I-Corps: Trustworthy Medical Code Recommendations

I-Corps：值得信赖的医疗规范建议

• 批准号: 2325785
• 负责人: Syed Ahmad Chan Bukhari
• 金额: $ 5万
• 依托单位: Saint John's University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325785&HistoricalAwards=false
• 关键词: Corps; Trustworthy; Medical; Code; Recommendations

The broader impact/commercial potential of this I-Corps project is to revolutionize the medical coding process by increasing the efficiency, accuracy, and explainability of artificially intelligence-driven computer-assisted coding systems. The innovation has the potential to reduce human effort and errors, streamline medical claims and billing processes, and integrate information systems across healthcare providers, payers, and insurance companies. The increased transparency and trustworthiness of artificially intelligent generated codes, in turn, would lead to faster processing and resolution of medical bills and claims with fewer rejections. As a result, healthcare providers will save costs on providing quality healthcare services with better patient outcomes. The project’s broader significance also lies in its applicability to information systems in other industries that rely on coding and structured information, such as pharmaceuticals, bio-medical, legal, and accounting.This I-Corps project is based on the development of an innovative technology that combines deep learning attention mechanisms with symbolic artificially intelligent, specifically knowledge graphs, to improve the explainability of computer-assisted coding models. The approach focuses on providing a deeper understanding of the relationships between highlighted words and predicted standard medical codes, thereby enhancing the accuracy and efficiency of the medical coding process. By addressing the need for increased transparency in artificially intelligent-driven medical coding systems, the project aims to advance the field of artificially intelligent explainability and contribute to the scientific understanding of the subject. The technology has the potential to pave the way for future advancements in artificially intelligent-driven coding systems in various domains and industries reliant on highly structured knowledge bases.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.

这个i-Corps项目的更广泛的影响/商业潜力是通过提高人工智能驱动的计算机辅助编码系统的效率、准确性和可解释性来彻底改变医学编码过程。这项创新有可能减少人为工作和错误，简化医疗索赔和账单流程，并整合医疗保健提供者、支付者和保险公司的信息系统。反过来，人工智能生成的代码的透明度和可信度的提高将导致更快地处理和解决医疗账单和索赔，并减少拒绝。因此，医疗保健提供者将在提供高质量医疗服务和更好的患者结果方面节省成本。该项目的更广泛的意义还在于它适用于其他依赖编码和结构化信息的行业的信息系统，如制药、生物医疗、法律和会计。该i-Corps项目基于一项创新技术的开发，该技术将深度学习注意机制与符号人工智能，特别是知识图相结合，以提高计算机辅助编码模型的可解释性。该方法侧重于更深入地理解突出显示的单词与预测的标准医疗编码之间的关系，从而提高医疗编码过程的准确性和效率。通过解决人工智能驱动的医疗编码系统中增加透明度的需求，该项目旨在推进人工智能可解释性领域，并促进对该主题的科学理解。这项技术有可能为人工智能驱动的编码系统在依赖于高度结构化知识库的各个领域和行业的未来进步铺平道路。这一奖项反映了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。