By Role · Clinical Data Management
Reduced Database Lock Time by 40–50%, Accelerating Trial Data Readiness
See how CDM teams reduce query volume by 60–70%, accelerate database lock by 40–50%, and cut operational costs by 30% with AI-powered data workflows.
60–70%
Query volume reduction
40–50%
Faster database lock
30%
Cost savings vs traditional CDM
60–70%
Manual effort reduction
WHO IT IS
The Context
A Clinical Data Management Director in the United States is overseeing studies generating 5,000–50,000 queries per trial, with ~80% of team capacity consumed by manual data cleaning.
Data remains fragmented across EDC, labs, and eCOA systems, with inconsistencies often identified late in the cycle.
This results in 6–8 month database lock timelines, with query backlogs peaking near study completion.
Challenges
Key barriers to Trial Execution
Late-Breaking Query Backlogs
- 5,000–50,000 queries per study
- Query review consumes significant CDM capacity
- Late-stage cleanup cycles create lock-readiness pressure
Inconsistent Site Data
- Variability across multi-center sites
- Different formats, units, and timelines
- Ongoing quality control challenges
Manual Bandwidth Drain
- 80% of data manager time spent on manual review
- Limited proactive quality management
- Reduced strategic oversight
Submission Deadline Pressure
- 6–8 month database lock timelines
- Manual reconciliation and validation cycles delaying submissions
- CDM bears downstream escalation pressure
Operational solution
Maxis AI agentic workflows — under human oversight throughout
AI Data Cleaning & Validation
- Real-time detection of inconsistencies
- Missing values and outliers resolved
- Human oversight maintained
Outcome
60–70% query reduction
Accelerated Database Lock
- Continuous validation across study
- Eliminates late-stage cleaning backlog
- DB lock reduced from 6–8 to 3–4 months
Outcome
40–50% faster database lock
Multi-Source Integration
- Unified data across EDC, lab, imaging, device
- Vendor-neutral execution layer
- No core system replacement required
Outcome
30% cost savings vs traditional CDM
Maxis AI operates as a governed and supervised execution layer within existing systems throughout.
See it in your data management workflow
Curious how this could work for your data management team?
We'll review your data management workflow, identify execution bottlenecks, and show where governed AI can accelerate database readiness in a 30-minute working session.
Measured impact
Quantified outcomes after deploying Maxis AI's agentic workflows
| Metric | Before Maxis AI | After Maxis AI |
|---|---|---|
| Database lock timeline | 6–8 months | 3–4 months (40–50% faster) |
| Query volume | 5,000–50,000 queries per study | 60–70% reduction in manual queries |
| Manual Effort | High manual workload | 60–70% reduction |
| Cost per trial | $850K average cost; 12–15 FTEs | 30% cost savings |
| Submission readiness | Delayed by data-quality issues | Faster analysis readiness and audit traceability |
Outcome
Quantified Benefits
Database lock compressed from 6–8 months to 3–4 months, 60–70% fewer queries, 30% cost savings, and the end-of-study reconciliation avalanche eliminated entirely.
Database lock timeline: 3–4 months (40–50% faster)
Query volume: 60–70% reduction in manual queries
Manual Effort: 60–70% reduction
Cost per trial: 30% cost savings
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