Case Study: Streamlining Data Management for a Gene Therapy Trial Integrating EDC and EMR Platforms in a Phase 2 Diabetic Ulcer Study

Background

A biotech sponsor developing a gene therapy for wound healing in ulcerative patients needed a robust data management strategy to reconcile EHR images with their clinical database (EDC). The Inference DM team identified complex issues in the process, highlighting opportunities for workflow optimization. The team implemented improvements across clinical and DM groups to ensure the data could be effectively measured for interim and final analysis.

Challenge

During a Phase 2 study involving 150 patients, the sponsor and their clinical CRO encountered critical data reconciliation issues between the clinical database (EDC) and their EHR platform. Technical limitations within the EDC system required additional resources to address software issues and train the staff.

The Inference data management team identified multiple instances where wound images from the index leg were mistakenly associated with the non-index leg, and uncovered issues with the EDC platform that contributed to the misclassification which led to significant data discrepancies, compromising the integrity of the wound assessment data and potentially impacting clinical decisions.

Approach

During routine data reconciliation, data management personnel encountered significant difficulties differentiating between discrepancies in wound images on the index leg and those on the non-index leg. These discrepancies posed challenges in ensuring accurate data representation and analysis, potentially impacting clinical outcomes.

Specific Example of Issues

  1. Misclassification of Wound Sites: Several wound images were mistakenly categorized under the wrong leg (index vs. non-index), leading to inaccurate wound progression tracking and skewed data analysis.

Methods

The Lead Data Manager (DM) proactively organized meetings with the sponsor to identify corrective actions and plan to timeline and resources to address them before Database Lock (DBL).

Key steps included:

  • Detailed Issue Analysis: Each discrepancy was thoroughly examined with stakeholder input.
  • Collaborative Problem-Solving: Joint brainstorming sessions generated practical solutions.
  • Clear Communication: Open communication was maintained among all parties.

Data Management implemented enhanced training for the study team to get around technical shortcomings with integration, focusing on:

  • Knowledge Enhancement: Training in EHR related reconciliation queries and discrepancies.
  • Skill Development: Hands-on training for image data reconciliation.
  • Confidence Building: Strengthening the team’s understanding of the systems being used and areas to pay attention to when managing EHR data.

Key Takeaways

This proactive approach led to positive outcomes:

  • Improved Data Accuracy: Resolved discrepancies ensured accurate data representation.
  • Enhanced Collaboration: Strengthened communication and collaboration.
  • Empowered Personnel: The study team gained the necessary knowledge and skills.
  • Workflow Improved: Boosted confidence and streamlined in managing EHR and clinical data.

Inference specifically resolved technical issues and ensured data accuracy. The importance of proactive problem-solving, collaboration, and reconciling EHR (image documentation) data gave our team the ability to address integration of disparate data to create a complete patient journey.

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