Automated Medical Report Validation System
A production-ready backend system that automatically validates medical reports against clinical protocols to reduce diagnostic and prescription errors.
Challenge
Healthcare providers face structural challenges:
- High cognitive load when reviewing diagnostic and treatment decisions
- Complex and evolving clinical protocols
- Manual verification is time-consuming and inconsistent
- Lack of automated quality control in existing systems
The solution had to:
- work with real medical reports
- provide clear, structured feedback
- integrate via API
- respect data privacy and security requirements
Solution
LAB325 designed and delivered a microservice-based backend system that automatically validates medical reports against disease-specific clinical protocols.
The system:
- receives medical reports via REST API
- analyzes diagnoses and prescriptions
- detects inconsistencies, omissions, and protocol deviations
- returns structured validation results in JSON format
The service acts as an assistive quality-control layer, supporting clinicians without replacing clinical judgment.
How the System Works
- A medical report is submitted through the API
- The system extracts diagnostic and treatment information
- Data is checked against relevant clinical protocols
- Errors and deviations are identified
- A structured validation report is returned
Each request is processed within a defined time window suitable for clinical use.
Clinical Protocol Engine
- Disease-specific clinical protocols stored in a dedicated database
- Initial support for individual disease categories, designed to scale to 50+
- Protocol versioning to track updates and changes
- Protocols can be updated independently of system logic
This ensures long-term relevance as medical guidelines evolve.
AI-Assisted Analysis
The system uses large language models (LLMs) to support analysis of medical text, including:
- understanding unstructured or semi-structured reports
- detecting missing diagnostic steps
- identifying incorrect or incomplete prescriptions
AI is used as a controlled assistive mechanism, embedded within a rule- and protocol-driven system, ensuring transparency and auditability.
The result
The delivered system enabled:
- Automated detection of diagnostic and prescription errors
- Reduced risk of protocol deviations
- Faster review of medical reports
- A scalable quality-control layer for healthcare systems
The solution was delivered as a production-ready backend service, designed for integration into existing medical platforms.
Technologies
Architecture
REST API, Microservice-based
Back-end
Python (FastAPI / Django)
Database
PostgreSQL
Data Format
JSON
AI / NLP Layer
LLM-based text analysis (e.g. LLaMA, custom NLP models)
Security
HTTPS, API authentication, logging
Project team:
DevOps Engineer
ML-Engineer
Project Duration:
Completed in 3 months
CONTACT US
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