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A Hybrid Knowledge-Grounded Framework for Safety and Traceability in Prescription Verification

arXiv:2603.10891v1 Announce Type: new Abstract: Medication errors pose a significant threat to patient safety, making pharmacist verification (PV) a critical, yet heavily burdened, final safeguard. The direct application of Large Language Models (LLMs) to this zero-tolerance domain is untenable due to their inherent factual unreliability, lack of traceability, and weakness in complex reasoning. To address these challenges, we introduce PharmGraph-Auditor, a novel system designed for safe and evidence-grounded prescription auditing. The core of our system is a trustworthy Hybrid Pharmaceutical Knowledge Base (HPKB), implemented under the Virtual Knowledge Graph (VKG) paradigm. This architecture strategically unifies a relational component for set constraint satisfaction and a graph component for topological reasoning via a rigorous mapping layer. To construct this HPKB, we propose the Iterative Schema Refinement (ISR) algorithm, a framework that enables the co-evolution of both graph a

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Yichi Zhu, Kan Ling, Xu Liu, Hengrun Zhang, Huiqun Yu, Guisheng Fan · · 1 min read · 28 views
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arXiv:2603.10891v1 Announce Type: new Abstract: Medication errors pose a significant threat to patient safety, making pharmacist verification (PV) a critical, yet heavily burdened, final safeguard. The direct application of Large Language Models (LLMs) to this zero-tolerance domain is untenable due to their inherent factual unreliability, lack of traceability, and weakness in complex reasoning. To address these challenges, we introduce PharmGraph-Auditor, a novel system designed for safe and evidence-grounded prescription auditing. The core of our system is a trustworthy Hybrid Pharmaceutical Knowledge Base (HPKB), implemented under the Virtual Knowledge Graph (VKG) paradigm. This architecture strategically unifies a relational component for set constraint satisfaction and a graph component for topological reasoning via a rigorous mapping layer. To construct this HPKB, we propose the Iterative Schema Refinement (ISR) algorithm, a framework that enables the co-evolution of both graph and relational schemas from medical texts. For auditing, we introduce the KB-grounded Chain of Verification (CoV), a new reasoning paradigm that transforms the LLM from an unreliable generator into a transparent reasoning engine. CoV decomposes the audit task into a sequence of verifiable queries against the HPKB, generating hybrid query plans to retrieve evidence from the most appropriate data store. Experimental results demonstrate robust knowledge extraction capabilities and show promises of using PharmGraph-Auditor to enable pharmacists to achieve safer and faster prescription verification.

Executive Summary

This study presents a novel framework, PharmGraph-Auditor, for safe and evidence-grounded prescription auditing. The framework leverages a Hybrid Pharmaceutical Knowledge Base (HPKB) and a Chain of Verification (CoV) paradigm to address challenges in pharmacist verification. By integrating Large Language Models (LLMs) with a trustworthy knowledge base, the system enhances the accuracy and transparency of prescription verification. Experimental results demonstrate the effectiveness of the proposed framework in extracting knowledge from medical texts and improving the efficiency of the auditing process. PharmGraph-Auditor has the potential to revolutionize pharmacist verification by ensuring safer and faster prescription verification.

Key Points

  • ▸ PharmGraph-Auditor is a novel framework for safe and evidence-grounded prescription auditing.
  • ▸ The framework integrates Large Language Models (LLMs) with a Hybrid Pharmaceutical Knowledge Base (HPKB) and a Chain of Verification (CoV) paradigm.
  • ▸ Experimental results demonstrate the effectiveness of the proposed framework in extracting knowledge from medical texts and improving the efficiency of the auditing process.

Merits

Strength in Knowledge Representation

The Hybrid Pharmaceutical Knowledge Base (HPKB) and Virtual Knowledge Graph (VKG) paradigm provide a robust and trustworthy representation of pharmaceutical knowledge, addressing the limitations of existing systems.

Improved Transparency and Accountability

The Chain of Verification (CoV) paradigm ensures that the auditing process is transparent and verifiable, enabling pharmacists to provide evidence-based answers to patients.

Demerits

Limited Domain Adaptation

The Iterative Schema Refinement (ISR) algorithm may struggle to adapt to nuances in different domains or jurisdictions, potentially limiting the framework's applicability.

Scalability Concerns

The proposed framework's reliance on a Hybrid Pharmaceutical Knowledge Base (HPKB) and a Chain of Verification (CoV) paradigm may lead to scalability challenges as the volume of prescriptions increases.

Expert Commentary

PharmGraph-Auditor represents a significant advancement in the field of pharmacist verification. By leveraging a Hybrid Pharmaceutical Knowledge Base (HPKB) and a Chain of Verification (CoV) paradigm, the framework addresses the limitations of existing systems and provides a robust and trustworthy representation of pharmaceutical knowledge. The proposed framework's potential to improve the accuracy and efficiency of prescription verification has significant implications for reducing medication errors and improving patient safety. However, the study's reliance on a Hybrid Pharmaceutical Knowledge Base (HPKB) and a Chain of Verification (CoV) paradigm may lead to scalability challenges as the volume of prescriptions increases.

Recommendations

  • ✓ Future research should focus on developing more sophisticated knowledge representation models that can adapt to nuances in different domains or jurisdictions.
  • ✓ The proposed framework should be evaluated in larger-scale studies to assess its scalability and feasibility in real-world settings.

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