Development of a hybrid artificial intelligence framework for pharmacotherapy optimization

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Abstract Introduction Pharmacotherapy optimization in multimorbid patients is increasingly complex due to polypharmacy, fragmented data, expanding electronic health records, and workforce constraints. Conventional clinical decision support systems remain largely rule-based and often fail to adequately incorporate patient-specific context. While artificial intelligence offers new opportunities, stand-alone models remain insufficiently reliable for high-risk pharmacotherapy decision support. Aim To develop a relevance-driven, clinician-supervised hybrid AI framework for pharmacotherapy optimization. Method Using a design science-informed approach, an interdisciplinary research group developed a conceptual framework for AI-supported pharmacotherapy optimization. Framework development was informed by prior feasibility work, published literature, clinical practice requirements, and iterative interdisciplinary discussions. Hybrid AI was defined as the combination of retrieval-augmented generation, deterministic safety rules, and large language model reasoning. Results Seven design principles were identified, including decomposition of clinical activities, relevance-based prioritization, hybrid reasoning under clinician oversight, integration of patient goals, transparency of evidence sources, longitudinal optimization within a governed closed loop, and evaluation as a design requirement. These principles informed a conceptual architecture integrating structured clinical data, patient preferences, longitudinal patient information, and evidence retrieval within a clinician-governed decision-support framework. Conclusion The proposed framework conceptualizes AI as a relevance-structuring, clinician-governed decision-support layer rather than an autonomous decision-maker. By combining hybrid reasoning, patient-specific context, and professional oversight, it provides a conceptual foundation for future development, implementation, and evaluation of AI-supported pharmacotherapy systems.