Case Study: Model Context Protocol (MCP) Repairs – Automating Clinical Trial Data Reconciliation with GDPR-Compliant Anonymization

Project Overview

The Model Context Protocol (MCP) Repairs project was designed to streamline clinical trial data reconciliation by automating the alignment of Electronic Health Records (EHR) with trial protocols while ensuring GDPR compliance. Clinical trials generate vast amounts of patient data, often stored in disparate EHR systems, leading to inefficiencies, errors, and compliance risks during data extraction and reconciliation.

This initiative introduced Protocol-Managed EHR Bridges—automated pipelines that harmonize EHR data with trial protocols—and GDPR-compliant anonymization tools to protect patient privacy. The solution reduced manual reconciliation efforts, improved data accuracy, and ensured regulatory adherence, making clinical trials faster and more reliable.

Challenges

1. Data Fragmentation & Inconsistencies – Clinical trial data is stored across multiple EHR systems with varying formats, leading to mismatches with trial protocols.
2. Manual Reconciliation Bottlenecks – Traditional reconciliation processes are labor-intensive, error-prone, and slow down trial timelines.
3. GDPR & HIPAA Compliance Risks – Patient data must be anonymized before analysis, but manual de-identification is unreliable and time-consuming.
4. Lack of Real-Time Data Syncing – Delays in data reconciliation hinder timely decision-making in trials.
5. Scalability Issues – As trials expand, manual processes become unsustainable, increasing costs and risks.

Solution

The MCP Repairs project introduced a two-pronged approach:

1. Protocol-Managed EHR Bridges

• Automated Data Mapping – AI-driven pipelines extracted EHR data and aligned it with trial protocols, reducing manual intervention.
• Standardized Data Formats – Unified data models ensured consistency across different EHR systems.
• Real-Time Reconciliation – Continuous synchronization between EHRs and trial databases minimized delays.

2. GDPR-Compliant Anonymization Tools

• AI-Powered De-Identification – Natural Language Processing (NLP) automatically redacted PHI (Protected Health Information) from unstructured EHR notes.
• Pseudonymization & Tokenization – Replaced identifiable data with secure tokens, enabling traceability without exposing patient details.
• Audit Trails & Compliance Reporting – Automated logs documented anonymization steps for regulatory audits.

Tech Stack

• Data Integration: FHIR APIs, HL7, OMOP CDM
• AI/ML: NLP (BERT, spaCy), rule-based anonymization models
• Cloud Infrastructure: AWS (S3, Lambda, Glue), Azure Synapse
• Database: PostgreSQL, Snowflake (for structured EHR data)
• Security: AES-256 encryption, Zero-Trust Architecture
• Compliance Tools: GDPR/ HIPAA validation frameworks, Privitar for data masking

Results

• 90% Reduction in Manual Reconciliation – Automated EHR bridges cut processing time from weeks to hours.
• 99.5% Data Accuracy – AI-driven mapping minimized protocol-EHR mismatches.
• Full GDPR Compliance – No breaches or compliance violations reported post-implementation.
• 30% Faster Trial Timelines – Real-time syncing accelerated interim analyses.
• Scalable for Multi-Site Trials – Successfully deployed across 50+ clinical sites.

Key Takeaways

1. Automation is Critical for Efficiency – AI-driven reconciliation eliminates bottlenecks in clinical trials.
2. Regulatory Compliance Must Be Baked In – GDPR-compliant tools should be integrated early in data pipelines.
3. Interoperability Drives Success – Standardized data models (FHIR, OMOP) ensure seamless EHR integration.
4. Real-Time Data Sync Enhances Decision-Making – Faster reconciliation improves trial agility.
5. Scalability Requires Cloud & AI – Cloud infrastructure and machine learning enable large-scale deployments.

The MCP Repairs project demonstrates how automation, AI, and strict compliance protocols can transform clinical trial data management—reducing costs, accelerating research, and safeguarding patient privacy.