Compliance-Aware Predictive Computing for Healthcare AI: Anticipating Common Medical Information Needs Under Privacy, Safety, and Regulatory Constraints

The increasing integration of generative artificial intelligence (AI) into healthcare information services has created new opportunities for improving information accessibility, supporting patient education, and reducing repetitive computational expenditure. Healthcare users frequently request overlapping categories of information concerning common diseases, preventive healthcare, medication safety, vaccination, medical examinations, chronic disease education, and healthcare service procedures. Although these information requirements may exhibit temporal, seasonal, demographic, and event-related patterns, conventional AI systems generally initiate computational processing only after individual requests arrive. Predictive computing provides an opportunity to anticipate recurring information demand and selectively prepare relevant computational resources before corresponding requests materialize. However, healthcare introduces additional requirements that distinguish medical information services from conventional generative AI applications, particularly patient privacy, information reliability, clinical safety, temporal validity, professional oversight, and regulatory compliance. This paper introduces Compliance-Aware Predictive Computing for Healthcare AI (CAPC-Health), a conceptual framework that extends Predictive Computing for Generative AI (PCGA), Predictive Computational Orchestration for Generative AI (PCO-GAI), and Economic-Aware Predictive Computing for Generative AI (EAPC-GAI) into healthcare information environments. The proposed framework investigates whether commonly recurring medical information needs can be predicted using appropriately authorized, privacy-preserving, and temporally available information signals. It integrates six principal functions: healthcare information demand forecasting, medical question generation, evidence-grounded information preparation, privacy-aware computational resource management, risk-sensitive response reuse, and adaptive healthcare inference orchestration. Unlike general-purpose predictive response preparation, CAPC-Health introduces a compliance-aware preparation boundary that distinguishes reusable general medical education from individualized clinical interpretation. The framework permits advance preparation of validated educational information, general preventive healthcare explanations, healthcare navigation resources, and other appropriately classified information. It does not assume that patient-specific diagnosis, treatment recommendations, medication decisions, or emergency assessments can be safely precomputed or reused without current clinical context and appropriate professional oversight. A unified optimization formulation is developed to evaluate the expected computational benefits of predictive preparation while accounting for prediction uncertainty, evidence quality, privacy risk, clinical relevance, information freshness, validation expenditure, storage requirements, and regulatory constraints. The framework also establishes a healthcare-specific experimental methodology incorporating demand prediction accuracy, computational savings, response reliability, privacy protection, safety violations, and service-level performance. The proposed research does not claim that predictive healthcare computation is inherently clinically beneficial or legally compliant across jurisdictions. Rather, it establishes a conceptual and experimental foundation for investigating whether common healthcare information requirements can be anticipated and computationally prepared without compromising essential privacy, safety, and governance requirements.