Speed and Accuracy of an AI-Automated Medical Record Screening Tool for Therapeutic Clinical Trials
Co-Founder & Chief Medical Officer
Clinical Director of Data Analytics
Senior Machine Learning Engineer
Director of AI
98.1%
PDF Record Accuracy
99.7%
HIE Record Accuracy
2.6 min
Median Processing Time (PDF)
Abstract
Background
The recruitment of medically qualified patients is a major bottleneck in therapeutic clinical trials. Traditional manual screening methods are cost and labor-intensive, requiring up to 8.8 hours per enrolled patient per study.
Objective
To assess the speed and accuracy of an AI-automated medical record screening tool for therapeutic clinical trials.
Design
Retrospective chart review.
Results
We observed an overall question accuracy of 98.1%, a false negative rate of 0.46%, a false positive rate of 7.41%, and a median processing time of 2.60 minutes for PDF records. For Health Information Exchange records, we observed an overall question accuracy of 99.7%, a false negative rate of 0.24%, a false positive rate of 0.77%, and a median processing time of 4.31 minutes.
Conclusion
Our platform demonstrates the potential to drastically reduce the time and cost associated with manual medical record review for screening and medical qualification of patients into therapeutic clinical trials.