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

该I-Corps项目为学术组织的卫生服务研究人员和数据资源相关决策者开发了一个集中的数据管理平台。 该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，并有更多的时间专注于网络，科学发现和创新。 此外，该技术提供了先进的数据清洗和预处理，并可用于新兴的合成数据行业和生成式人工智能（AI）技术的数据解决方案。 该平台的简化流程旨在生成基于证据的知识，可供医疗管理人员、政策制定者和决策者利用，以增强医疗服务。 未来，该平台不仅可以作为研究工具，还可以促进用户的数据知情决策能力。这个I-Corps项目利用体验式学习，结合对行业生态系统的第一手调查，评估该技术的翻译潜力。该解决方案基于软件技术的开发，解决了卫生服务研究人员面临的公共和私人来源的数据聚合，清理，集成和维护的挑战。 该技术使用集中式数据仓库，提供分析就绪的数据集，并减轻研究人员的重复数据任务。此外，它通过使用传统统计工具和高级人工智能（AI）算法的混合改进数据采集，提取，预处理和清理阶段来改进数据管理的各个方面。 由于该平台集成了新的数据源和方法，因此它旨在扩展，以确保可扩展性。 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的研究的智力追求。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.