UNSTRUCTURED CLINICAL DATA / CASE STUDY
From Clinical Documents to Research-Ready Data
Turning complex clinical documentation into research-ready data required more than extraction.
It required understanding what the available information could actually support.
Unstructured clinical data
Heterogeneous source documents
Multicountry RWE
Different data environments
Data governance
Controlled processing and QC
The Challenge
Clinical information was available. But was it fit for research?
Routine clinical documentation captures valuable information about patient care, but it is rarely designed for secondary research.
The central challenge was determining whether heterogeneous clinical sources could provide sufficiently complete, interpretable and reliable information to support the research question.
This required evaluating data availability, source characteristics and operational feasibility before defining extraction and transformation workflows.
Data availability ≠ Data usability
The Approach
The data management approach was shaped by differences in clinical source systems, data structure, accessibility and local operational capabilities. These factors required an assessment of data suitability and processing constraints before selecting an appropriate operating model.
The objective was to accommodate heterogeneous data environments while maintaining consistent requirements for data quality, governance and fitness for research purposes
The diagram illustrates two general data management operating models relevant to heterogeneous clinical data environments. Project-specific procedures and configurations are intentionally omitted.
Research objectives & data requirements
Scientific purpose, source assessment and feasibility criteria
Governance & processing framework
Data protection, responsibilities and quality expectations
Centralised processing model
Source data acquisition
Central extraction & transformation
Data structuring & quality assessment
Site-led model
Local source assessment
Site-managed data preparation
Data delivery & quality assessment
Research-ready datasets
Quality-assessed data aligned with study requirements
Governance before data: establishing the conditions for secure processing
Before transferring or processing clinical documentation, relevant stakeholders, including the sponsor, Data Protection Officers (DPOs), and participating clinical sites, needed to establish appropriate governance arrangements.
These arrangements addressed data protection requirements, anonymisation responsibilities, secure transfer principles and access controls. Where source documentation contained identifiable patient information, appropriate safeguards were required throughout processing, independently of any subsequent anonymisation activities.
Agreed protocols
Anonymisation and operational responsibilities
Secure transfer
Controlled data exchange
Restricted access
Authorised personnel only
Adapting data management to different source environments
A shared research objective does not necessarily imply a uniform data management workflow. Clinical data sources may differ in structure, accessibility, documentation practices and local operational capabilities.
These differences influence where teams can extract, transform, and assess data quality, as well as how they should allocate responsibilities across participating stakeholders.
The data management approach therefore needs to accommodate heterogeneous source environments while maintaining consistent requirements for data quality, traceability and fitness for research purposes.
The operating model may vary. The quality expectations should not.
Turning clinical documents into structured data
Conceptual data transformation workflow
Heterogeneous clinical source data
Structured and unstructured information
Unstructured data
Information extraction and interpretation
Structured data
Mapping and harmonisation
Data transformation and standardisation
Consistent variables, formats and definitions
Quality assessment and validation
Completeness, consistency and source verification
Research-ready datasets
Data assessed against study requirements
Transforming heterogeneous clinical data into research-ready datasets requires extracting and interpreting relevant information, harmonising variables and preserving their clinical meaning. The specific techniques depend on the characteristics of the source data and the intended research objectives.
Throughout this process, traceability between source information and derived variables is essential to support quality assessment, validation and integration into the research data model.
TECHNICAL CAPABILITIES
Text processing · Data transformation · Quality assessment
From extraction to trustworthy data
Extracting information from clinical documents was only one part of the challenge. The resulting datasets still required the agreed anonymisation procedure, variable-level quality assessment and verification against the source documentation.
Descriptive statistics were used to examine individual variables and identify potential data quality issues. Randomly selected patient records were then checked against the corresponding source documents, followed by a second quality control review performed by another team member.
Only after these checks could the datasets be assessed for eligibility, completeness and suitability for the study requirements.
What Data Readiness Changes.
Same research objective. Different levels of data readiness.
SCENARIO A
Fragmented data
Higher interpretation and transformation effort
SCENARIO B
Structured data
Greater potential for direct research use
Data readiness begins at the source.
The structure, completeness and interpretability of clinical information influence the effort required to prepare data for research. Structured capture can reduce some transformation activities, but does not eliminate the need for quality assessment.
My Contribution
Turning complexity into a governed data process.
Assess
Source suitability and feasibility
Structure
Requirements and transformation
Govern
Data quality and coordination
Connecting clinical research requirements, heterogeneous data sources and quality expectations.
The Key Takeaway
Research-ready data is designed, not simply extracted.
Transforming clinical information into reliable research data requires more than technology. It depends on understanding the source, defining appropriate transformation processes, assessing data quality and preserving clinical meaning.
The value of data management lies in making information trustworthy and fit for its intended research purpose.
Project details have been intentionally generalised. Proprietary information, study-specific results and confidential procedures are not disclosed.