Search

The integration of large language models (LLMs) into clinical decision infrastructures represents a transformative shift in healthcare delivery, enabling enhanced reasoning, data synthesis, and adaptive support for clinicians. This conceptual manuscript proposes a novel architecture, termed the adaptive LLM-orchestrated clinical ecosystem (ALOCE), designed to seamlessly embed LLMs within existing electronic health record (EHR) systems, interoperability frameworks, and governance protocols. By delineating a multi-layered structure encompassing data ingestion, semantic processing, decision augmentation, and continuous monitoring, ALOCE addresses key challenges such as data silos, ethical AI deployment, and real-time adaptability in clinical environments. Drawing on theoretical foundations from AI governance and healthcare informatics, the architecture incorporates feedback topologies for drift detection and ethical alignment, ensuring robustness in diverse clinical workflows. Conceptual formulas are introduced to model risk propagation across layers, decision confidence thresholds, and governance load balancing, providing interpretive tools for system designers. The manuscript synthesizes recent literature on clinical AI architectures, highlighting interoperability standards like FHIR and the role of LLMs in augmenting human decision-making without empirical validation. Ultimately, this work outlines a blueprint for scalable, ethical LLM integration, fostering improved patient outcomes through intelligent infrastructure orchestration. While theoretical, the implications extend to policy, deployment strategies, and future research in AI-driven healthcare systems.

Clinicians often need rapid, evidence-based answers that integrate patient-specific electronic health records (EHRs) with clinical guidelines, but existing decision support tools are limited in real-time personalization. While large language models (LLMs) offer strong medical reasoning, they are prone to hallucinations and lack direct access to local EHR data, making them unsafe for standalone clinical use; meanwhile, traditional retrieval systems cannot synthesize coherent, context-aware responses. This paper proposes a retrieval-augmented generation (RAG) framework that combines dual-source retrieval from both institutional EHRs and clinical guideline databases. The system includes an EHR indexer, a guideline repository, a semantic retriever, an LLM-based generator, and a safety filter for hallucination mitigation. By grounding outputs in retrieved patient data and evidence-based recommendations, the model improves factual reliability, explainability, and clinical trustworthiness. Overall, the framework enables safe, real-time clinical question answering by integrating LLM reasoning with verified medical sources, with future validation planned on public EHR and guideline datasets.

Hospital discharge summaries are critical for care transitions, directly impacting readmission prevention and medication reconciliation, yet physicians spend 15-30 minutes per patient drafting these documents, contributing substantially to documentation burden and professional burnout. Manual summarization of daily progress notes and laboratory results is repetitive, time-consuming, and error-prone, as clinicians must sift through lengthy unstructured notes across multiple hospital days while identifying salient events and trends. We propose a large language model with parameter-efficient fine-tuning for automated discharge summary generation that processes chronologically ordered daily progress notes alongside time-series laboratory results to produce structured discharge documentation. The framework consists of a base LLM augmented with LoRA adapters, a progress note encoder for section segmentation, a laboratory result integrator that computes trend indicators, and a summary generator that produces sectioned discharge output. Parameter-efficient fine-tuning enables domain adaptation to clinical text with minimal computational resources, preserving patient-specific information while reducing hallucination through retrieval of key factual details from the input notes. This framework offers a practical pathway to reduced documentation burden and improved discharge quality, with potential for widespread deployment across health systems given the modest computational requirements of PEFT approaches.

This article proposes a conceptual framework for a diagnostic support system in emergency departments that leverages large language models, retrieval-augmented generation, and chain-of-thought reasoning. By combining triage notes and vital signs, the system generates a ranked differential diagnosis list to assist clinicians without replacing their judgment. The framework includes components like a triage note encoder, a vital sign encoder, a retrieval module, and a diagnosis ranker, using evidence from clinical guidelines, curated references, and de-identified prior cases. The approach grounds the model in authoritative knowledge while ensuring transparency and explainability in the diagnostic process. However, prospective validation, integration into workflows, and clinician oversight are crucial before implementation to ensure safety and effectiveness.