Clinical Decision Support Systems (CDSS) have emerged as a critical component of modern healthcare management, particularly in the context of hospitals striving to enhance patient safety, improve quality of care, and optimize clinical workflows. These systems integrate medical knowledge, patient data, and evidence-based guidelines to provide real-time assistance to clinicians during the diagnostic and therapeutic decision-making process. In an era where healthcare complexity is rapidly increasing due to factors such as aging populations, chronic disease prevalence, and technological advancements, CDSS serves as a bridge between clinical expertise and computational intelligence. The primary function of CDSS is to support clinicians by offering relevant patient-specific information and recommendations at the point of care. This encompasses a wide range of applications, including alerts for potential drug interactions, reminders for preventive care, diagnostic support, and treatment suggestions based on the latest clinical guidelines. For instance, a physician prescribing medications for a patient with multiple comorbidities can rely on CDSS to automatically check for potential adverse drug interactions or contraindications. This reduces the risk of medication errors, which are a significant cause of morbidity and mortality in hospitals. By ensuring that clinicians have access to accurate and up-to-date information, CDSS enhances the overall safety of patient care. In addition to reducing errors, CDSS improves clinical decision-making by standardizing the approach to complex medical conditions. Hospitals face the challenge of variability in clinical practice, where different physicians might make different decisions for the same clinical scenario. CDSS addresses this by embedding evidence-based protocols and guidelines into the system, guiding clinicians toward decisions that align with best practices. For example, in managing conditions like sepsis, myocardial infarction, or diabetes, CDSS can provide step-by-step recommendations for diagnostics, treatment initiation, and monitoring, thereby reducing practice variability