Value-based healthcare is ranked third on ISPOR's 2026 to 2027 Top 10 HEOR Trends report, a recognition that the global shift from volume-based to outcomes-based reimbursement is no longer a future direction. It is the operational reality that pharma market access teams are navigating today. [1] Health systems facing financial and workforce pressure are actively restructuring incentives around patient outcomes rather than service volume. For pharma organisations, this is not a policy trend to monitor. It is a direct mandate to produce evidence that speaks the language of outcomes, costs, and patient-reported benefit in terms that payers can evaluate and act on.
The evidence architecture required for VBHC is structurally different from the trial-plus-HTA model that has driven HEOR for the past two decades. It requires PRO data, RWE on real-world effectiveness, health economic evidence that survives payer scrutiny, and a continuous monitoring infrastructure that supports outcomes-based contracts throughout the product lifecycle. Pienomial's Knolens platform provides the enterprise knowledge layer and market access analytics platform infrastructure that enables HEOR teams to build, maintain, and deploy this evidence architecture at the speed and quality that VBHC demands.[9]
1. What Value-Based Healthcare Actually Demands from Pharma Evidence
The core principle of VBHC is straightforward: reimbursement and access should be tied to demonstrated patient outcomes in real-world practice, not to clinical trial efficacy alone. For pharma organisations, this principle translates into three concrete evidence obligations that the traditional HTA dossier model does not fully satisfy.[1]
First, outcomes measurement beyond trial endpoints. VBHC requires data on overall survival, quality of life, functional status, and healthcare resource utilisation from patients treated outside controlled trial conditions, in the broader population, over longer time horizons, and with the comorbidity profiles typical of the health system in question. Second, population relevance. Trial populations are selected and restricted. Payers want outcomes data for their actual patient population, not the ideal patient defined by trial inclusion criteria. Third, temporal scope. Trial follow-up is typically two to three years. VBHC evidence requirements extend to the full product lifecycle, because outcomes-based contracts and managed access agreements require evidence that is generated and updated continuously, not compiled once at submission.[2]
CMS is targeting 100% of Medicare beneficiaries in alternative payment models by 2030, making VBHC not just a European HTA concern but a defining feature of the US market access landscape as well. [3] HEOR teams that have not yet restructured their evidence generation programmes around VBHC requirements are already building towards a gap that will become visible at the moment of payer negotiation.
2. The Five Components of a VBHC Evidence Architecture
A complete VBHC evidence architecture requires five interconnected evidence components, each of which must be continuously maintained rather than compiled once. The traditional HEOR model, which produces a static evidence package at the time of HTA submission, is insufficient for VBHC because payer requirements evolve, outcome data accumulates, and contracts require performance monitoring.[8]
Component 1, Patient-reported outcomes evidence: Validated PRO instruments with EQ-5D utility values for cost-effectiveness modelling, condition-specific outcome measures for HTA body clinical review, and real-world PRO data from patients treated in practice rather than trial conditions. AI synthesis of PRO evidence across multiple source types, including trial sub-studies, observational studies, and validation publications, is essential for building a comprehensive PRO evidence base.
Component 2, Real-world effectiveness evidence: Comparative effectiveness in the actual prescribing population, treatment persistence and adherence data, healthcare resource utilisation including hospitalisation rates and outpatient visits, and long-term outcome data beyond trial follow-up. Each of these evidence types requires different data sources, different analytical methodologies, and different documentation standards for HTA submission.
Component 3, Economic outcomes evidence: Direct medical cost data covering drug acquisition, administration, adverse event management, and hospitalisation; indirect cost data including productivity loss and caregiver burden; and a budget impact model calibrated to each specific payer system rather than a generic health system model.
Component 4, Long-term outcomes evidence: Survival extrapolation models grounded in RWE; long-term safety and tolerability data from post-market surveillance; patient outcomes over the full disease course, including disease progression, treatment switching, and end-of-life resource use.
Component 5, Outcomes-based contract framework: Pre-specified, measurable outcome endpoints; agreed data sources and analysis methodology; a monitoring infrastructure that tracks performance continuously; and an adjustment mechanism that responds when outcomes deviate from the contracted prediction.[4]
3. Outcomes-Based Contracts: The Evidence Infrastructure They Require
Outcomes-based contracts and managed entry agreements are expanding across European and US markets. Research published in the Journal of Managed Care and Specialty Pharmacy found that nearly all payers and pharmaceutical manufacturers report positive value from OBCs, with 2 to 3 times more contracts expected to be implemented in the following five years across EU-5 and US markets. [4] The OECD has documented performance-based MEAs across its member states as a standard mechanism for managing payer uncertainty about the real-world value of new treatments.[6]
However, the implementation challenges are substantial. Research on outcomes-based contract implementation in Europe identified that the most significant barrier is not the willingness of either party to enter into agreements, but the infrastructure required to generate, collect, and analyse the outcome data on which the contract depends. [7] Italian AIFA data from the most established MEA programme in Europe shows that registry data quality has historically been insufficient for meaningful outcomes analysis, with one study finding that AIFA MEAs allowed recovery of only approximately 5% of total drug expenditure through payback mechanisms.[5]
These implementation challenges are fundamentally evidence infrastructure challenges. An OBC is only as strong as the data architecture supporting it. HEOR teams that arrive at OBC negotiations with pre-built, validated evidence infrastructure, including agreed data sources, pre-specified endpoints, and a monitoring infrastructure already in place, negotiate from a position of commercial strength. Those that arrive without this infrastructure concede contract terms to the payer by default.
4. AI-Powered PRO Synthesis for VBHC Evidence
PRO evidence synthesis is one of the most time-consuming components of VBHC evidence preparation. PRO data exists across multiple heterogeneous sources: clinical trial PRO sub-studies, published observational PRO studies, PRO validation publications, health-related quality of life mapping studies, and patient registry data. Manual synthesis across these sources is slow, inconsistent, and rarely produces a comprehensive evidence base that covers all relevant instruments and populations.[8]
KnolAI, the research module of the Knolens enterprise knowledge layer platform, searches across published PRO literature using structured PICOTS queries, extracts utility values, HRQoL scores, and mapping function parameters with sentence-level source attribution, identifies methodological quality issues including instrument version, translation validation, and missing data handling, and generates a structured PRO evidence table in the format required for cost-effectiveness model input.
Automated EQ-5D mapping is a specific AI capability that addresses one of the most common HEOR evidence gaps. When a product's trial used a non-preference-based PRO instrument and direct utility data is unavailable, the HEOR team must apply a validated mapping algorithm to convert the trial PRO data to EQ-5D utility values. KnolAI identifies the best available mapping function for a given instrument in a given indication, retrieves the mapping parameters with source attribution, applies uncertainty characterisation reflecting the mapping uncertainty, and documents the full methodology chain from trial PRO data through mapped utility values to cost-effectiveness model input.
Health economic model quality depends on the quality of evidence inputs. The most common HTA rejection reasons for cost-effectiveness models include transition probabilities from outdated or methodologically weak studies, utility values mapped from non-EQ-5D instruments without validated mapping functions, and comparator treatment effects from indirect comparisons without appropriate uncertainty characterisation. Each of these failures is an evidence sourcing failure, not a modelling failure.[1]
KnolAI addresses these failures by building a structured model input pack where every parameter is sourced from the best available evidence. For each model input category, KnolAI searches the knowledge layer for the highest-quality available data, documents the search methodology and quality assessment, and flags inputs where evidence quality is limited, enabling the modelling team to characterise uncertainty appropriately rather than discover data limitations during the HTA review process.
The life sciences AI platform advantage for cost-effectiveness modelling is that the model input pack is not static. As new clinical publications, registry data, or RWE analyses become available, KnolAI identifies updates relevant to existing model parameters and alerts the HEOR team. The model input pack reflects the current best evidence at each submission rather than the evidence available at the time of initial model construction, which is critical for products with ongoing post-market evidence generation and for submissions to multiple HTA bodies on staggered timelines.
6. Building Payer-Specific Value Narratives with AI
Different players weigh different outcomes differently, and the VBHC shift has not produced a single universal value framework. NICE weights QALY gain and cost-effectiveness. G-BA weights clinical benefit against the ZVT comparator. US commercial payers weight total cost of care and formulary management impact. CMS weights Medicare budget impact. Each of these frameworks requires a different framing of the same underlying clinical and economic evidence.[3]
KnolComposer, the authoring module of the Knolens platform, generates payer-specific value narratives from a shared evidence layer. For each payer, the relevant evidence modules are retrieved, including QALY data for NICE, OS and QoL data for G-BA, budget impact data for CMS, and total cost of care data for US commercial payers, and assembled into a structured, traceable value story. The human expert role is to validate the payer-specific framing and add qualitative context drawn from payer relationship knowledge and contracting history.
The consistency safeguard is critical. Because all payer-specific narratives draw from the same underlying evidence layer, the clinical facts are identical across submissions. Contextualisation, the adaptation of framing, language, and outcome weighting to each payer's framework, occurs at the output layer. A diligent payer reviewer comparing submissions across markets will find consistent clinical numbers attributed to consistent sources, not inconsistent claims suggesting that different evidence was used for different audiences.
7. RWE as the VBHC Evidence Foundation
VBHC evidence requirements are fundamentally real-world evidence requirements. The outcomes that matter to payers, including real-world effectiveness, treatment persistence, quality of life in practice, and total cost of care, are only measurable in real-world data. Trial efficacy data is the starting point, not the endpoint, of the VBHC evidence case.[2]
The RWE evidence agenda for a VBHC evidence strategy covers four areas. Real-world comparative effectiveness: propensity-score matched or adjusted analyses comparing the product against the actual real-world standard of care using claims or EHR data. Treatment persistence and adherence: claims-based analysis of time to discontinuation, dose modifications, and restart patterns in the treated patient population. Healthcare resource utilisation: hospitalisation rates, emergency department visits, and outpatient visits by treatment group, providing the data inputs for payer budget impact modelling. Long-term outcomes: registry linkage to survival data for products with limited trial follow-up, addressing the extrapolation uncertainty that is the most common driver of conditional or restricted reimbursement.[4]
KnolAI maps available RWE data sources to each evidence category, assesses data quality and coverage completeness, and generates a prioritised RWE evidence plan with feasibility assessment and timeline recommendations. For HEOR teams building VBHC evidence programmes, this structured evidence plan is the starting point for both retrospective data analysis and prospective study commissioning decisions.
8. Integrating VBHC Evidence into Medical Affairs Strategy
Medical Affairs is the bridge between HEOR evidence and payer decision-making. In the VBHC era, this bridge must be dynamic: as new RWE is published, as OBC performance data accumulates, and as new HTA decisions in the indication are issued, the value evidence base evolves. Medical Affairs teams that operate from static evidence packages, updated annually if at all, are systematically under-equipped for payer interactions in a VBHC environment.[8]
Knolens enables a living VBHC evidence capability for Medical Affairs. Continuous monitoring of new RWE publications, HTA decisions, and outcomes data in the indication keeps the evidence layer current. Evidence modules update automatically as new data is validated. Medical Affairs-ready summaries are generated on demand, with full source attribution, tailored to the specific payer's formulary decision framework.
The use case is practical: a payer meeting is scheduled for the coming week. The Medical Affairs lead requests a current value narrative from the Knolens platform, specifying the payer, their therapeutic area focus, and the key uncertainty they have expressed in prior interactions. KnolAI retrieves the current best evidence on each relevant value dimension, KnolComposer generates a structured, traceable narrative in the appropriate format, and the Medical Affairs lead arrives at the meeting with current, attributed evidence rather than a static slide deck from the last dossier submission cycle.[9]
9. How Fast Can Your Team Build a VBHC Evidence Programme with Knolens?
Building a VBHC evidence programme does not require a lengthy internal design phase before you see value. Knolens ships with pre-built PRO synthesis pipelines, pre-configured RWE source mapping templates, and ready-to-run payer-specific value narrative frameworks for NICE, G-BA, HAS, CMS, and US commercial payers. The product is already structured for VBHC. You are configuring it to your indication, not building it from scratch.
Most HEOR teams are running their first live VBHC evidence output within six weeks of onboarding. Here is what that looks like.
Sprint 1, Weeks 1 to 2, VBHC evidence audit and first gap output live: Knolens is connected to your indication and target payer markets. KnolAI runs the first automated VBHC evidence audit, assessing your current evidence assets against the five VBHC evidence components and identifying gaps by payer and evidence type. Your team receives a structured gap report within days showing exactly which PRO, RWE, economic, and long-term outcomes evidence is missing for each target market. No internal evidence cataloguing exercise required. [9]
Sprint 2, Weeks 3 to 4, RWE mapping and first PRO synthesis: KnolAI maps available RWE data sources to each evidence gap for your indication, assessing data quality, coverage completeness, and feasibility. Simultaneously, the first PRO synthesis runs across published PRO literature for your indication, with automated EQ-5D mapping where direct utility data is unavailable. Model input packs are pre-populated with the best available sourced evidence for each cost-effectiveness model parameter. [8]
Sprint 3, Weeks 5 to 6, Payer-specific value narratives and OBC framework configured: KnolComposer generates the first set of payer-specific value narratives from the synthesised evidence layer, formatted for your priority payer markets. For markets where outcomes-based contracting is required or expected, the OBC endpoint specification document and data source agreement template are generated from the evidence available in the knowledge layer. Cross-market consistency verification runs automatically before any narrative is finalised.
From Sprint 3 onward, Knolens operates as a living VBHC evidence layer. New RWE publications, HTA decisions, and OBC performance data are ingested continuously. Evidence modules update as new data is validated. Medical Affairs leads can request a current, payer-specific value narrative on demand before any payer meeting, drawn from the same governed evidence base that supports the formal HTA submission. [6]
Conclusion
Value-based healthcare is not a future aspiration for pharma market access. It is the operational standard that players in Germany, Italy, England, and increasingly the United States are applying today. HEOR teams that build the evidence architecture proactively, before payers request it, will achieve faster, broader, and more durable market access outcomes than those that respond to payer requests from a static evidence base.[1]
Pienomial's Knolens platform provides the enterprise knowledge layer and market access analytics platform infrastructure to build, synthesise, and continuously maintain the VBHC evidence base that modern payers require. From PRO synthesis through OBC performance monitoring, every evidence output is structured for payer scrutiny and governed by a knowledge layer that updates continuously as the landscape evolves. [9] CTA: Book a VBHC Evidence Architecture Consultation with Pienomial's HEOR Team.
