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CASE STUDY

i2e helps clinical sponsor to redesign RBQM around business needs, not just technology

industry-iconCLIENT :Confidential
industry-iconINDUSTRY :Pharmaceutical
industry-iconDURATION :30 days
CLIENT :
Confidential
INDUSTRY :
Pharmaceutical
DURATION :
30 days

Business case

A clinical sponsor knew its approach to risk-based quality management wasn't sustainable. The team was buried in the mechanics of the work: preparing data, loading files, managing signals. There was no time left to focus on what the results meant or how to act on them. The analysis was getting done, but the thinking behind it wasn't.

The sponsor faced a choice. Treat this like a typical IT project and bring in consultants to write requirements and code, or take a different approach, one that started with business context rather than technology.

Our Solution

  • SME-led shaping the project was shaped by subject matter experts who understood the realities of RBQM and data-driven data review, not by a standard requirements process.
  • Early solution architecture a solution architect joined from the start to explore new technologies rather than simply replicate the old system.
  • Joint design process the SME and technical team mapped the sponsor's real goals together, explored options, and designed a solution built around how the team actually wanted to work.
  • Redesign, not replacement this was not a lift and shift. It was a redesign grounded in the day-to-day needs of RBQM teams working with complex trial data.

Challenges overcome

  • Mechanics over meaning: the team's time went to data prep, file loading, and signal management, leaving no room for interpretation.
  • Risk of a standard IT approach: a typical requirements-and-code project risked replicating the old system's limitations rather than addressing them.
  • Legacy process constraints: the existing approach to RBQM was not sustainable and limited how the team could work.

Benefits

  • Delivered a validated, faster, and more affordable approach rather than a like-for-like system replacement.
  • Gave the team a solution designed around how they actually work, not a standard implementation.
  • Created space for the team to interpret what the data showed, not just process it.

Results

Results
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