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Diagnostic delay remains a leading source of preventable harm across healthcare systems. Yet, it is rarely modelled as the temporally ordered sequence of missed or deferred actions that it truly is. This conceptual systems paper reframes diagnostic delay as a sequence-detectable phenomenon and introduces a novel architectural response: the time-to-action sequence detection and mitigation architecture (TASDMA). TASDMA integrates clinical AI system architectures, EHR intelligence ecosystems, and real-time decision support pipelines into a single governance-ready infrastructure that continuously monitors care sequences, forecasts delay propagation, and triggers time-bounded actions before harm accrues. Drawing exclusively on peer-reviewed literature, the framework synthesises advances in healthcare analytics infrastructures, interoperability frameworks, and AI governance without empirical training or performance claims. Three interpretive equations formalise risk propagation, decision confidence decay, and governance load under sequence drift. The architecture is presented as a five-layer, closed-loop orchestration model with bidirectional feedback topology specifically engineered for deployment within existing EHR ecosystems. By shifting the analytic focus from static risk scores to dynamic sequence surveillance, TASDMA offers a theoretical foundation for next-generation clinical decision support that treats time itself as the primary therapeutic variable. The manuscript delineates the infrastructural, interoperability, and governance requirements for safe, equitable scaling across diverse care delivery environments.