This paper introduces a novel Dynamic Access Decision Scoring (ADS) framework that integrates cognitive computing and big data to address emerging challenges in controlling access to healthcare data systems. Traditional rule-based access control mechanisms lack the cognitive capabilities to process dynamic security requirements, creating vulnerabilities when managing large-scale electronic health records (EHRs). Our framework leverages cognitive computing by combining machine learning algorithms, behavioral pattern analysis, and real-time data analytics to create an intelligent security system that safeguards sensitive medical data while maintaining computational efficiency. The core innovation lies in developing a cognitive mathematical template that data scientists and researchers can adapt through deep learning and analytical processing. The framework introduces a modular formula as an adaptive cognitive pattern, incorporating four computational elements: machine learning predictions, historical pattern recognition, risk analytics, and temporal context processing. Each element employs cognitive algorithms that security architects can calibrate within their specific data ecosystems. The framework’s primary contribution demonstrates how cognitive probabilistic approaches can dynamically adapt to complex healthcare environments. This research advances big data security by establishing a cognitive computing foundation for making access control decisions, effectively bridging theoretical data models with practical machine intelligence implementation in healthcare information systems.