Clinical Report: Evaluation of Financial and Access Framing in Pediatric Asthma Referral Plans

Overview

This study evaluates the variability in financial-access and geographic-access language in pediatric asthma referral plans generated by large language models (LLMs). Findings indicate that certain demographic signals influence the framing of these plans.

Background

Large language models are increasingly utilized in clinical settings for documentation and referral support. Understanding how these models frame access and financial considerations is essential for ensuring equitable healthcare delivery.

Data Highlights

Key Findings

• LLM-generated referral plans varied significantly in financial-access and geographic-access language.
• The DeShawn name signal was associated with a higher financial-access term rate.
• The bundled geographic-access signal also increased financial-access term rates.
• Institutional Specificity and Triage Ranking were consistently high across models.
• Human validation indicated moderate reliability in endpoint-specific assessments.

Clinical Implications

Clinicians should be aware of potential biases in LLM-generated content regarding financial and access-related language.

Conclusion

The findings highlight the importance of assessing both biomedical content and structural framing in LLM-generated clinical documents.

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4. Frontiers in Pediatrics, 2026 -- Development and internal validation of a multidimensional nomogram integrating PIV, LDH, and FeNO for predicting poor asthma control in school-aged children
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