Clinical Data Model Solutions — Solution Consulting, PoCs and Implementation | Care2Data
Engagement 01
Solution Consulting
Many clinical data challenges originate long before submission. Traditional consulting often accelerates system development. Care2Data focuses first on structural clarity.
Capabilities
This enables organisations to clearly define:
What matters most for regulatory submission
What must remain traceable across systems
What must be defensible during regulatory review
How clinical data flows across EDC, lab, safety, and transformation pipelines
Which structural assumptions are aligned with regulatory expectations
Methodology
How We Execute
Knowledge Modelling
Design semantic models that formalise clinical entities, relationships, and regulatory context across datasets and workflows.
Repository Architecture Design
Architect scalable, governed knowledge infrastructure to serve as the enterprise source of truth for clinical data systems.
Clinical Discovery Framework Design
Build frameworks enabling context-aware search, discovery, and reasoning across structured and unstructured clinical datasets.
Governance & Lifecycle Design
Define governance structures, lifecycle controls, and version management aligned with regulatory expectations.
Consider a clinical program approaching submission, where datasets across SDTM, analysis outputs, and supporting documentation have been developed independently.
- Traceability gaps between SDTM and ADaM datasets
- Unclear alignment between validation rules and study intent
- Ambiguity around what qualifies as "submission-ready" data
- Analyse data flows across systems and transformation pipelines
- Establish semantic models to define relationships across datasets
- Align validation and verification logic with regulatory expectations
- Define clear, governed criteria for submission readiness
This transforms a fragmented data landscape into a coherent, defensible framework — enabling teams to approach submission with clarity, consistency, and confidence.
Engagement 02
Proof of Concepts (PoCs)
Most Proof of Concepts demonstrate features. Care2Data PoCs evaluate structural resilience.
Our PoCs are not designed to prove success. They are designed to identify structural weaknesses early — when they are still manageable.
Capabilities
Using real clinical datasets, we validate:
Whether validation logic performs under real data variability
Whether transformations scale while maintaining traceability
Whether lineage remains inspection-ready
Whether semantic gaps introduce hidden risk
Whether cross-domain reasoning surfaces structural inconsistencies early
Methodology
How We Execute
Real Dataset Testing
Apply validation and verification logic on real clinical datasets using Kwalify™'s intelligence layer — exposing hidden structural weaknesses.
Structural Assumption Validation
Simulate variability across subjects, domains, and study conditions to stress-test assumptions under realistic operating conditions.
Traceability & Lineage Evaluation
Evaluate end-to-end lineage across transformation pipelines — ensuring inspection-ready traceability at every stage.
Semantic Gap Analysis
Identify semantic gaps through semantic reasoning and anomaly detection — moving beyond rule execution to contextual analysis.
Consider a clinical program where validation rules and data transformations have been developed and appear to function correctly in controlled environments.
- Inconsistencies emerge across domains and timepoints
- Traceability between source data and derived outputs becomes unclear
- Validation rules fail to capture contextual dependencies
- Lineage breaks under scale and cross-study comparisons
- Apply validation and verification logic on real clinical datasets using Kwalify™'s intelligence layer
- Simulate variability across subjects, domains, and study conditions
- Evaluate traceability and lineage across transformation pipelines
- Identify semantic gaps through semantic reasoning and anomaly detection
By leveraging Kwalify™, validation moves beyond rule execution to context-aware, explainable analysis, enabling deeper insight into how data behaves under real conditions.
This enables teams to uncover structural risks early — before they translate into regulatory issues, rework, or submission delays.
Engagement 03
Design & Development
Care2Data delivers design and development of clinical data systems as part of outsourced product and application development — built with one fundamental assumption:
Unlike traditional system development focused on functionality alone, Care2Data engineers regulatory-grade data architectures designed for inspection readiness, traceability, and long-term reliability.
Capabilities
Our systems are designed to support:
Reliable ingestion from complex clinical data sources (EDC, labs, safety systems, external data)
Standardisation into CDISC-compliant formats such as SDTM and ADaM
Embedded validation and verification controls — built into workflows, not applied post-processing
Version-controlled transformation pipelines ensuring reproducibility and auditability
End-to-end traceability across the clinical data lifecycle
Methodology
How We Execute
Regulatory-Grade Architecture Design
Architect systems aligned with regulatory expectations and data integrity principles — built for inspection readiness from the ground up.
Semantic Model & Validation Embedding
Embed semantic models and validation logic directly within system design — ensuring knowledge drives how data is processed and interpreted.
CDISC-Compliant Standardisation
Design pipelines for reliable ingestion and standardisation of clinical data into SDTM and ADaM formats aligned with submission requirements.
Scalable & Integration-Ready Engineering
Ensure scalability across studies, programs, and therapeutic areas — integrating seamlessly with existing enterprise systems and workflows.
Outcome
Every component is designed to support inspection readiness, auditability, and operational durability — ensuring systems remain reliable under regulatory scrutiny and scalable for long-term use.
This is not simply system configuration. It is regulatory-grade data architecture engineered for validation and verification at scale.
Engagement 04
Data Quality Checking & What Happens Next
Identifying discrepancies alone does not improve data quality. Governance requires structured resolution, traceability, and documented evidence.
Care2Data delivers Validation & Verification of clinical trial data as a core service — enabled through the Kwalify™ intelligence platform — ensuring data quality, integrity, consistency, and conformance across the entire clinical trial lifecycle.
Capabilities
From Detection to Defensibility
Care2Data transforms data quality checking into a governed, end-to-end assurance process, enabling:
Faster issue resolution across clinical datasets
Reduced rework and submission delays
Stronger regulatory compliance and audit readiness
Consistent, defensible clinical data across studies
Explainable root-cause analysis with cross-domain impact assessment
Methodology
How We Execute
Detection
Identify discrepancies, inconsistencies, and anomalies across clinical datasets using rule-based and semantic validation powered by Kwalify™.
Classification
Contextualise issues by domain, severity, and regulatory relevance — prioritising what matters most for submission readiness.
Root-Cause Resolution
Leverage semantic reasoning and cross-dataset analysis to diagnose underlying causes — not just surface errors.
Evidence Documentation
Generate traceable, audit-ready records of validation outcomes, decisions, and corrective actions.
Submission Readiness Validation
Ensure datasets meet standards for quality, traceability, and regulatory defensibility prior to submission.
Care2Data delivers this capability through Kwalify™'s validation and verification engine, which:
- Applies semantic, context-aware validation across linked datasets
- Enables explainable root-cause analysis and impact assessment
- Verifies data integrity through anomaly detection and cross-domain reasoning
- Provides traceability, lineage, and audit-ready validation outputs
- Integrates seamlessly with existing clinical systems and workflows
This service can be delivered as:
- Validation & Verification as a Service across ongoing clinical programs
- Pre-submission readiness assessment and validation
- Continuous data quality monitoring and governance implementation
Validation is not a checkpoint.
It is a governed process of assurance.
Engagement 05
Knowledge Model
Clinical definitions, mapping logic, derivations, and regulatory interpretations cannot remain distributed across documents or individual expertise.
Care2Data engineers knowledge as a core architectural layer within clinical data systems — ensuring that definitions, relationships, and validation logic are embedded directly into how data is structured, processed, and interpreted.
This shifts knowledge from static documentation to active, system-driven intelligence.
Capabilities
Through embedded knowledge models, organisations can:
Ensure consistent interpretation of clinical data across studies and teams
Reduce reliance on manual interpretation and individual expertise
Enable context-aware validation and verification processes
Maintain traceability across source, transformation, and submission layers
Support scalable, repeatable clinical data operations
Methodology
How We Engineer Knowledge Models
Standardised Entity Definitions
Develop formal ontology and taxonomy frameworks that define clinical entities, relationships, and regulatory context across datasets and workflows.
Mapping Logic Embedding
Embed SDTM and ADaM mapping logic directly within transformation pipelines — ensuring consistency and traceability at every stage.
Validation Rule Integration
Integrate validation and derivation rules into system workflows — operationalising knowledge as active, system-driven intelligence.
Metadata & Provenance Frameworks
Build metadata frameworks with lineage, provenance, and traceability context — supporting auditability and regulatory defensibility.
Regulatory Interpretation Models
Align knowledge models with submission expectations through regulatory interpretation frameworks embedded in system architecture.
Outcome
Knowledge becomes an integral part of the system — driving how data is validated, interpreted, and governed. Clinical data transitions from fragmented information to structured, reasoning-enabled intelligence, strengthening consistency, defensibility, and long-term operational scalability.
Knowledge is no longer external to the system.
It is engineered into it.
Institutional knowledge becomes operational infrastructure.
Engagement 06
Training & Handover
Systems that cannot be understood cannot be sustained or defended.
Care2Data delivers structured training and enablement services to ensure client teams can confidently operate, manage, and extend clinical data intelligence systems independently.
Our approach focuses on building capability — not dependency.
Capabilities
Teams are equipped to:
Operate clinical data intelligence systems independently
Defend validation decisions with confidence and traceability
Scale clinical data intelligence across programs and therapeutic areas
Maintain and evolve ontologies, taxonomies, and governance frameworks
Prepare for regulatory queries with traceable, defensible evidence
Methodology
How We Execute
Clinical Data Flow & Architecture
Understand end-to-end data movement across EDC systems, transformation pipelines, and submission datasets.
Validation & Verification Logic
Interpret validation rules, reasoning outputs, and anomaly detection results — ensuring clarity in how data is assessed.
Knowledge Models & Semantic Systems
Work with ontology frameworks, knowledge graphs, and interlinked data models embedded within clinical systems.
Governance & Lifecycle Management
Manage rule evolution, version control, and traceability in alignment with regulatory expectations.
Kwalify™ Platform Enablement & BOT Model
Guide teams through phased capability transfer — from Build and Operate to full independent ownership through the BOT model.