The digital transformation of the healthcare sector rests on a fundamental pillar: the ability to effectively exploit medical data and, even more so, existing medical data. Yet despite the widespread use of computerised patient records, a large share of health information remains unusable for analysis and decision-making. Structuring medical data is now a major challenge for healthcare organisations, researchers and medical solution providers.
What is structured medical data?
Structured medical data is health information organised in a standardised, coded format that enables automated processing and use by IT systems. Unlike free text entered in a report, structured data follows a precise nomenclature that facilitates storage, search and analysis.
Three levels of data structuring
Clinical data falls into three distinct categories:
Structured data
It follows a rigid, standardised model. It is organised in predefined fields with coded values: date of birth, weight, blood pressure, ICD-10 diagnostic codes, CCAM procedures. This data can be used directly in relational databases.
Semi-structured data
It has partial organisation, generally in the form of tags or markers. An operative report with identified sections (anaesthesia, surgical procedure, complications) belongs to this category. The content remains free text, but minimal structure allows certain information to be isolated.
Unstructured data
It corresponds to free text without formal organisation: written clinical observations, discharge letters, comments in patient records. It represents the majority of health data produced daily and remains difficult to exploit without specific processing.
Concrete examples of structured medical data
In hospital practice, several types of information lend themselves naturally to structuring:
The challenges of structured medical data
Structuring medical data is not merely a technical or administrative matter. It directly conditions the ability of health systems to improve quality of care, advance research and optimise their operations.
Improving quality and safety of care
Structured clinical data enables the implementation of automatic alerts during prescription: drug interactions, documented allergies, contraindications according to known pathologies. These clinical decision support systems significantly reduce the risk of therapeutic errors, particularly in emergency situations where there is no time to analyse the full patient record.
Continuity of care also benefits from easily accessible structured data. During transfers between departments or organisations, essential elements (medical history, ongoing treatments, latest lab results) can be identified instantly, even without prior knowledge of the patient.
Accelerating clinical research
Identifying cohorts is a major challenge for clinical studies. With structured health data, researchers can instantly query vast databases to identify patients meeting precise criteria: given pathology, age range, previous treatments, biological criteria. What used to require weeks of manual record review can now be done in a few hours.
Practice analysis and therapeutic effectiveness evaluation also rely on structured data. Real-world evidence (RWE) studies use routine data to complement controlled clinical trials, offering a more complete picture of treatment effects in real-world conditions.
Management and performance of healthcare organisations
Hospital management relies on quality and performance indicators that require reliable structured data. Monitoring average lengths of stay, readmission rates, completeness of diagnostic coding or drug consumption depends on the ability to automatically extract this information.
Activity-based funding (T2A) requires rigorous documentation of procedures and diagnoses. Structured clinical data facilitates PMSI coding and limits revenue losses due to incomplete or imprecise coding.
Interoperability and information sharing
The development of coordinated care pathways and territorial platforms requires information systems to communicate effectively. Interoperability standards such as HL7 FHIR rely on structured medical data that can be exchanged between heterogeneous applications.
This interoperability goes beyond simple document sharing: it enables the aggregation of data from multiple sources (hospital, primary care, laboratories, imaging) to build a unified view of the patient journey.
How to structure medical data today?
Faced with the complexity of structuring medical data, dedicated solutions are emerging to meet the specific needs of healthcare organisations.
MAGE-X: structuring existing medical data without changing practice
MAGE-X is an innovative platform that uses artificial intelligence to automatically transform existing medical texts into exploitable structured clinical data, without changing how physicians work.
The solution stands out for its ability to process large volumes of existing medical documents (hospitalisation reports, consultation letters, test results) and automatically extract relevant information: diagnoses, treatments, medical history, complications, etc.
MAGE-X’s approach addresses a major challenge: enabling the valorisation of the data assets accumulated in patient records without requiring changes in physician practice. Organisations thus have a pragmatic path to accelerate their digital transformation and exploit their structured health data for research, management and continuous improvement of care.
The platform also integrates the regulatory requirements specific to the healthcare sector, in particular in terms of security, HDS-certified hosting, GDPR compliance and ISO 27001. This attention to the legal framework enables organisations to adopt these innovative technologies with confidence.
Conclusion: towards data-driven medicine
The transition to systematic exploitation of structured medical data represents much more than a technological evolution. It constitutes a paradigm shift in how care, research and health system organisation are conceived.
Organisations that succeed in this transformation generally combine three factors: performant and ergonomic technologies, change management involving healthcare professionals, and clear data governance ensuring quality and regulatory compliance.
The coming years will likely see the generalisation of hybrid solutions, combining targeted structured entry for essential elements and automated extraction for the rest. The objective remains constant: unlock the value contained in health data to improve care while scrupulously respecting patient confidentiality and rights.
Emerging solutions like MAGE-X illustrate this evolution by offering an AI-powered solution for structuring existing data with 95% accuracy. The future of digital health will be built on these foundations: quality structured health data that is accessible, secure and truly at the service of professionals.