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Case study
One defensible evidence source for the whole field team
A medical affairs director gives MSLs and medical information one shared, cited evidence base, ending duplicated searches and keeping responses non-promotional and traceable.
Pharma & Life SciencesAI SearchResearch AgentExtract DataQuality signalsShared workspace
The sentence-level citations are what make it usable in a regulated setting. Every claim we surface can be traced and defended, and because the synthesis is done once, our whole field team works from the same source.
Panoramica
Sofia leads a medical affairs function whose MSLs and medical information team engage in non-promotional scientific exchange and answer unsolicited medical information queries, everything traceable and current.
La sfida
The MSL and medical information teams each rebuilt the same evidence searches for the same therapeutic area, with no shared sourced foundation. Staying current while keeping every claim defensible and non-promotional was manual and inconsistent.
Come si inserisce Paperguide
Paperguide gives the function one place to synthesize the evidence once, cite it to the statement, and have the whole team work from the same defensible source.
Nella pratica
The problem Sofia set out to fix was duplication with a compliance edge. Her MSLs and her medical information team were, in effect, answering the same underlying questions about the same therapeutic area, separately, each rebuilding the evidence from scratch, and every answer had to be both scientifically current and strictly non-promotional. Inconsistency across the team wasn’t just inefficient; in a regulated setting it was a risk.
Now the synthesis happens once. For a question like current evidence on biosimilar interchangeability for adalimumab, AI Search produces a cited answer with every claim linked to its source, exactly the kind of traceable, evidence-only response that scientific exchange demands and that a generalized tool referencing websites of varying quality could never safely provide. To guide the team’s scientific narrative and evidence-generation priorities, she runs a publication gap analysis with the Research Agent to see where the published evidence is genuinely thin. For briefing documents, she extracts a comparative evidence table, population, endpoint, effect, safety, across the relevant studies, and quality signals make each source’s strength visible before anyone leans on it. Because it all lives in a shared workspace, that one defensible synthesis is what the entire field team works from.
The result is a single sourced evidence base across the therapeutic area: duplicated searches end, every response carries sentence-level citations that keep it defensible and non-promotional, and medical information turnaround gets faster without cutting any corner that a reviewer would notice.
Le funzionalità al lavoro qui: AI Search, Research Agent, Extract Data, Quality signals, Shared workspace.
I risultati
- A shared, sourced evidence base across the therapeutic area, ending duplicated searches
- Sentence-level citations on every response, defensible and non-promotional
- Faster turnaround on medical information queries
- Publication gaps identified to focus evidence generation
Altri case study
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Three separate tools became one workspace, so reading, extracting, and comparing papers happens in a single pass.
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Custom-column extraction across hundreds of nanomedicine papers, every value cited back to its source.
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