SEND Data Validation and Reconciliation for Clinical Pathology

Summary

A $130 billion pharmaceutical company, responsible for the safety evaluation of a new drug, faced inefficiencies in validating clinical pathology data.
For in vivo studies, clinical pathology data (e.g., hematology, clinical chemistry, urinalysis) must be mapped to SEND domains such as LB (Laboratory Test Results), CL (Clinical Observations), MI (Microscopic Findings) and submitted to FDA.
The data, originating from the in vivo studies, was dispersed across multiple Electronic Health Record (EHR) systems, requiring consolidation and submission to the FDA to secure approval for subsequent phases.

Why It Matters: Any errors in the underlying data or mistakes during submission could significantly delay FDA approval. To mitigate this risk, rigorous validation of the data prior to submission is critical.

The Issue: However, the team encountered substantial challenges in delivery of timely and accurate data.

1. Inefficient manual processes hindered timely analysis and strained resources:

• Scientists spent 3–5 hours per study reconciling data manually, amounting to 1,200 hours annually.
• This repetitive effort delayed critical project milestones and diverted attention from higher-value analysis.

2. Potential validation gaps threatened FDA approval timelines:

• Labor-intensive workflows increased the risk of errors in SEND submissions.
• These vulnerabilities posed a risk of costly delays, with potential multi-billion-dollar business implications.

To address the need for automation while ensuring compliance and consistency with FDA standards, the company implemented iceDQ, a data reliability platform that automated data reconciliation, validation, and exception handling across the entire pipeline.

Results with iceDQ:

• Validation timelines were reduced by over 95%
• Resource requirements for validation dropped by 60%
• Maintained FDA submission requirements by 100%

This case study highlights how a major pharmaceutical company successfully transitioned from manual validation and reconciliation to an automated, reliable data pipeline using iceDQ.

The SEND Data Collection and Submission System

The pharmaceutical company’s clinical data pipeline was structured to ensure that clinical pathology data spanning various Electronic Health Record (EHR) platforms, Oracle systems (Staging & Cerner), and SAVANTE, were consistently collected, processed, validated, and prepared for regulatory submission to the FDA.

Fig 1: The SEND Data System

Challenges in Validating & Reconciling SEND Data

The manual data validation and reconciliation of data between systems created bottlenecks affecting study timelines and regulatory compliance:

• Manual Data Validation Was Time-Consuming: Scientists spent thousands of hours annually verifying HL7 message accuracy between Oracle and Cerner, often relying on random sampling due to data volume, diverting time from critical analysis.

• No Real-Time Reconciliation with SAVANTE: Lack of automated validation between Oracle and SAVANTE created regulatory risks and potential FDA approval delays.
• Manual Business Rule Check Before FDA Submission: Scientists manually applied business rules before submission. While Pinnacle 21 ensured CDISC compliance, it couldn’t catch missing or mismatched records, leading to critical gaps and potential delays in drug approvals.

These validation and reconciliation pain points collectively introduced delays in FDA submission timelines, risking incomplete or rejected data packages, and ultimately causing drug approvals to be delayed by months, resulting in a commercial impact totaling billions of dollars.

Enter iceDQ: Automated Data Validation Testing

To eliminate these inefficiencies and reduce regulatory risk, the pharmaceutical company implemented iceDQ, an automated data reliability platform built to streamline validation, enforce business rules, and reconcile critical study data across systems.

Fig 2: iceDQ’s implementation within the clinical data pipeline

How iceDQ Supports SEND-Readiness

Here’s how iceDQ can be strategically applied across the SEND lifecycle:

1. Automated Data Validation

• iceDQ enables rule-based validation of raw and transformed data against SEND Implementation Guide (SENDIG) standards.
• You can define custom rules to check for:
  • Domain-specific constraints (e.g., LBTESTCD values in LB domain)
  • Cross-domain consistency (e.g., matching subject IDs across TS and DM)
  • Controlled terminology compliance

2. Data Reconciliation Across Systems

• Clinical pathology data often originates from multiple EHRs, LIMS, and lab systems.
• iceDQ automates reconciliation logic to ensure consistency across these sources before mapping to SEND domains.

3. Exception Handling and Audit Trails

• iceDQ flags anomalies and exceptions in real time, allowing teams to troubleshoot and resolve issues before submission.
• Every validation run is logged, creating a traceable audit trail–a key requirement for regulatory inspections.

4. Integration with SEND Tools

• iceDQ can be integrated upstream of SEND dataset generation tools (e.g., Pinnacle 21, SENDIG mappers).
• This ensures that only clean, validated data enters the SEND packaging process, reducing rework and submission delays.

5. Continuous Monitoring

• Post-submission, iceDQ can monitor production data pipelines for drift or anomalies, ensuring that future studies remain SEND-ready by design.

Business Impact & Conclusion

The implementation of iceDQ transformed the pharmaceutical company’s clinical pathology data validation workflows and delivered measurable operational and regulatory benefits:

Fig 6: Value Proposition using iceDQ

• 95% Reduction in Validation & Reconciliation Time: Manual validation and reconciliation dropped from ~1,200 to 60 hours annually, a 95% reduction.
• 60% Reduction in FTE reliance: iceDQ cut FTE QA needs by 60% across ~250 studies per year.
• 100% Compliance with FDA Submission Requirements: iceDQ improved FDA SEND submission readiness by automating validations, standardizing checks, reducing errors, and enabling non-technical teams to manage data quality independently.

By implementing iceDQ, the pharmaceutical company eliminated bottlenecks associated with manual clinical pathology data validation across disparate systems. Automated reconciliation, FDA-compliant rule enforcement, and real-time dashboards enabled a seamless validation process.

This transformation in the data workflow ensured compliance and safeguarded drug development timelines, ultimately protecting billions in potential revenue from costly regulatory delays.