AutoDeclare's use with RCTs: AutoRCT
Case Study
A digital therapeutics (DTx) developer based in Manchester, and their USA investor, faced several critical challenges when implementing a RCT:
Solution
AutoDeclare: AI-powered automation methods. The research results, process and results is shown below for individual AutoRCTs.
Results
Outcomes
Learning Points
Mitigations
A digital therapeutics (DTx) developer based in Manchester, and their USA investor, faced several critical challenges when implementing a RCT:
- Labour-intensive documentation processes that were new to the business
- Inconsistent reporting standards from local, regional and national gate-keepers
- Extended trial completion timelines because of the lack of availability of academic researchers
- Rising costs amidst market pressures as often researchers need to be recruited for individual trials
Solution
AutoDeclare: AI-powered automation methods. The research results, process and results is shown below for individual AutoRCTs.
Results
- Trial completion time shortened by 60%
- Documentation errors reduced by 90%
- Staff productivity increased by 75%
- Cumulative cost savings relative to a academic only RCT: £234,960 - Total time saved: 2,070 hours
Outcomes
- Reduced time-to-market by 60%
- Strengthened investor relations
- Enhanced regulatory compliance
- Improved trial data quality
Learning Points
- Initial regulator staff resistance to new technology
- Data migration complexities
- System integration hurdles
- Learning curve impact on productivity
Mitigations
- Phased implementation approach
- Parallel systems operation
- Regular validation protocols
Time Analysis
Traditional Process
200 hours
Automated Process
27.5 hours
Time Saved
172.5 hours (86%)
Cost Analysis
Traditional Cost
$36,200
Automated Cost
$11,725
Cost Saved
$24,475 (67.6%)