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Reduced Database Lock Time by 40–50%, Accelerating Trial Data Readiness
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Outcomes
What we did
60–70%
Query volume reduction
40–50%
Faster database lock
30%
Cost savings vs traditional CDM
20–30%
Data cleaning workload reduction
About Company
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.
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
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Issues caught at entry but surface late
1,000+ query backlogs before DB lock
Unsustainable cleanup cycles
Inconsistent Site Data
Variability across multi-center sites
Different formats, units, and timelines
Ongoing quality control challenges
Manual Bandwidth Drain
80% time spent on manual review
Limited proactive quality management
Reduced strategic oversight
Submission Deadline Pressure
DB lock delays impact submissions
Downstream pressure across teams
CDM bears escalation burden
Core platform
Operational Solution
Maxis AI Agentic Workflows — Under Human Oversight Throughout
AI Data Cleaning & Validation
Real-time detection of inconsistencies
Missing values and outliers resolved
60–70% query reduction
80–90% faster SAP
Accelerated Database Lock
DB lock reduced from 6–8 to 3–4 months
Continuous validation across study
Eliminates late-stage cleaning backlog
40–50% faster database lock
Multi-Source Integration
Unified data across EDC, lab, imaging, device
Vendor-neutral execution layer
No core system replacement required
30% cost savings vs traditional CDM
Maxis AI operated as a governed and supervised execution layer within existing systems throughout.
Core platform
Measured Impact
Quantified Outcomes After Deploying Maxis AI’s Agentic Workflows
| Metric | Before Agentic AI | After Agentic AI |
|---|---|---|
| Database lock timeline | 6–8 months; late-stage query backlog delaying lock across 30–40% of trials | 3–4 months; 40–50% faster through continuous real-time data validation |
| Query volume | ~15% of data points required manual queries at end of study; 80% of DM time on manual review | 60–70% reduction in manual queries; AI executes routine cleaning and queries under supervision |
| Data cleaning workload | High manual reconciliation consuming team capacity near lock date | 20–30% reduction in cleaning workload; team focused on complex exception handling |
| Cost per trial | $850K average CDM cost per trial; 12–15 FTEs required; 8–12% error rates | 30% cost savings vs traditional CDM model; capacity expanded without proportional headcount |
| Submission readiness | Delayed — database lock compression reducing time available for statistical analysis | Faster statistical analysis readiness; improved audit traceability; reduced end-of-study crunch |
Outcomes
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.
40–50% faster database lock — 6–8 months compressed to 3–4 months
60–70% reduction in manual data queries through real-time continuous validation
30% cost savings compared to traditional CDM model
20–30% reduction in data cleaning workload — teams refocused on complex clinical judgment
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