What is it about?
Emergency departments (EDs) play an important role in health systems since they are the front line for patients with emergency medical conditions who frequently require diagnostic tests and timely treatment. To improve decision-making and accelerate processes in EDs, this study proposes predictive data mining models for classifying patients according to whether or not they are likely to require a diagnostic test based on referral diagnosis, age, gender, triage category and type of arrival.
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Why is it important?
This study provides guidance for ED triage staff, researchers and practitioners in making rapid decisions regarding patients’ diagnostic test requirements based on specified variables in the predictive models. This is critical in ED operations planning as it potentially decreases waiting times, while increasing patient satisfaction and operational performance.
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This page is a summary of: The likelihood of requiring a diagnostic test: Classifying emergency department patients with logistic regression, Health Information Management Journal, March 2020, SAGE Publications,
DOI: 10.1177/1833358320908975.
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