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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | Texas Tech University |
| Country | United States |
| Start Date | Mar 01, 2021 |
| End Date | Apr 30, 2023 |
| Duration | 790 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2124331 |
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.
Texas Tech University
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