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. We mirrored their clinical trial using AI agents to act like researchers, evidence providers and regulators. The cumulative cost savings relative to a academic only RCT was £234,960. The total time saved: 2,070 hours
The learning points from the process are shown below
Results
Outcomes
Learning Points
Mitigations
From a suppliers perspective. The cost saving was not as important as the time savings: as it is time to market that is the rate limiting step in a digital products journey to success.
Alas the DTx customer in this case study failed. They attributed their failure to a human only research process that took too long to complete: the affect on cashflow was devastating.
In attempt to measure the affect of using Agents to gather and analyse data, relative to one human. The following analysis was completed.
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. We mirrored their clinical trial using AI agents to act like researchers, evidence providers and regulators. The cumulative cost savings relative to a academic only RCT was £234,960. The total time saved: 2,070 hours
The learning points from the process are shown below
Results
- Trial completion time shortened by 60%
- Documentation errors reduced by 90%
- Staff productivity increased by 75%
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
From a suppliers perspective. The cost saving was not as important as the time savings: as it is time to market that is the rate limiting step in a digital products journey to success.
Alas the DTx customer in this case study failed. They attributed their failure to a human only research process that took too long to complete: the affect on cashflow was devastating.
In attempt to measure the affect of using Agents to gather and analyse data, relative to one human. The following analysis was completed.
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%)