Pakistan Journal of Engineering Technology and Science (PJETS) Volume 12. Issue.02, PP. 53-64, December 2024 E-ISSN 2224-2333 https://doi.org/10.22555/pjets.v12i2.1115 P-ISSN 2222-9930 53 Creative Common CC BY: This article is distributed under the terms of the Creative Commons Attributes 4.0 License. It permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Intelligent Decision Support Systems for Optimizing Medical Emergency Responses Rubaisha Waqar Ahmed 1 , Sidra Abid Syed 1 , Mariam Raziq 1 , Shahzad Nasim * 2 , and Syed Jamal Haider Zaidi 3 1 Department of Biomedical Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan 2* Department of Management Sciences & Technology, The Begum Nusrat Bhutto Women University, Sukkur 3 IQRA University, Karachi, Pakistan *Corresponding Author: shahzadnasim@live.com Abstract: Nowadays, in the realm of an emergency medical services-EMS, swift and an accurate decision-making is a critical for an ensuring of timely responses and all the optimal patient outcome results. In Emergency Medical Services (EMS), fast and accurate decision-making is crucial for timely responses and optimal patient outcomes. With the rise of advanced technologies and the growing availability of healthcare data, Intelligent Decision Support Systems (IDSS) present an opportunity to enhance medical emergency responses (MER). This paper explores how IDSS can optimize MER by incorporating real-time analytics, data integration, and decision-support algorithms. An effective IDSS integrates diverse data sources such as patient health records, historical incidents, and geographical information. This integration enables comprehensive situational awareness, allowing responders to make informed decisions tailored to each emergency scenario. Real-time analytics help process incoming data to detect patterns, trends, and anomalies. Through machine learning and predictive modelling, IDSS can anticipate emergencies, allocate resources proactively, and optimize response routes, reducing patient treatment times. Decision-support algorithms embedded in IDSS provide actionable insights based on available data. These algorithms consider factors such as the severity of medical conditions, proximity to healthcare facilities, and available equipment, helping responders prioritize tasks and allocate resources efficiently. Keywords: Intelligent Decision Support Systems-IDSS, Medical Emergency Responses-MER, Data Integration, Real- Time Analytics, Predictive Modeling, Emergency Medical Services-EMS. I. INTRODUCTION In today’s dynamic healthcare landscape, the effective management of medical emergencies poses significant challenges ranging from resource allocation to timely response coordination. Intelligent Decision Support Systems have emerged as a promising solution to optimize medical emergency responses by utilizing an advanced promising solution for MER-optimization technologies, such as AI-artificial intelligence, ML-machine learning, and data analytics. These systems integrate real-time data from various sources, including patient information, geographic data, and historical trends, to facilitate informed decision-making among healthcare professionals and emergency respondents. [4-6] The deployment of an IDSS in the context of a medical emergency offers multifaceted benefits, including enhanced resource utilization, improved response times, and, ultimately, better patient outcomes. By analyzing vast amounts of data and taking factors like severity levels of the conditions, resource availabilities, and geographical constraints, IDSS can assist emergency respondents in prioritizing cases, allocating resources efficiently, and navigating through complex scenarios with optimal strategies. [4-6] Furthermore, an IDSS can adapt and evolve based on feedback loops and continuous learning from past responses, thereby refining decision-making processes and enhancing overall system performance. Utilization of predictive modelling and pattern recognition techniques, these systems can also anticipate potential emergencies, identify high-risk