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A Decision Support System for Pathology Test Result Reviews in an Emergency Department to Support Patient Safety and Increase Efficiency. | LitMetric

AI Article Synopsis

  • The review of pathology test results in Emergency Departments can be tedious and often leads to inaccuracies, prompting the need for an automated solution.
  • The developed system utilizes text mining and clinical terminology to effectively analyze microbiology test results, significantly reducing the volume of results needing review by 92%.
  • By reconciling antibiotic sensitivities with discharge prescriptions, the system enhances patient safety and ensures that important diagnoses are accurately identified, achieving a high 91% accuracy in prioritizing follow-up cases.

Article Abstract

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.

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Source
http://dx.doi.org/10.3233/SHTI190319DOI Listing

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