I-Corps: Translation Potential of a Centralized Platform for Health Services Research

这个i-Corps项目为学术组织中的卫生服务研究人员和与数据资源相关的决策者开发了一个集中的数据管理平台。这项技术为研究人员提供了数据管理和分析工具，以加快他们的工作，并让他们有更多时间专注于网络、科学发现和创新。此外，该技术提供先进的数据清理和预处理，并可与新兴合成数据行业和生成性人工智能(AI)技术的数据解决方案一起使用。该平台简化的流程旨在生成基于证据的知识，医疗保健管理员、政策制定者和决策者可以利用这些知识来增强医疗保健服务。未来，该平台不仅可以作为研究工具，还可以为用户提供数据知情的决策能力。这个i-Corps项目利用体验式学习和对行业生态系统的第一手调查来评估该技术的翻译潜力。该解决方案基于软件技术的开发，以解决卫生服务研究人员在公共和私人来源的数据聚合、清理、集成和维护方面面临的挑战。该技术使用一个集中的数据仓库，提供可供分析的数据集，并减轻研究人员的重复数据任务。此外，它通过混合使用传统统计工具和高级人工智能(AI)算法来改进数据采集、提取、预处理和清理阶段，从而改进了数据管理的各个方面。随着该平台集成新的数据源和方法，它旨在进行扩展，确保可伸缩性。这种方法可以节省研究人员的时间和资源，使他们能够专注于他们研究的智力追求。这一奖项反映了NSF的法定使命，并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，被认为值得支持。

This I-Corps project develops a centralized data management platform for health services researchers and data-resource related decision-makers at academic organizations. The technology provides data management and analytics tools to researchers to expedite their work and allow more time to focus on networking, scientific discovery, and innovation. In addition, the technology provides advanced data cleansing and preprocessing, and may be used with data solutions for the emerging synthetic data industry and generative artificial intelligence (AI) technologies. The platform's streamlined processes are designed to generate evidence-based knowledge that may be leveraged by healthcare administrators, policymakers, and decision-makers to enhance healthcare services. In the future, the platform may not only serve as a research tool but also facilitate data-informed decision-making capabilities for users.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of software technology that addresses the challenges health services researchers face with data aggregation, cleansing, integration, and maintenance from public and private sources. The technology uses a centralized data warehouse, providing analysis-ready data sets and alleviating researchers from repetitive data tasks. In addition, it refines aspects of data management by improving data acquisition, extraction, preprocessing, and cleansing phases using a mix of traditional statistical tools and advanced artificial intelligence (AI) algorithms. As the platform integrates new data sources and methodologies, it's designed to expand, ensuring scalability. This approach may save researchers time and resources, enabling them to focus on the intellectual pursuits of their research.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.