Assessing Clinical Decision Support Systems in Enhancing Emergency Nursing Accuracy

Abstract

Clinical Decision Support Systems (CDSS) are becoming increasingly important in enhancing the accuracy of diagnosis and efficiency in intervention of emergency nursing practice. The current paper critically evaluates the implementation and effects of the CDSS in acute care settings in terms of its effect on clinical judgements, triage, and patient safety outcomes. The paper brings attention to the role of algorithm-based advice and evidence-based prompts in alleviating cognitive overload, reducing medication errors, and facilitating the decision-making process in a high-acuity setting. The electronic health record, laboratory system, and real time monitoring systems provide clinical data that are examined to assess the impact of CDSS on diagnostic sensitivity and specificity. The results highlight the role of CDSS as a way of enhancing compliance to clinical guidelines, enhancing faster sepsis detection, and better pain control and cardiac events management. Nonetheless, the issues of usability, the interoperability of the system, and the problem of alert fatigue still play a significant role in influencing the engagement of nurses and system stability. The paper suggests the incorporation of adaptive machine learning, prioritization of contextual alerts, and more effective training modules to increase compliance with the user and accuracy of the system. The evaluation summarizes that the successful integration of CDSS into the emergency processes is a cognitive continuation of clinical experience, which has a great impact on increasing the accuracy and safety of nursing judgments in time-intensive situations.