Introduction
Structured medical data now occupies a central place in the digital transformation of the healthcare sector. Patient records, test results, electronic prescriptions, data from connected devices: the volume of information is exploding. The ability to structure this data drives quality of care, organisational efficiency and the development of artificial intelligence in health.
In this article we explain what structured medical data is, how it differs from unstructured data, its benefits, main use cases and the challenges involved in implementing it.
What is structured medical data?
Structured medical data is clinical information organised in a predefined, standardised format that can be read automatically by IT systems.
It is typically stored in:
Examples of structured medical data
By contrast, unstructured data includes free-text reports, medical images or handwritten notes.
Structured vs unstructured data: what’s the difference?
| Criterion | Structured data | Unstructured data |
|---|---|---|
| Format | Standardised | Free-form |
| Machine-readable | Yes | Complex |
| AI exploitation | Straightforward | Requires NLP/annotation |
| Examples | ICD-10 codes, lab results | PDFs, free text, images |
| Analysis quality | High | Variable |
Modern systems often aim for a hybrid approach: structuring a growing share of clinical data while leveraging unstructured data through NLP and semantic annotation.
Why structure medical data?
Structuring health data addresses several strategic issues.
1. Improving quality of care
It enables:
2. Clinical and epidemiological research
Structured data facilitates:
3. Developing AI in healthcare
Algorithms require:
4. Optimising hospital processes
How to structure medical data?
Structuring medical data relies on interoperability standards and software solutions that can leverage existing data.
Using interoperability standards
To normalise and exchange medical data, the most widely used standards are:
These reference frameworks ensure automatic, secure reading of data and facilitate care continuity across different organisations.
MAGE-X: structuring existing data with AI
MAGE-X is a SaaS platform specialised in structuring medical data already present in clinical records. It enables physicians and healthcare organisations to turn their unstructured information into reliable, standardised, actionable clinical data without changing existing professional practices.
Using artificial intelligence, MAGE-X:
- automates extraction and structuring of information from medical records,
- standardises data for use in clinical analysis and research,
- supports medical decision-making with relevant, real-time insights,
- improves patient follow-up and leverages clinical experience at organisation level,
- meets regulatory and data security requirements (GDPR, CNIL, HDS).
MAGE-X is therefore a lever to industrialise data structuring and build reliable foundations for AI, clinical research and improved hospital processes.
Use cases for structured medical data
Structured medical data is now used for:
Security and compliance of structured medical data
Health data is highly sensitive and regulated. Organisations must comply in particular with:
Structuring precisely facilitates access control, audits, data minimisation and cybersecurity oversight.
Limitations and challenges
Despite their benefits, structured medical data presents certain challenges:
The main challenge is to combine structuring with usability so as not to add to the burden on clinical practice.
Conclusion
Structured medical data is a key lever for modernising health systems. It improves quality of care, accelerates research and enables the large-scale use of medical artificial intelligence. Its deployment must however incorporate strong requirements in terms of ethics, privacy protection and interoperability.