SecureCHEK AI in Action: Case Studies

SecureCHEK AI is proven to accelerate the submission of review-ready materials and reduce reviewer rejections through evidence-linked claims management and a rigorous cross-checking methodology prior to Medical, Legal, Regulatory (MLR) review. Reviewers can trust the findings, marketers can secure approvals faster, and agencies can produce compliant, submission-ready materials more efficiently.

Explore the case studies below to see how SecureCHEK AI addresses distinct operational priorities across life sciences organizations.

Medical Affairs for Precommercial Education

Patient Access/Payer for Commercialized Products

Promotion for Commercialized Products

Large Medical Device Company

Simplifies enterprise-wide MLR review and approval

Objective

Simplify enterprise-wide MLR review and approval for approximately 1,000 products through state-of-the-art claims substantiation and automated content prechecking.

Situation

Roughly one-third of MLR rejections involved incomplete or insufficiently substantiated submissions. Claims were not consistently linked to supporting evidence, creating avoidable substantiation gaps.

Outcomes

Within 90 days, users reported higher-quality submissions and fewer moderator rejections. Submitters spent less time finding/linking claims; reviewers received clear comparisons with approved language.

Opportunity

Fewer rejections with projected enterprise-wide savings of 25,000+ hours.

Large Healthcare Agency

Reduces preparation time for review-ready submissions

Objective

Accelerate the preparation of MLR submissions by eliminating manual prechecks performed by copywriters and editors.

Situation

Manual prechecking of working content introduces errors, including incorrect substantiation, missing references, and ISI errors. Correcting preventable errors can cause teams to miss MLR review meetings and delay distribution of marketing materials.

Outcomes

Agency avoided ~four days of review by preventing common deviations early in content development. Instant identification of all deviations, from serious red flags to preventable errors, reduces fees.

Opportunity

On-time MLR review meetings, quicker distribution of marketing materials.

Small Pharmaceutical Company

Increases coordinator capacity

Objective

Increase coordinator capacity by using AI to verify that every MLR-requested revision has been accurately incorporated.

Situation

Company is actively promoting its product with extremely limited resources. Manual checking is time-consuming because coordinators must read every word.

Outcomes

Reduced coordinator checking time by 68%, doubling coordinator capacity.

Opportunity

Less coordinator review time, greater capacity without additional resources.

Medical Affairs for Precommercial Education

Startup Biotech Company

Establishes governance to scale Phase III data release

Objective

Create the governance infrastructure to rapidly scale compliant, evidence-based communications to healthcare professionals.

Situation

As pivotal Phase 3 readout approaches, demand increases for content review by lean teams. Approved messaging and evidence reside in multiple locations, making it labor-intensive to ensure accuracy, consistency, traceability, and governance.

Outcomes

Library of approved, substantiated messages completed in under one week. Automated prechecking reduces MLR fees by one-third.

Opportunity

Faster review of growing materials for scalable Phase III data dissemination.

Large Pharmaceutical Company

Speeds promotional activation for ePrints

Objective

Speed promotional activation by instantly identifying which peer-reviewed ePrints are aligned with product labeling and appropriate for promotional use.

Situation

Promotional distribution of peer-reviewed ePrints requires content to align with approved product labeling. Manual evaluation delays awareness of scientific data and timely engagement with healthcare professionals.

Outcomes

Automated checks compare each ePrint with approved labeling to identify inconsistencies and unsupported claims. Teams receive an evaluation within 48 hours to support submission decisions and reduce review time.

Opportunity

Short review cycles and more timely dissemination of scientific evidence.

Patient Access/Payer for Commercialized Products

Large Pharmaceutical Company

Reduces routine costly legal review demands and editorial outsourcing

Objective

Reduce routine legal review demands by enabling editorial consultants to automate prechecks before submission for legal consultation.

Situation

Claims library maintained in Excel makes it difficult for agencies to consistently follow approved instructions. Editorial services are outsourced to verify patient access and payer information in every promotional material.

Outcomes

Automated library created in under one week, reducing legal review by 90% through 100% verification of required legal language.

Opportunity

Fewer routine legal consults with same editorial resources, faster review.

Ready to see SecureCHEK AI in action?

Request a demo to learn how we can accelerate your MLR review process.