Fine Tuned Private Model for Clinical Visit Summary Processing

The Documentation Bottleneck in Clinical Trials

• Time-Intensive, Manual Entry : Clinical Research Coordinators (CRCs) spend 8-15 minutes per visit manually writing and structuring summaries. This is valuable time taken away from patient care and other critical tasks.

• Inconsistent Data Quality : Manual summaries often vary in format, terminology, and completeness across different coordinators, sites, and studies, leading to downstream data cleaning challenges.

• Compliance & Protocol Risk : Inconsistent or incomplete documentation creates risks for protocol deviations and regulatory compliance, potentially jeopardizing study integrity.

Meet Smarter Clinical Summaries: An Al-Powered Solution a state-of-the-art, fine-tuned Small Language Model (SLM) designed to solve this exact challenge.

Meet Smarter Clinical Summaries: An Al-Powered Solution a state-of-the-art, fine-tuned Small Language Model (SLM) designed to solve this exact challenge.

• A fine-tuned Small Language Model (SLM) that converts raw, unstructured clinical visit notes into standardized, protocol-compliant 8-section visit summaries.
• Supports multiple therapeutic areas (Urology, Oncology, Cardiovascular, CNS, Metabolic/GLP-1) and adapts to different study designs and visit types.
• Can run securely on cloud or on-prem systems like NVIDIA DGX Spark, ensuring full data privacy and regulatory compliance.

Key Capabilities 

The generator is designed for precision and compliance across a wide range of studies:

1. Comprehensive Data Extraction and Pre-Processing

The SLM automatically identifies and extracts all critical data points from the raw notes, including:

• Participant ID, Phase, and Therapeutic Area (Urology, Oncology, CNS, Metabolic/GLP-1, etc.)
• Visit Type and Next Visit Details
• Symptoms, Safety Notes, and Compliance Data
• Laboratory Results, Procedures Performed, and Protocol Deviations

2. Fine-Tuned, Protocol-Aligned Summaries

Using the extracted data, the model generates consistent summaries that adhere strictly to the study protocol and standard operating procedures (SOPs). It can adapt to different study designs and visit complexities with a high degree of accuracy.

3. Template and Consistency Enforcement

The system includes robust guardrails to ensure documentation quality:

• No Missing Sections: The 8-section template is always complete.
• No Hallucinated Content: Content is constrained to the information present in the raw notes.
• Standardized Formatting: Ensures consistent terminology and structure across all CRCs, sites, and studies.

4. Privacy-Safe, On-Premise Deployment

Data privacy and regulatory compliance are paramount. The system can be deployed securely:

• On-Device Inference: It runs fully on powerful on-premise systems like NVIDIA DGX Spark (utilizing its 128GB unified memory) for zero-egress local inference.
• Regulatory Compliance: Full support for site-level compliance requirements (e.g., HIPAA, 21 CFR Part 11). Cloud deployment options (GCP/Azure) are also available.

The Outcomes: Measurable Impact

The practical application of this technology has yielded significant improvements in trial efficiency and quality:

• 70% of visit summaries generated with 100% accuracy (no CRC edits needed).
• Remaining summaries required minor terminology or formatting adjustments.
• Visit documentation time reduced from 8–15 minutes to under 1 minute.
• JSON outputs integrated cleanly into mock EDC/RTSM workflows.
• Produced highly consistent documentation across coordinators, sites, and studies.

Technical Deep Dive

• Model: Google Gemma 3 (a highly capable, fine-tuned Small Language Model).
• Training Data: 1000+ highly curated visit-summary pairs to ensure clinical domain expertise.
• Runtime: NVIDIA DGX Spark (for on-prem) or major cloud providers (GCP/Azure).
• Framework: A custom pre-processor + normalization pipeline ensures the raw data is clean and consistent before being fed to the model.
• Guardrails Layer: A layer dedicated to enforcing SOP-aware constraints, preventing hallucinations, and validating the final JSON output.

Conclusion

The Clinical Trial Visit Assistant provides a formal, reliable, and compliant approach to automating clinical visit documentation. By combining significant efficiency gains, strong first-draft accuracy, standardized outputs, and secure on-premise deployment on NVIDIA DGX Spark, the system addresses core operational challenges in clinical research.
Its design illustrates how targeted, specialized AI models can support regulated processes while maintaining precision, privacy, and compliance.