CASE STUDY
Scaling Clinical Evidence Review Without Scaling Headcount: An AI Integration Case Study
Consultant: Jaani Gandhi
Our Results
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Managed
THE PRODUCTS
Anesthesia & Patient Monitoring
A Fortune 500 manufacturer in the anesthesia and patient monitoring space, developing Class I through Class IIb devices for use in various surgical and recovery environments.
THE CHALLENGE
Siloed Evidence, Scaled Risk
The client's clinical research team was already conducting literature reviews at scale - across 50+ products and tens of personnel - but the value of that work was largely siloed within compliance.
Review depth and methodology varied by on and off shore writers and vendors, leaving clinical evaluation reports exposed to notified body deficiencies and safety signal gaps, while the broader organization - medical affairs, clinical affairs, and sales - had no reliable mechanism to leverage the evidence being collected.
Missed off-label use data and incomplete performance evidence were recurring liabilities, but the deeper problem was structural: a high-volume, resource-intensive process was delivering compliance outputs only, when the same literature had the potential to inform commercial and clinical strategy across the business.
At this scale, the cost of that inefficiency compounded on both ends - deficiency risk on the regulatory side, and unrealized cross-functional value on the other.
THE APPROACH
Systems Over Searches
To address this, we focused on training AI infrastructure that matched the precision of a domain-specialist reviewer, and embedding it directly into the existing medical writing workflow - replacing ad hoc literature searches with a repeatable, product-specific system.
Key elements of the approach included:
Product-specific AI prompt architecture: Designed structured prompt frameworks calibrated to each device's clinical profile, enabling large-volume literature interrogation with consistent depth and safety signal capture across all 45 products
SOTA prompt library for medical writing teams: Built a dedicated prompt library standardizing how writers generate and validate state-of-the-art sections, removing individual variability from the highest-risk portion of each CER
Deficiency tracker as institutional memory: Established a living deficiency tracker that converted notified body queries into forward-looking input, so each submission cycle started sharper than the last
This gave the team the capacity to maintain 50+ active product documentation cycles without adding headcount - and without sacrificing the clinical rigor each submission required.
THE OUTCOME
From Compliance Output to Cross-Functional Asset
As a result, the client achieved a robust system to collect literature evidence across submissions following implementation, with consistent safety and performance data capture across all 50+ products in scope.
Beyond the metrics, the medical writing team shifted from a reactive posture - patching gaps after notified body queries - to a proactive one, with documented systems that improve with each iteration.
With a scalable AI prompt infrastructure and institutional deficiency tracking now embedded in operations, the team is positioned to absorb additional product lines into the MDR compliance cycle without proportional cost or time increases.
Facing a similar challenge?
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