LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines

Researchers developed an LLM-orchestrated framework that automates conformance checking in healthcare by extracting patient care pathways and clinical guidelines from unstructured text, eliminating the need for formal Computer-Interpretable Guidelines. Testing at Alessandria Hospital's neurological ward showed 86% of stroke care traces adhered to clinical guidelines, demonstrating practical feasibility of AI-driven healthcare compliance assessment.

This research addresses a critical gap in healthcare quality assurance: the inability to efficiently validate patient care against clinical guidelines when formal, machine-readable guideline representations don't exist. Conformance checking traditionally requires Computer-Interpretable Guidelines (CIGs), expensive to develop and rarely available in real-world hospital systems. By orchestrating multiple LLMs to process unstructured clinical documents—discharge letters, guideline texts—the framework automates what previously required manual review or expensive guideline formalization.

The healthcare industry has struggled with guideline adherence measurement for decades. Hospitals maintain extensive clinical documentation but lack automated systems to assess whether care pathways match best practices. This creates blind spots in quality assurance and compliance monitoring. The modular LLM architecture represents a shift toward leveraging natural language processing to bridge this gap, making conformance analysis accessible to institutions without specialized informatics resources.

For healthcare providers and hospital systems, this approach offers significant operational value. Automating conformance checking reduces manual audit burden, identifies care pathway deviations faster, and potentially improves patient outcomes through systematic adherence monitoring. The 86% conformance rate at Alessandria Hospital suggests hospitals may already be compliant but lack visibility into their own compliance levels. Vendors developing healthcare AI tools should recognize growing demand for LLM-based compliance solutions that work with existing documentation systems.

Future implementation will likely depend on LLM reliability in clinical contexts, validation across different medical domains beyond stroke care, and integration with hospital information systems. Regulatory frameworks may evolve to accept LLM-derived compliance assessments, potentially transforming how healthcare institutions monitor guideline adherence at scale.

• →LLM-orchestrated framework enables automated conformance checking without requiring pre-built Computer-Interpretable Guidelines, reducing implementation barriers
• →86% conformance rate at Alessandria Hospital demonstrates that LLMs can reliably extract and assess patient care pathways against clinical guidelines
• →This approach addresses a decades-old healthcare challenge by automating what previously required expensive guideline formalization or manual audits
• →The modular architecture could be adapted across medical specialties and hospital systems lacking specialized informatics infrastructure
• →Healthcare vendors now have a viable model for building AI-driven compliance tools that integrate directly with existing unstructured clinical documentation