CLINICAL DATA STANDARDIZATION / CASE STUDY
From Fragmented to Comparable Real-World Data
Using the same clinical software did not guarantee consistent data. Local configurations, free-text entries and multilingual terminology had to be harmonised before meaningful real-world comparisons could be made.
Multicountry RWE
Heterogeneous clinical data
Data Harmonisation
Multilingual standardisation
Data Integrity
Unique identifiers and QC
The Challenge
A multicountry Real-World Evidence (RWE) study aimed to assess whether findings from controlled clinical trials were reflected in routine clinical practice.
Although participating centres used the same clinical information system, local customisations created substantial data heterogeneity. Differences in field configurations, uncontrolled free-text entries, multilingual terminology and centre-specific clinical interpretations made direct comparisons unreliable.
An additional challenge concerned record integrity: identifiers generated independently by different system installations were not necessarily unique when datasets were combined.
The central challenge was not simply to aggregate data from multiple sources, but to make those data consistent, comparable and reliable without losing their original clinical meaning.
Same system ≠ Comparable data
The Approach
The data standardisation approach was shaped by differences in local configurations, multilingual terminology and inconsistent source data structures. These conditions required a harmonisation strategy capable of preserving meaning while making records comparable across sites.
The objective was to transform heterogeneous clinical data into a research-ready dataset without losing semantic consistency, record integrity or data quality.
The diagram illustrates the main stages of the data harmonisation workflow, from heterogeneous clinical datasets to comparable, research-ready data.
Project-specific configurations and operational details are intentionally omitted.
Pseudonymised source datasets
Multicentre clinical data with heterogeneous configurations and terminology
R-Based Data Transformation
Data restructuring, parsing and mapping to the target data model
Semantic & Linguistic Harmonisation
Free-text categorisation, multilingual terminology mapping and expert-guided interpretation
Data Integrity & Quality Control
Unique record identifiers, descriptive statistical checks and source-to-target data reconciliation
EDC integration
Quality-reviewed, harmonised clinical data structured and loaded into the final study database
The Outcome
BEFORE
Fragmented Clinical Data
Inconsistent terminology, local configurations and non-unique record identifiers.
AFTER
Comparable Real-World Data
Harmonised terminology, unique record identifiers and quality-assessed data ready for meaningful analysis.
The resulting dataset supported the evaluation of real-world clinical practice against existing trial evidence, with findings broadly consistent with those previously reported in controlled studies.
Data standardisation made meaningful real-world comparisons possible.
The Lesson Learned
Data standardisation is not simply a technical exercise.
Different clinical environments may use the same software while assigning different meanings to apparently identical data.
The most important lesson was the need to involve clinical experts in resolving semantic ambiguities, rather than relying exclusively on automated transformations.
Technical consistency does not guarantee clinical comparability.