The Problem
The client's HEOR team was running 12 simultaneous systematic literature reviews across oncology, rare disease, and immunology — each to support HTA submissions, advisory board materials, or clinical development decisions.
Each SLR was conducted as an isolated, point-in-time exercise. Separate analyst teams. Manual database querying, abstract screening, full-text review, data extraction, and PRISMA reporting. Weeks of effort per indication. And once completed, the output was immediately outdated as new publications continued to emerge.
The team's own estimate: 60% of total HEOR effort was going to evidence assembly. Not evidence of interpretation. Not insight generation. Assembly.
When HTA submission updates required refreshed SLRs, the answer was full re-execution. There was no mechanism to do otherwise.
What Evalueserve Did
Evalueserve implemented the full LitVerse platform across all 12 active indication workstreams: LitVerse as the living evidence repository, Lit.AI as the automation engine, and LitBot as the conversational query interface.
Lit.AI was configured with the client's PICOS/PICOT frameworks for each indication and connected to PubMed, Embase, and the client's internal clinical study library. AI agents took over abstract screening, full-text pre-screening, and structured data extraction into standardized schemas. Senior HEOR consultants retained oversight at quality control and bias assessment checkpoints — where expert judgment is not optional.
PRISMA flowcharts and epidemiology summaries were auto generated at each SLR completion milestone. And critically, the repository stayed live: as new publications were ingested, evidence bases updated automatically.
The Results
SLR execution time fell 42% on average across the 12 indications. The highest gains were indications with large, rapidly evolving evidence bases — exactly where manual execution was most expensive.
Three SLRs that would have required full re-execution for HTA submission updates were instead refreshed automatically as new publications were ingested. Estimated saving: 6 weeks of analyst time per update cycle.
LitBot was adopted by the Chief Medical Officer's office for rapid evidence of queries ahead of payer meetings and advisory boards. A briefing preparation process that had previously taken 2–3 days per session was eliminated.
Why This Matters
Point-in-time SLRs have a structural problem: HTA agencies operate on different timelines than the evidence base. By the time a dossier is submitted, the SLR underpinning it may be months out of date. Full re-execution is the only available response — until you have a living repository.
The shift LitVerse enables is not primarily about speed. It's about changing the unit of effort from re-execution to refreshing. That changes how HEOR teams allocate senior capacity, how submission timelines are planned, and how much of the team's expertise is spent on interpretation rather than assembly.
The CMO office querying evidence in real time before payer meetings is not a minor workflow improvement. It's a capability that changes how those meetings are prepared.
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Overview & Impact
A global biotech’s HEOR team needed a more efficient way to manage multiple systematic literature reviews across rapidly evolving evidence bases. LitVerse transformed isolated, point-in-time reviews into a living SLR workflow, automating evidence updates and reducing the manual effort required to keep reviews current.