I-Corps: Assessment Platform for Operationalizing High Reliability Organizational Hallmarks in Healthcare

I-Corps：用于在医疗保健领域实施高可靠性组织标志的评估平台

• 批准号: 2124331
• 负责人: Timothy Matis
• 金额: $ 5万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124331&HistoricalAwards=false
• 关键词: Corps; Assessment; Platform; Operationalizing; Reliability

The broader impact/commercial potential of this I-Corps project is the development of software with embedded machine learning algorithms that seeks to create a safer healthcare environment for both providers and patients. The potential market includes all types and sizes of healthcare institutions, including national healthcare systems, large regional hospitals, community health centers, rural hospitals, and ambulatory clinics. The goal of the proposed technology is to promote a culture of quality and patient safety by supporting administrative and clinical partnerships as they transition toward a goal of operationalizing High Reliability Organizational Hallmarks in healthcare. This change in culture may reduce the incidence of avoidable medical errors, and as a result, may make healthcare more affordable for the general population.This I-Corps project is based on the development of software that seeks to both increase the reporting of near-miss and safety incidents by clinicians and the utilization of these reports by administrators to make informed system-level improvements in healthcare based on High Reliability Organizational (HRO) theory. The lack of voluntary reporting of safety incidents arising from the inadequacy of protocols and software addressing the common physical and psychological barriers to reporting by the end user is problematic in healthcare. In addition, for those reports that are collected, there are no accepted rubrics for scientifically scoring them against the managerial hallmarks of an HRO; This marginalizes their inferential utility. The proposed technology addresses both of these issues through the implementation of a front-end web and application interface that is designed using human factors principles, and a back-end machine learning and natural language processing algorithm that classifies the corpus of safety incident reports in real-time.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/商业潜力是开发具有嵌入式机器学习算法的软件，旨在为提供者和患者创造更安全的医疗保健环境。 潜在市场包括所有类型和规模的医疗机构，包括国家医疗系统，大型区域医院，社区卫生中心，农村医院和门诊诊所。 拟议技术的目标是通过支持行政和临床合作伙伴关系，促进质量和患者安全的文化，因为他们向在医疗保健中实施高可靠性组织标志的目标过渡。这种文化上的变化可能会减少可避免的医疗差错的发生率，因此，这个I-Corps项目是基于软件的开发，该软件旨在增加临床医生对未遂事件和安全事件的报告，以及管理员对这些报告的利用，以使系统更加知情。基于高可靠性组织（HRO）理论的医疗保健水平改进。 由于解决最终用户报告的常见生理和心理障碍的协议和软件不足，导致安全事件缺乏自愿报告，这在医疗保健领域是一个问题。 此外，对于那些收集的报告，没有公认的标准来根据HRO的管理特点对其进行科学评分;这使其推理效用边缘化。所提出的技术通过使用人为因素原则设计的前端Web和应用程序界面的实现来解决这两个问题，以及后端机器学习和自然语言处理算法，其将安全事故报告的语料库分类为真实的-时间。该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和知识价值进行评估，被认为值得支持更广泛的影响审查标准。