Nguyen Anthony, Hassanzadeh Hamed, Zhang Yushi, O'Dwyer John, Conlan David, Lawley Michael, Steel Jim, Loi Kylynn, Rizzo Peter
The Australian e-Health Research Centre, CSIRO, Brisbane, Queensland, Australia.
The Prince Charles Hospital, Department of Health, Brisbane, Queensland, Australia.
Stud Health Technol Inform. 2019 Aug 21;264:729-733. doi: 10.3233/SHTI190319.
The review of pathology test results for missed diagnoses in Emergency Departments is time-consuming, laborious, and can be inaccurate. An automated solution, with text mining and clinical terminology semantic capabilities, was developed to provide clinical decision support. The system focused on the review of microbiology test results that contained information on culture strains and their antibiotic sensitivities, both of which can have a significant impact on ongoing patient safety and clinical care. The system was highly effective at identifying abnormal test results, reducing the number of test results for review by 92%. Furthermore, the system reconciled antibiotic sensitivities with documented antibiotic prescriptions in discharge summaries to identify patient follow-ups with a 91% F-measure - allowing for the accurate prioritization of cases for review. The system dramatically increases accuracy, efficiency, and supports patient safety by ensuring important diagnoses are recognized and correct antibiotics are prescribed.
急诊科对漏诊的病理检查结果进行复查既耗时又费力,而且可能不准确。开发了一种具有文本挖掘和临床术语语义功能的自动化解决方案,以提供临床决策支持。该系统专注于复查微生物学检查结果,这些结果包含培养菌株及其抗生素敏感性信息,这两者都可能对患者的持续安全和临床护理产生重大影响。该系统在识别异常检查结果方面非常有效,将需要复查的检查结果数量减少了92%。此外,该系统将出院小结中的抗生素敏感性与记录的抗生素处方进行核对,以91%的F值识别患者随访情况,从而准确确定需要复查的病例优先级。该系统通过确保识别重要诊断并开具正确的抗生素,显著提高了准确性、效率并支持患者安全。