A payer meeting is confirmed for next week, and the market access lead needs a current evidence summary covering efficacy, safety, comparative effectiveness, and budget impact for a specific patient population the payer has flagged as their primary concern. The standard evidence package took three months to assemble for the original submission. There are no three months available now. This is the situation that rapid evidence reviews exist to solve, and it is becoming a more frequent and more time-pressured situation across pharma market access functions as payer engagement accelerates and managed access review cycles compress.
At Pienomial, we built KnolAI as the AI research platform that makes rapid evidence review payer submission AI achievable without compromising the rigour that payer and HTA reviewers require. Nested Knowledge's 2025 case study demonstrated that AI-assisted rapid reviews can now be completed in as little as three hours, with the majority of remaining time spent on interpretation and presentation rather than the screening and extraction work that traditionally consumes weeks of analyst effort. [1] This post explains how to perform a rapid evidence review for a payer submission that is both fast and methodologically defensible, and how KnolAI delivers this capability as part of the broader Knolens enterprise intelligence platform.[9]
1. What a Rapid Evidence Review Is and When You Need One
A rapid evidence review is a streamlined evidence synthesis methodology designed to answer a specific, focused research question within a compressed timeline, typically days rather than the months required for a comprehensive systematic literature review. Rapid reviews are not a lower-quality substitute for a full SLR. They are a different methodology, appropriate for a different decision context, with their own established standards.[3]
The most common pharma market access triggers for a rapid evidence review include: a payer meeting scheduled with limited preparation time where the payer has flagged a specific evidence concern, an HTA body clarification request requiring a focused evidence update within a four-to-six-week response window, a formulary committee review requiring an updated competitive evidence comparison, and a managed access agreement review point requiring a current evidence snapshot for a specific outcome measure. In each case, the decision-maker needs current, accurate, sourced evidence quickly, and a twelve-week SLR project is not a viable response.[2]
Several HTA bodies have formally recognised the value of rapid review methodologies. NICE has trialled more rapid appraisals for cancer treatments, and CADTH, now Canada's Drug Agency, has piloted expedited review processes for specific product categories. Ireland has its own established rapid review pathway, and Scotland operates abbreviated submission routes. [5] These formal pathways reflect a broader recognition that rigorous evidence synthesis does not require months when the methodology is structured for speed without sacrificing systematic rigour.[2]
2. Why Manual Rapid Reviews Still Take Too Long
The core challenge of conducting a rapid evidence review manually is that the time-consuming components of evidence synthesis, database searching, title and abstract screening, full-text retrieval and screening, and data extraction, do not compress proportionally just because the deadline is shorter. A rapid review conducted by a skilled HEOR team manually still requires days of screening effort for a moderately sized literature set, and the screening burden scales with the number of records returned by the search, not with the time available to complete the review.[8]
Research from Canada's National Collaborating Centre for Methods and Tools documented this challenge directly: given the vast quantity of literature available, the key obstacle in rapid evidence synthesis is the time and effort required to manually screen large result sets within an accelerated timeline. Their solution was to integrate AI into the title and abstract screening stage specifically, reducing the manual screening burden and enabling more timely access to high-quality synthesis evidence. [8] This is the right instinct, but partial AI integration at a single stage leaves the full-text screening, extraction, and synthesis stages still bottlenecked by manual effort.
3. The KnolAI Rapid Review Workflow: From Question to Evidence Summary
KnolAI applies AI across every stage of the rapid review process, not just abstract screening, which is what enables the compression from weeks to hours rather than weeks to days. The workflow operates in five stages, each building on the Knolens knowledge layer.[9]
Stage 1, Focused question definition: The HEOR analyst defines the specific question the payer or HTA body needs answered, structured as a focused PICOS framework. Because this is a rapid review, the scope is deliberately narrower than a comprehensive SLR: a specific patient subgroup, a specific comparator, or a specific outcome measure, rather than the full evidence landscape for the indication.
Stage 2, Accelerated search and retrieval: KnolAI executes the search across the relevant databases within the Knolens knowledge layer, which is already populated with continuously updated, validated clinical literature for the indication if a living evidence base has been established, or runs a fresh targeted search if not. Because the knowledge layer is pre-validated, search execution and initial relevance filtering happen in minutes rather than the hours required for a from-scratch database search.[9]
Stage 3, Automated screening with dual-validation: Title, abstract, and full-text screening run against the focused PICOS criteria with simulated dual-reviewer validation and kappa calculation, satisfying methodological rigour requirements even within the compressed timeline. This is the stage where AI delivers the most dramatic time compression: a screening task that takes a human reviewer days takes KnolAI minutes, with the inter-rater reliability documentation generated automatically.[1]
Stage 4, Structured extraction with source attribution: Data extraction from included studies runs with sentence-level source attribution, producing a structured evidence table where every efficacy figure, safety signal, and comparative data point links to its specific source location. This is not a summary. It is a sourced evidence table ready for direct use in a payer presentation or a clarification response.
Stage 5, Synthesis and presentation-ready output: KnolComposer generates the narrative synthesis and presentation-ready summary from the structured evidence table, formatted for the specific audience, whether a payer committee, an HTA reviewer, or an internal market access team. The HEOR analyst's remaining work is interpretation and strategic framing, exactly the high-value activity that the Nested Knowledge case study found teams spending their time on once AI handled the mechanical screening and extraction work.[1][9]
4. Maintaining Methodological Rigour Under Time Pressure
The risk in any rapid review methodology, AI-assisted or manual, is that speed compromises rigour in ways that undermine the credibility of the evidence with the payer or HTA reviewer who receives it. A rapid review that cuts corners on methodology documentation, search reproducibility, or source verification produces evidence that a sophisticated reviewer will discount, defeating the purpose of the exercise.[6]
KnolAI maintains rigour under time pressure through three structural safeguards. First, every search string executed is documented and reproducible, regardless of the timeline, because the search execution is automated and logged by default rather than manually conducted and inconsistently recorded. Second, every screening decision carries its inclusion or exclusion rationale and is logged in the audit trail, so the rapid review methodology can be reconstructed and verified even though it was completed in hours. Third, every extracted data point carries claim-level source attribution, meaning the speed of the review does not come at the cost of the traceability that payer and HTA reviewers require.[9]
This combination, speed without sacrificing documentation rigour, is precisely what distinguishes a defensible rapid review from a shortcut. A payer or HTA reviewer examining a KnolAI-generated rapid review sees the same source attribution standard as a comprehensive SLR, delivered on a compressed timeline because the mechanical bottlenecks of search, screening, and extraction have been automated, not because the evidence standard was lowered.[4]
5. The AI Disclosure Question: What to Tell the Payer or HTA Body
A notable finding in current HTA practice is that AI use in evidence generation remains uncommon in formal disclosures and may not be openly communicated by manufacturers, often due to concern about non-acceptance of AI-derived methodologies. [7] This caution is becoming less necessary as HTA bodies formalise their AI guidance: NICE published its position statement in 2024, and Canada's Drug Agency published an adapted version of NICE's statement in 2025. [4] The direction of travel is toward explicit AI disclosure requirements, not toward AI avoidance.
At Pienomial, our recommendation is transparency by default. A rapid evidence review generated with KnolAI carries a complete audit trail documenting the search methodology, screening criteria, inter-rater reliability calculation, and extraction methodology, exactly the documentation that satisfies NICE's PALISADE and TRIPOD+AI checklist requirements when AI is disclosed. [4] Because the methodology is fully reproducible and the evidence chain is fully sourced, disclosure of AI use strengthens rather than weakens the credibility of the rapid review: it demonstrates that the speed of the review came from process automation, not from cut corners.[9]
6. Rapid Review for Payer Budget Impact Questions
Payer committees frequently request rapid evidence on a specific budget impact or formulary positioning question that was not the primary focus of the original HTA submission. A formulary committee may ask specifically about real-world treatment persistence data, total cost of care comparisons against the current standard of care, or outcomes in a specific comorbidity subgroup that the original submission did not address in depth.[2]
KnolAI's rapid review capability extends beyond clinical efficacy questions to these payer-specific evidence requests. A budget impact rapid review draws on the same Knolens knowledge layer used for clinical evidence, but the PICOS framework is structured around cost and resource utilisation outcomes rather than clinical endpoints. The search includes claims database literature, real-world treatment pattern studies, and health economic publications relevant to the specific payer question. The output is formatted as a payer-ready evidence brief with the budget impact data sourced and attributed at the same standard as a clinical evidence rapid review.[9]
7. Rapid Review for Managed Access Agreement Checkpoints
NICE and other HTA bodies increasingly use managed access agreements with defined evidence review checkpoints at 12 to 36-month intervals. Each checkpoint requires a current evidence update, and the turnaround time between the checkpoint notification and the required evidence submission is often measured in weeks, not months.[5]
For products with a living evidence base already established in Knolens, the MAA checkpoint rapid review is dramatically accelerated because the underlying evidence layer has already been continuously updated since the prior checkpoint. KnolAI generates the checkpoint evidence summary by querying the current state of the living evidence base for the specific outcome measures the MAA requires, rather than conducting a search from scratch. For products without a pre-established living evidence base, KnolAI runs a full rapid review workflow against the MAA-specified PICOS criteria within the checkpoint timeline.[9]
8. Common Mistakes in Rapid Evidence Reviews and How to Avoid Them
Five mistakes consistently undermine the credibility or usefulness of rapid evidence reviews, whether conducted manually or with AI assistance.[3]
Mistake 1, Scope creep: A rapid review that attempts to answer too broad a question loses the speed advantage that makes it rapid. The PICOS framework for a rapid review should be deliberately narrower than a comprehensive SLR, focused on the specific decision the payer or reviewer needs to make.
Mistake 2, Undocumented search methodology: Even under time pressure, the search strings, databases searched, and date of execution must be documented. A rapid review that cannot demonstrate its search methodology invites the same credibility challenge as an undocumented comprehensive SLR.[6]
Mistake 3, Single-pass screening without quality checks: Speed does not exempt a rapid review from basic quality assurance. KnolAI's simulated dual-screen validation with kappa calculation provides this quality check even within a compressed timeline.
Mistake 4, Presenting rapid review findings as comprehensive: A rapid review answers a focused question. It should be clearly labelled as a rapid review with its defined scope, not presented as if it represents the full evidence landscape for the indication. Mislabelling creates credibility risk if a reviewer identifies evidence outside the rapid review's deliberately narrow scope.
Mistake 5, No connection to the broader evidence base: A rapid review conducted in isolation, without connecting its findings to the organisation's broader evidence architecture, produces a one-time document rather than a contribution to institutional knowledge. KnolAI's rapid review findings are added to the Knolens knowledge layer as validated entity-relationship triples, available for future queries rather than disappearing into a standalone document.[9]
9. How Fast Can Your Team Run a Rapid Evidence Review with KnolAI?
The honest answer to how fast a rapid evidence review can be completed depends on the complexity of the question and the state of your organisation's existing knowledge layer, but the Nested Knowledge case study's finding that AI-assisted rapid reviews can be completed in as little as three hours reflects what is achievable when the AI handles search, screening, and extraction comprehensively. [1] KnolAI is built to deliver this speed as a standard capability, not a best-case outlier.[9]
Same-day turnaround, Focused single-question rapid reviews: For a narrowly scoped question against an indication where the Knolens knowledge layer is already populated, KnolAI can deliver a sourced evidence summary within hours of the request, covering search execution, screening, extraction, and synthesis in a single automated workflow with human review of the final output before distribution.
Two to three day turnaround, Rapid reviews requiring fresh literature search: For indications without a pre-established living evidence base, or for questions requiring a broader literature search than the existing knowledge layer covers, KnolAI completes the full search, screening, and extraction workflow within two to three days, still substantially faster than the multi-week timeline of a manual rapid review process.
Ongoing, Living evidence base eliminates the rapid review bottleneck entirely: For products with an established Knolens living evidence base, most rapid review requests are answered by querying the continuously updated knowledge layer directly, with no new search or screening cycle required. The rapid review becomes a query against current intelligence rather than a new evidence synthesis project.[9]
Conclusion
The pressure on pharma market access and HEOR teams to deliver current, defensible evidence on compressed timelines is increasing, driven by faster-moving payer engagement cycles, formal HTA rapid review pathways, and managed access agreement checkpoints with limited turnaround windows. A rapid evidence review payer submission AI capability that maintains full methodological rigour while compressing the timeline from weeks to hours is no longer a nice-to-have efficiency gain. It is becoming an operational requirement for market access functions that need to respond to payer questions at the speed payers now expect.
At Pienomial, we built KnolAI as part of the Knolens enterprise intelligence platform specifically to make this speed achievable without the credibility trade-off that manual rapid reviews and shortcut methodologies create. Every KnolAI rapid review carries the same source attribution and audit trail standard as a comprehensive SLR, delivered on a timeline that matches the speed at which payer and HTA decisions now move. [9] CTA: See how KnolAI delivers payer-ready rapid evidence reviews in hours. Book a demo with the Pienomial team.
