自我识别技能差距，并找到证书和培训，该项目旨在填补医疗保健劳动力的空白。这些工具将帮助个人识别他们拥有相关技能的医疗保健领域，也将帮助公司识别需要提升医疗保健技能的员工。一旦列出了个人技能，该工具将帮助用户确定他们可以从哪里获得必要的医疗保健技能和证书。该工具将可用于桌面和移动设备，这扩大了这个项目的范围。该项目旨在通过为最终用户提供将自己的技能与现有医疗保健职位和证书所需的技能进行比较的能力，并在某些情况下找到填补差距的方法，从而解决COVID-19危机。框架和相关凭证将来自凭证引擎，其他数据将来自开放教学大纲项目。该项目将应用人工智能（AI），特别是自然语言理解（NLU），以(a)与这些证书相关的交叉能力框架，(b)将用户输入的技能与获得这些证书所需的技能进行比较，(c)生成衡量和量化两种技能之间差距的“一致性分数”，以及(d)建议有可能缩小这些差距的课程和证书计划。这将有助于研究NLU在基于能力的认证和培训中的应用，并将提供具体领域（医疗保健）的数据。包括通过开放数据基础设施验证和共享基本能力框架翻译和一致性评分服务，将改善我国的医疗保健人才管道。该RAPID奖项由综合活动办公室的融合加速器项目颁发，并与融合加速器轨道B：工作的未来和人类技术前沿有关。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。

The project aims to serve the national need of increasing the capacity to have more qualified healthcare providers to address the challenge of the Coronavirus Disease 2019 (COVID-19). By developing and deploying web-based tools that individuals and employers can use to explore healthcare-related competency frameworks, self-identify skill gaps, and find credentials and training, this project seeks to fill gaps in the healthcare workforce. These tools will help individuals identify healthcare areas where they have adjacent skill sets and will also help companies identify employees who are candidates for upskilling into healthcare. Once individual skills are inventoried, the tool will help users identify where they can obtain the requisite healthcare skills and credentials. The tool will be usable on desktop and mobile devices which broadens the reach of this project. The project seeks to address the COVID-19 crisis by providing end users with the ability to compare their own skills with those required for available healthcare roles and credentials and, in some cases, find ways to fill gaps. The frameworks and related credentials will come from the Credential Engine and additional data will come from the Open Syllabus Project. This project will apply artificial intelligence (AI), specifically natural language understanding (NLU), to (a) crosswalk competency frameworks associated with these credentials, (b) compare skills entered by users to those required to earn those credentials, (c) generate an "alignment score" that measures and quantifies the gap between the two skill sets, and (d) suggest courses and credential programs with the potential to close those gaps. This will contribute to research into applications of NLU to competency-based credentialing and training and will provide data in a concrete domain (healthcare). Including validation and sharing of the underlying competency framework translation and alignment score services via an open data infrastructure will improve our nation’s healthcare talent pipeline.This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities and is associated with the Convergence Accelerator Track B: Future of Work and the Human-Technology Frontier.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.