Dr. Chen Liang, an assistant professor in the Department of Health Services Policy and Management at the University of South Carolina Arnold School of Public Health, has collaborated with colleagues at the University of Texas Health Science Center and Louisiana Tech University to complete a systems-centered analysis of patient safety events. He published the paper in the International Journal of Medical Informatics.
“Systems-centered root cause analysis of patient safety events presents unique advantages as it aims to disclose vulnerabilities of healthcare systems,” says Dr. Liang. “However, the increasing number of collected events poses the problems of low efficiency and information overload for traditional root cause analysis.”
With this study, the researchers aimed to improve systems-centered root cause analysis by developing information extraction and presentation. In doing so, they experimented with supervised machine-learning methods to extract safety-related information from more than 3,000 patient safety event reports and evaluated how optimized information presentation could help facilitate the disclosure of system vulnerabilities in traditional root cause analysis (RCA).
“Our study demonstrated the feasibility of using multilabel text classification for identifying safety-related information from the narrative description of patient safety events,” says Dr. Liang. “The extracted information when grouped by safety-related information can better aid human experts to conduct systems-centered RCA and disclose system vulnerabilities.”Tags: Friday Letter Submission, Publish on January 31