Development clinical decision support system to enhance accuracy in diagnosis and treatment of stroke in emergency patients

Abstract

Objective: To develop and evaluate the effectiveness of a Clinical Decision Support System (CDSS) for diagnosing and treating stroke patients in the emergency department.

Methods: A quasi-experimental one-group pretest-posttest study was conducted. The CDSS was developed using Decision Trees algorithm, trained on retrospective patient data from 2021-2023 (n=300). The dataset was split into 70% training, 20% validation, and 10% testing sets. A pilot study was conducted with 30 patients.

Results: The developed model achieved 92.5% diagnostic accuracy, 91.8% precision, 93.2% recall, and 92.5% F1-score. After implementation, the mean door-to-needle time significantly decreased from 49.46 to 37 minutes (25.19% reduction, p<0.05). Physician satisfaction with the system was 95%, and no incidents of misdiagnosis or medication complications were reported.

Conclusion: The developed CDSS significantly improved the efficiency of stroke diagnosis and treatment in the emergency department by reducing treatment time and enhancing physician confidence in decision-making.

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