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In the evolving landscape of healthcare analytics, the integration of artificial intelligence (AI) into clinical systems demands robust mechanisms to address inherent uncertainties in data quality. This conceptual manuscript introduces a novel design framework aimed at enhancing probabilistic reliability indices for clinical data, fostering uncertainty-aware analytics in healthcare environments. By synthesizing theoretical insights from clinical AI architectures, electronic health record (EHR) intelligence ecosystems, and decision support pipelines, we propose a structured approach that incorporates probabilistic modeling to quantify and mitigate data quality risks. The framework emphasizes interoperability frameworks and governance systems to ensure seamless integration into clinical workflows, without relying on empirical datasets or performance metrics. Key components include layered architectures for uncertainty propagation assessment, feedback loops for dynamic reliability adjustment, and interpretive formulas for decision confidence and risk management. This work highlights the theoretical implications for AI governance in healthcare, advocating for proactive uncertainty management to support reliable clinical decision-making. Through a synthesis of peer-reviewed literature, we delineate architectural principles that prioritize data quality assurance in probabilistic terms, offering a blueprint for future conceptual developments in uncertainty-aware healthcare systems. Ultimately, this framework seeks to bridge gaps in current analytics infrastructures by embedding reliability indices that adapt to clinical variabilities, promoting safer and more effective AI-driven healthcare analytics.