Legacy data structures and locally managed correction workflows created operational complexity across five practice sites. Field research across 14 clinical actors showed that the future state needed to support real-world patient, coordinator and provider workflows while improving data consistency, change traceability and platform scalability.
For a PE-backed real-world evidence organization pursuing expansion, the modernization mandate was also a value-creation mandate. Leadership needed an operating foundation that could support additional registry services and commercial opportunities while improving specialist capacity, strengthening regulated data controls and changing the economics of launching and operating new registries.
PE-backed growth mandate: Expand registry services and commercial opportunities by building the operating foundation for enterprise value creation.
The engagement combined program governance with four domain workstreams so workflow, data lineage, architecture, safety processing, platform economics and implementation decisions could be evaluated as one modernization program.
Coordinated executive decisions, sequencing, risks, vendor direction and delivery across four domain workstreams.
Scope was separated into must-have, should-have, could-have and deferred requirements so leadership could protect the delivery path while keeping future enhancements visible.
Core Operating Insight: A cloud data warehouse cannot establish a single source of truth if analysts continue correcting raw data in local scripts. True data integrity requires converting analyst workarounds into a closed-loop, audited operating process.
Analysts fixed data in local scripts, versions drifted, lineage became difficult to reconstruct, and final analytic files could vary by user or workstation.
The documented target design routed corrections through a tracked workflow, updated shared data through managed change, and had analysis consume a common source rather than recreate its own truth.
A tracked correction workflow connects issue detection, source correction, verification, and shared downstream data.
Incomplete or incorrect data is detected in reporting, analysis, or data review.
The issue remains visible while the appropriate correction path is determined.
Route live source issues back to the EDC or site path; handle historical data through controlled correction.
Confirm the correction before the shared data environment feeds downstream use.
Reporting, safety case history, and analysis consume a common data environment instead of recreating corrections inside local scripts.
Because the advisory engagement ended before the client’s full benefits-realization period, InteliGems did not independently measure every post-launch outcome. The public record therefore distinguishes reported capacity outcomes, documented vendor economics and design requirements, and planned live-state targets.
Documented per-registry build and annual support economics avoided through the selected modular AWS direction.
Documented vendor TCO / cost avoidanceReported combined monthly time returned across biostatistics and clinical data management.
Reported capacity outcomePlanned live state target for the cycle to prepare biostatistical analytic files.
Planned live success targetPlanned live state target for elapsed time to modify clinical case report forms.
Planned live success targetThe strongest lesson is not about one platform. Comparable modernization mandates are more credible when clinical operating reality, data controls, platform economics, and delivery governance are evaluated together.
Platform modernization creates more value when leadership connects how work is actually performed, how data changes are controlled, what the economics support, and what must be proven before scale.
Field research can expose adoption constraints, hidden workload, and transition requirements before they become implementation problems.
Define source lineage, correction rights, validation evidence and review responsibility before scaling downstream analytics or automation.
Make / Buy / Assemble decisions should connect capability and implementation effort to vendor economics, operating capacity, and the ability to support additional registry services.
These are current external developments, not claims about the historical engagement. The cited FDA and ICH guidance and FDA/EMA Good AI Practice principles address reliable data, traceability, proportionate controls, clear responsibilities, and lifecycle governance in relevant clinical and drug-development contexts.
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Principal-led advisory and operating leadership for executive teams facing consequential AI, governance, transformation, commercialization, and recovery mandates.
advisory@inteligems.io
