<![CDATA[Catastrophic diseases, such as heart attacks, strokes, and acute organ failure, require rapid and accurate prediction to improve emergency response and patient survival. Artificial Intelligence (AI) has been increasingly utilized to enhance diagnostic accuracy and early warning systems in medical emergencies. However, the effectiveness and challenges of AI implementation in emergency prediction remain a critical area of study. This study employed a quantitative method with a retrospective observational analytic design. Data were collected from electronic medical records of 500 patients with catastrophic diseases at RS Grandmed Lubuk Pakam. Univariate analysis was conducted to describe patient characteristics, while bivariate and multivariate analyses were performed using logistic regression to evaluate the predictive capabilities of AI models in emergency cases. The study analyzed data from 500 patients, where AI-based prediction models demonstrated an accuracy rate of 87% in identifying high-risk patients. The Chi-Square test showed a significant relationship between AI predictions and actual emergency events (p < 0,001). Logistic regression indicated that AI-based models were 3.2 times more effective in predicting emergencies compared to traditional methods (p < 0.001). The findings align with previous studies that highlight AI’s potential in enhancing medical decision-making. However, challenges such as data quality, model interpretability, and integration with clinical workflows must be addressed. The study emphasizes the need for further research to optimize AI algorithms and ensure ethical, safe, and effective implementation in emergency medical settings.]]>
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