LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines
arXiv:2606.09489v1 Announce Type: new Abstract: Objective: Conformance checking in healthcare seeks to assess whether patient care pathways adhere to clinical guidelines. However, its practical application often depends on the availability of formal, machine-interpretable representations of guidelines, such as Computer-Interpretable Guidelines (CIGs), which are seldom available in real-world clinical settings. Methods: This work introduces a modular framework based on the orchestration of Large Language Models (LLMs) to support medical conformance checking directly from unstructured clinical a
The rapid advancement of large language models makes their application to complex, unstructured data, such as medical guidelines, newly feasible for practical use in healthcare.
This development significantly lowers the barrier to entry for widespread adoption of conformance checking in healthcare, improving patient safety and care quality without requiring costly, formal guideline codification.
The reliance on explicitly encoded, computer-interpretable guidelines for automated conformance checking is reduced, opening up new pathways for AI application in real-world clinical environments.
- · AI-driven healthcare solution providers
- · Hospitals and clinics
- · Medical AI researchers
- · Patients
- · Developers of proprietary CIG platforms
- · Healthcare systems slow to adopt AI
Healthcare providers gain a new, accessible tool for ensuring protocol adherence and improving care quality.
The demand for specialized medical AI talent and integration services will increase, accelerating AI adoption across the healthcare sector.
This could lead to a broader regulatory push for AI-assisted clinical decision support tools and a re-evaluation of how medical guidelines are developed and disseminated.
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Read at arXiv cs.AI