Software to structure medical data: the MAGE-X solution for turning patient records into actionable data

Introduction

Healthcare organisations accumulate massive volumes of information every day: reports, discharge letters, imaging, lab results, clinical observations. The problem is simple: most of this information is unstructured.

Result:

• medical data that is hard to exploit
• time lost for care teams
• poor reuse in clinical research
• major obstacles to artificial intelligence projects

Software to structure medical data therefore becomes essential to turn this information into a strategic asset.

That is exactly MAGE-X’s mission: to automatically structure existing medical data without changing the clinical tools already in place.

What do we mean by structuring medical data?

Structuring means transforming:

• free text
• PDFs
• reports
• scanned documents
• medical correspondence
• clinical annotations

into organised, standardised data, for example:

• coded diagnoses
• harmonised patient information
• lab results recognised by LOINC
• dated clinical events
• procedures aligned with CCAM
• concepts standardised with SNOMED CT

Structuring then enables:

• simple querying
• reliable statistics
• activity management
• automatic cohort building
• AI model training

Current limits: a mass of unstructured data

Today:

• more than 70% of clinical data is unstructured
• a large share is stored in PDF
• access depends on human reading
• finding information takes time

The consequences are direct:

• risk of errors or inconsistencies
• duplicate tests
• poor traceability
• underuse of the clinical knowledge produced

Hospital AI and data projects then hit a wall: no structured data = no possible valorisation.

Main MAGE-X features

• automatic structuring of unstructured medical data
• semantic recognition of medical concepts
• classification by pathology, clinical event or specialty
• alignment with standard terminologies (ICD-10, CCAM, LOINC, SNOMED CT)
• pseudonymisation / anonymisation according to use case
• export to data warehouse, EDS, BI tool or AI platform

Software to structure medical data: concrete use cases

1. Structuring existing patient records

• no need to re-enter data
• no need to change your EPR
• immediate valorisation of histories

2. Building cohorts for clinical research

• automatic identification of inclusion/exclusion criteria
• multivariate extraction
• reproducible queries

3. Feeding health data warehouses

• better data quality
• less manual cleaning
• built-in normalisation

4. AI and machine learning in healthcare

• creation of training datasets
• reduced bias
• traceability of transformations

Why choose MAGE-X?

MAGE-X offers key advantages:

• technology already operational
• rapid implementation
• ongoing maintenance
• built-in regulatory compliance
• embedded medical expertise

While avoiding:

• high development costs
• dependence on scarce, overloaded internal teams
• future technical debt

Security, compliance and regulatory framework

Structuring medical data involves strict requirements. MAGE-X operates in a compliant framework:

• GDPR
• CNIL
• certified health data hosting (HDS)
• reference methodologies such as MR-004

The platform includes:

• access logging
• processing traceability
• authorisation management
• anonymisation or pseudonymisation as required

Conclusion

If you are looking for software to structure medical data, your challenge is twofold: valorise what you have and prepare for the future (AI, research, management).

MAGE-X addresses this challenge by turning masses of heterogeneous information into reliable, standardised, actionable clinical data.

Structuring is no longer a theoretical project: with MAGE-X, it becomes operational, automated and secure.

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