Case Study
From Quantitative to Scoping Reviews: How Terumo BCT Built a Defensible Clinical Evidence Practice with DistillerSR
By shifting from quantitative to scoping reviews and integrating AI-enabled screening, the team now manages a multitude of CERs including Class I, IIa, IIb and III products, and has maintained few follow-up queries from notified bodies.
The team’s most important shift was moving to scoping reviews that fit the available literature—DistillerSR’s flexibility made that
transition possible.
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teams looking to achieve long- term efficiency.
founded in 1921 and headquartered in Tokyo. Founded in 1965 in Lakewood, Colorado, Terumo BCT specialises in blood component
collection and processing, therapeutic apheresis, and cell and gene therapy technologies. With a portfolio that includes Class III devices
requiring rigorous, audit-ready Clinical Evaluation Reports (CERs), maintaining high-quality clinical evidence is a core regulatory requirement.
The team had been conducting quantifiable literature reviews—designed for meta-analysis and the kind of large, comparable evidence bodies that approach requires. In practice, however, the available literature for their device categories rarely supported that level of quantification. Cochrane guidance, too, pointed toward quantifiable reviews being appropriate less frequently than the team had been applying them.
The conclusion was clear: scoping reviews were a better fit. And making that shift—methodological, not just procedural — turned out
to be where DistillerSR’s flexibility became most valuable.
“DistillerSR didn’t just digitize our process; it enabled a fundamental shift in our methodology, allowing us to evolve from resource-intensive quantifiable reviews to more appropriate scoping reviews that fit the available literature”
The result was a review process better matched to the evidence landscape they actually worked in: one that could surface meaningful clinical insights across a body of literature without forcing a false precision that the data couldn’t support. Today, the team uses DistillerSR to conduct a multitude of CERs including Class I, IIa, IIb and III products — all conducted through the scoping review approach they have refined over time
Alongside the methodological evolution, the team has progressively integrated DistillerSR’s AI features into their workflow—and the impact on efficiency has been significant.
- Classifiers and Rerank are deployed at the start of each project, dramatically reducing the front-end screening burden and allowing clinical staff to focus on higher-value analysis rather than initial triage.
- AI screening is used to accelerate reference categorisation, with results reviewed and verified by the clinical team before any decisions are made.
- A centralized data repository replaced fragmented files with a single, searchable record of all screened articles — making it easier to maintain consistency and audit trails across review cycles.
plans and reports that detail the systematic literature review process and document when and how AI is used to facilitate review.
First, they are actively evaluating DistillerSR’s Smart Evidence Extraction (SEE) feature, which they believe would add particular value for synthesising peer-reviewed published articles—with any new capability validated across a subset of projects before broader deployment.
Second, and more ambitiously, the team aims to transition from point-in-time annual project cycles to a continuously maintained
literature database — and to build a cross-functional knowledge base that allows colleagues in Medical Affairs, Quality, and Regulatory Affairs to query and access evidence independently, without requiring clinical team involvement for every data request
“Our next strategic step is to enhance our annual project cycles into a continuously updated database and build a cross- functional knowledge base. We envision a user-friendly interface that allows other departments—like Medical Affairs, Quality, and Regulatory Affairs—to query the system and access information without extensive training, which would eliminate the need for our clinical team to manually pull data for them.”
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