Automating eTMF Document Classification & Extraction:
Leverage Pre-Trained AI to Streamline TMF compliance with CDISC and Reduce Submission Rejections
The Roots: What spurred this on?
Due to increasing regulatory standards, refuse-to-file (RFT) action by the regulatory agencies is on the rise. For pharmaceutical companies of any size, a refuse-to-file (RFT) can carry business-critical impacts, such as significant stock drops, mass layoffs and negative brand reputation.
How do these issues germinate and why are they so difficult to eradicate?
1. Incomprehensible Volumes
Organizations managing clinical trials are tasked with an enormous endeavor to bring order to millions of documents, spanning 300+ nested categories, which is critical for a new drug submission to regulatory bodies, such as the FDA.
2. Gaps In eTMF Automation
The industry made strides in taming the chaos by providing a TMF Reference Model to help standardize the Clinical documentation filing, with many eTMF solutions on the market today helping do just that. But, the process of classifying, formatting and enriching clinical documentation and metadata is still a human-centric process.
3. Difficult to Transfer Knowledge
AI models have been getting increasingly more intelligent over the recent past. But they were not smart enough yet to understand highly nuanced and contextual clinical documentation workflows reliant heavily on unstructured data. Up until now, bringing order to clinical data has only been achieved by human professionals with large expertise in this field.
4. No Room For Poor Data Governance
Global shift to standardization via GDP, ALCOA+ and recently updated ICH E6 (R3) brought much needed built-for-purpose procedures, resulting in better data integrity and auditability. This means organizations are upheld to a higher standard of quality when it comes to clinical data and documentation submitted for review. While the review process is becoming increasingly mechanized, the submission preparation process is still largely human-driven and is ultimately error-prone.
What can Adlib do to help?
Relying on assistance of machine automation with human-like intelligence to classify clinical documentation and extract metadata in line with requirements can ensure regulatory acceptance of submissions and eliminate refuse-to-file (RFT). Adlib's Pre-Trained AI capabilities can now make this possible.
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Stemming business challenges
Inefficiencies & Delays
Manual documentation classification and metadata extraction and validation is time-consuming, demanding on resources, error-prone and ultimately delays time to market.
Increased Chance of RFT Letters
Varying approaches and training across business units may cause inconsistencies in TMF increasing rework, demand on resources and delays.
Misfiling & Retrieval Issues
Human errors may cause misplaced or incomplete documentation, complicating future retrieval, audits and rejection risk.
Scalability & Cost Concerns
Staff shortages, increased turnover and expertise gaps hinder growth and elevate operational costs.
Benefits of automating with Adlib's Pre-trained AI
Happier, More Productive Staff
Automated preparation of compliant, validated and submission-ready TMF documentation frees up time, empowering your staff to get more done.
Faster Approval & Launch
More efficient TMF prep and reduced chance of regulatory rejection enable your organization to expedite Go-To-Market action plan knowing that your submission will get accepted and approved on time.
Proven Compliance & Accuracy
Pre-trained AI gets the work done accurately and in adherence with regulatory requirements every single time, eliminating rejection risk.
Adlib AI Grows With Your Needs
The elasticity of Adlib's cloud-based solution can support spikes in demand or flourishing growth without resource constraints.
reduction in refuse-to-file (RFT) action
due to misclassified documentation or missing metadata.
efficiency gain within 6 months
without growing your team.
How It Works:
Leverage custom-built and pretrained AI model to drive eTMF document classification
How It Works:
Leverage custom-built and pretrained AI model to facilitate extraction of metadata from clinical trial documents that have been classified using the eTMF schema.
How It Works:
Annotation module tracks low confidence classification and data extraction entries enabling the human in the loop to correct & modify the class assigned to each document or extracted metadata.
How it Works:
Automated retraining capability enables the underlying models to continually improve knowledge libraries for even better performance.
How It Works:
The systems seamlessly plugs into existing DMS, eTMF, RIM , eQMS systems and other systems via API integration.
How It Works:
Advanced document assembly functionalities automatically standardize 300+ document types, including PDF, Word, TIFFs, Images and Excel, into conformant, submission- and archive-ready formats reducing technical rejections.
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