Overview
Automating Document Processing in HCLS with AI: A Case Study on PSC BiotechProvectus |
Analytics & Modeling - Machine Learning Analytics & Modeling - Natural Language Processing (NLP) | |
Life Sciences Pharmaceuticals | |
Logistics & Transportation Product Research & Development | |
Behavior & Emotion Tracking Object Detection | |
Data Science Services Training | |
Operational Impact
The automated solution developed by Provectus for AI/ML-powered processing and classification of documents has led to significant improvements in PSC Biotech's operations. The solution not only designed and built a highly accurate ML model for observation classification but also set up an entire ecosystem to ensure its performance and cost-efficiency. This included a secure and reproducible, end-to-end ML infrastructure with CI/CD pipelines and all integrations for user-friendly management of documents in PSC Biotech’s existing pipeline. As a result, PSC Biotech can now handle FDA Form 483 observations much faster, more accurately, and cost-effectively, and at scale. The new observation classification solution has enabled PSC Biotech to dramatically decrease the time spent on manual review of observations, optimize processing costs, and increase accuracy and throughput of document processing while mitigating risks of infractions made by mappers and reviewers. | |
Quantitative Benefit
Automated AI/ML solution for observation classification delivered | |
ML model with precision and recall exceeding 70% on new FDA Form 483 observations | |
Secure, reproducible, end-to-end ML Infrastructure for intelligent document processing | |