Detect relevant information in essential documents to accurately file documents in your TMF. ML provides a whole workbench of tools to automate your efforts to maintain a healthy, up-to-date TMF.
Here are four business cases where ML can help you approach TMF documentation processes with greater efficiency, timeliness, and quality:
Document Indexing
Classify essential clinical trial documents by reviewing each document’s TMF number, level, zone, section, artifact, sub-artifact, site, contacts, and other key details.
ML ADVANTAGE: Automatically compare many documents, stratify information, check for similarities, and find common formats to accurately classify documents in your TMF.
Metadata Extraction
Identify and capture structured and fielded data in TMF documents, such as the site, country, contact, and document date.
ML ADVANTAGE: Automatically identify structured and fielded data in documents, process forms that have the same structure, then analyze data sets extracted from documents to further classify them by site, country, investigator, and contact.
Inspection Readiness
Conduct a comprehensive assessment of eTMF completeness to pinpoint issues and identify anomalies before a regulatory inspection.
ML ADVANTAGE: Automatically identify all documents with the same classification, perform rulebased conformity checks, do quality verification of outliers and anomalies, and get a quality report to see where there are document issues.
PHI Redaction
Remove personally identifiable information from site documents, such as email addresses.
ML ADVANTAGE: Automatically pinpoint personally identifiable information in documents— such as an email address— to be flagged and redacted before TMF archival.
By automating complex processes for the TMF, ML can greatly influence your team’s approach to data for future clinical trials. For more information on how you can apply ML to your TMF documentation process, read our blog here, and contact us at info@trialinteractive.com.
