Research Summary
This study presents MORPHEUS, an edge-hosted large language model (LLM) pipeline for automated NICU discharge summary generation. Evaluated across 401 neonatal admissions using a clinician-defined rubric spanning 72 clinical fields, the system demonstrated higher accuracy, completeness, coherence, actionability, and factual consistency than clinician-authored summaries while preserving privacy through edge-based orchestration.
Objective
To evaluate an edge-hosted large language model framework capable of generating neonatal intensive care discharge summaries while preserving patient privacy.
Methods
MORPHEUS was evaluated using 401 NICU admissions. Performance was measured using a clinician-defined rubric covering 72 clinical documentation fields, including completeness, clarity, coherence, actionability, and factual accuracy.
Key Findings
• Higher completeness than clinician-authored summaries
• Improved documentation consistency
• Privacy-preserving edge deployment without cloud processing
• Demonstrated feasibility for real-world NICU workflows
Impact
This work demonstrates how edge AI can reduce clinician documentation burden while maintaining high-quality discharge summaries and protecting sensitive patient information.