Market access teams are under increasing pressure to demonstrate the value of new medicines with evidence that is clinically credible, economically relevant, and understandable to different healthcare decision-makers.

The challenge is that payer evidence generation can involve large amounts of clinical, economic, epidemiological, patient, and real-world data. Teams may need to review published literature, analyse comparators, understand treatment pathways, estimate resource utilisation, assess outcomes, and prepare evidence for health technology assessment (HTA) and payer discussions. A market access analytics platform can help teams bring these evidence sources together and support more efficient, data-driven decision-making.

Artificial intelligence is beginning to change how this work is performed.

AI in market access does not mean allowing an algorithm to make reimbursement decisions. Instead, AI can help market access and HEOR professionals find, structure, compare, and analyse evidence faster while keeping scientific and methodological judgment with experienced teams.

ISPOR identifies AI as its top HEOR trend for 2026–2027 and notes that AI is increasingly being applied across HEOR, including systematic literature reviews, complex dataset analysis, and evidence generation. At the same time, ISPOR emphasises the importance of human oversight. [1]

For pharmaceutical organisations, the opportunity is significant: AI can shorten repetitive evidence-generation activities and give teams more time to focus on interpretation, strategy, and payer relevance.

What Is Payer Evidence Generation?

Payer evidence generation is the process of developing and organising evidence that helps demonstrate the clinical, economic, and broader value of a healthcare intervention.

Depending on the product and market, evidence may include:

• Clinical efficacy and safety

• Comparative effectiveness

• Quality of life

• Patient-reported outcomes

• Epidemiology

• Healthcare resource utilisation

• Treatment patterns

• Cost of illness

• Budget impact

• Cost-effectiveness

• Real-world outcomes

• Unmet medical need

• Patient preferences

Different markets and decision-makers can place different emphasis on these evidence categories.

A regulator may focus primarily on safety and efficacy. An HTA body may examine comparative effectiveness and cost-effectiveness. A payer may also be concerned with budget impact, treatment pathways, utilisation, and affordability.

This makes evidence generation a multidimensional process.

AI can help teams manage this complexity by connecting evidence from multiple sources and accelerating the analysis required to turn that evidence into usable insights.

Why AI Is Becoming Important in Market Access

Traditional market access workflows often involve substantial manual research.

Analysts may need to:

1. Search scientific literature.

2. Screen publications.

3. Extract relevant findings.

4. Identify comparators.

5. Review epidemiological evidence.

6. Analyse treatment pathways.

7. Gather economic data.

8. Review HTA decisions.

9. Identify payer-relevant outcomes.

10. Prepare evidence summaries and dossiers.

Many of these activities require expert judgment, but not every step requires the same amount of manual effort.

AI can help automate or accelerate repetitive activities while allowing experts to remain responsible for the final interpretation.

This is consistent with broader developments in HEOR. An ISPOR good-practices task force identified several areas where machine learning can enhance HEOR, including cohort selection, prediction of health outcomes, causal inference, and economic modelling. [2]

The practical opportunity is therefore not simply "use AI."

It is to identify where AI can reduce research friction without compromising evidence quality.

Five Ways AI Speeds Up Payer Evidence Generation

1. AI Accelerates Systematic Literature Review

Literature review is one of the most time-consuming components of evidence generation.

Market access and HEOR teams may need to identify studies across multiple databases and then screen large numbers of publications against predefined inclusion and exclusion criteria.

AI can assist with:

• Literature discovery

• Deduplication

• Title and abstract screening

• Relevance classification

• Study categorisation

• Data extraction

• Evidence summarisation

• Identification of evidence gaps

For example, an AI system can classify publications according to population, intervention, comparator, outcomes, study design, and geography.

This does not eliminate expert review.

Instead, it can reduce the amount of repetitive screening that researchers need to perform manually.

AI can also help identify relationships across studies that may be difficult to see when publications are reviewed individually.

This becomes particularly useful when evidence is spread across clinical trials, observational studies, economic evaluations, and patient-outcome research.

ISPOR's current AI and HEOR work recognises systematic literature reviews as an important area where generative AI can improve efficiency. [3]

The key requirement is traceability.

A market access professional should be able to identify the source supporting an extracted finding rather than relying on an unsupported AI-generated statement.

2. AI Speeds Up Evidence Extraction and Synthesis

Finding evidence is only part of the challenge.

Teams also need to understand what the evidence says.

A typical payer evidence project can involve dozens or hundreds of documents containing information about:

• Patient populations

• Treatment outcomes

• Comparators

• Adverse events

• Duration of therapy

• Healthcare utilisation

• Costs

• Quality-of-life outcomes

• Treatment discontinuation

• Subgroup outcomes

AI can help extract these variables and organise them into structured evidence sets.

For example, a team assessing a new oncology therapy could ask an AI system to identify how relevant studies report progression-free survival, overall survival, treatment discontinuation, and quality-of-life outcomes.

The system can then organise findings across studies for human review.

This can significantly reduce the time required to build an initial evidence map.

The distinction between extraction and interpretation remains important.

AI can identify what a study reports.

An HEOR expert must still determine whether the evidence is comparable, methodologically appropriate, and relevant to the decision being addressed.

3. AI Helps Build and Refine Economic Evidence

Economic evidence is another area where AI can potentially reduce analytical workload.

HEOR teams may develop:

• Cost-effectiveness models

• Budget impact models

• Cost-of-illness studies

• Resource-utilisation analyses

• Treatment pathway models

• Scenario analyses

Machine learning has already been identified as potentially useful in economic modelling, including approaches that can help address structural, parameter, and sampling uncertainty in cost-effectiveness analysis. [2]

AI can support the surrounding workflow by helping teams:

• Identify model inputs

• Structure evidence

• Compare assumptions

• Extract resource-use estimates

• Identify relevant cost sources

• Explore scenarios

• Document assumptions

• Detect inconsistencies

This can be particularly useful when model inputs come from multiple evidence sources.

However, economic models are not simply data-processing exercises.

They involve assumptions about treatment pathways, comparators, outcomes, costs, time horizons, discounting, uncertainty, and other methodological choices.

AI should therefore support modelling rather than operate as an unchecked substitute for HEOR expertise.

4. AI Connects Real-World Evidence With Payer Questions

Real-world evidence can be highly valuable for market access because it can provide insight into how treatments perform outside controlled clinical-trial environments.

Teams may need to examine:

• Patient characteristics

• Treatment patterns

• Adherence

• Persistence

• Healthcare utilisation

• Hospitalisations

• Resource consumption

• Outcomes

• Treatment switching

• Patient-reported outcomes

AI and machine learning can help analyse large real-world datasets and identify relevant patterns.

ISPOR has highlighted the use of machine learning in HEOR for real-world data analysis supporting market access. [4]

For market access teams, the important question is not simply:

What does the real-world dataset show?

It is:

Which findings are relevant to the payer's decision?

For example, an analysis may show differences in healthcare resource utilisation between treatment groups.

A market access team may then evaluate whether those differences could affect:

• Total cost of care

• Budget impact

• Hospitalisation rates

• Treatment persistence

• Patient outcomes

5. AI Accelerates Payer and HTA Evidence Dossier Development

Evidence often needs to be translated into structured outputs for different stakeholders.

These may include:

• Value dossiers

• Payer evidence summaries

• HTA submissions

• Evidence tables

• Executive summaries

• Literature reviews

• Budget impact documentation

• Medical and economic evidence packages

For example, an AI workflow can help connect:

Clinical evidence → comparative evidence → economic evidence → patient outcomes → payer implications

This creates a more connected evidence-generation process.

A strong system should also preserve the source behind each important statement.

This is essential because a polished dossier is only valuable if its claims can be traced back to credible evidence.

What Should a Market Access Analytics Platform Provide?

A market access analytics platform should do more than provide a dashboard.

The most useful platforms connect data, evidence, analytics, and workflow.

Important capabilities can include:

Evidence Discovery

Search and organise clinical, economic, epidemiological, and real-world evidence.

Evidence Extraction

Identify relevant information from large document collections.

Evidence Mapping

Connect evidence to products, populations, comparators, outcomes, and markets.

Analytics

Support analysis of clinical, economic, and real-world data.

Scenario Analysis

Allow teams to explore different assumptions and potential outcomes.

Source Traceability

Show where important findings originated.

Collaboration

Allow HEOR, market access, medical, and strategy teams to work from shared evidence.

Workflow Management

Connect research activities with ongoing evidence-generation projects.

AI in Market Access Should Be Evidence-First

One of the most important principles for using AI in market access is to make evidence the foundation of the workflow.

AI-generated content can be useful, but it should not become the evidence itself.

For example, if an AI system states that a therapy reduced hospitalisations, a user should be able to identify:

• The underlying study

• The population studied

• The comparator

• The outcome definition

• The analysis period

• The relevant numerical result

• Any important limitations

This is particularly important for payer-facing evidence.

Payers and HTA organisations need evidence they can assess, not simply conclusions generated by software.

NICE has specifically stated that AI methods used in evidence generation and reporting should have a clear rationale and that their potential benefits need to be balanced against risks such as bias, cybersecurity, reduced human oversight, transparency, and accessibility. [5]

This reinforces an important principle:

The faster AI makes evidence generation, the more important evidence governance becomes.

Human Oversight Remains Essential

AI can accelerate evidence generation, but it cannot independently determine whether evidence is appropriate for every payer or HTA context.

Experts still need to assess:

• Study quality

• Applicability

• Generalisability

• Bias

• Statistical validity

• Comparator relevance

• Clinical relevance

• Economic assumptions

• Uncertainty

• Payer requirements

The appropriate role of AI is therefore often human-in-the-loop.

AI performs the high-volume work.

Experts perform the high-judgment work.

This operating model can increase productivity without weakening methodological standards.

FDA's January 2026 principles for good AI practice in drug development similarly emphasise human-centric design, risk-based approaches, clear context of use, data governance, performance assessment, and lifecycle management. [6]

AI and Cross-Market Evidence Generation

Market access teams increasingly need to prepare evidence for multiple countries.

Different markets can have different:

• HTA methodologies

• Payer expectations

• Evidence requirements

• Comparator definitions

• Cost inputs

• Treatment pathways

• Submission processes

AI can help teams reuse and adapt evidence without starting every analysis from scratch.

For example, a central evidence repository can provide a structured view of:

Core evidence → country-specific evidence → local economic inputs → payer requirements → submission outputs

This can make global evidence strategies more efficient.

However, localisation remains essential.

A study that is relevant in one market may not directly answer the questions asked by another HTA body or payer.

AI should therefore help identify what needs to change rather than automatically assuming that one evidence package fits every market.

Payer Evidence Generation Software: What to Look For

When selecting payer evidence generation software, pharmaceutical organisations should evaluate the entire evidence lifecycle.

1. Evidence Coverage

Can the platform handle clinical, economic, epidemiological, and real-world evidence?

2. Source Quality

Can users verify information against authoritative sources?

3. AI Transparency

Can users understand how AI-generated outputs were produced?

4. Data Governance

Are appropriate controls available for sensitive information?

5. Reproducibility

Can important analyses and outputs be recreated?

6. Collaboration

Can HEOR, market access, medical, and regulatory teams work from shared evidence?

7. Global Scalability

Can the platform support multiple products and markets?

8. Integration

Can it connect with existing data and analytical environments?

9. Auditability

Can teams maintain records of important evidence and analytical decisions?

10. Human Review

Can experts review, edit, validate, and approve AI-assisted outputs?

These capabilities are often more important than simply having the latest generative AI model.

How Pienomial Can Support AI-Enabled Market Access

Pienomial provides an AI-powered intelligence environment for organisations working with complex information.

For life sciences organisations, its life sciences solution can support workflows that require teams to connect scientific, clinical, competitive, and market information.

For market access and HEOR teams, this type of environment can help organise evidence, accelerate information discovery, and support analysis across large knowledge collections.

Pienomial's broader platform approach can also help organisations create connected intelligence workflows instead of maintaining isolated research activities across multiple systems.

The value is particularly relevant when evidence needs to move across functions.

A clinical development team may generate one type of evidence.

An HEOR team may analyse another.

Market access may need to translate those findings into payer-relevant value.

A connected AI environment can help teams maintain a common evidence foundation while allowing each function to focus on its specific objectives.

Building a Responsible AI Workflow for HEOR

Organisations adopting AI for HEOR should establish governance alongside implementation.

A practical framework can include six steps.

Step 1: Define the Use Case

Determine exactly what the AI system is expected to do.

Step 2: Establish Evidence Sources

Identify which sources are approved for the workflow.

Step 3: Set Performance Requirements

Define what constitutes acceptable accuracy and reliability.

Step 4: Maintain Human Review

Establish which outputs require expert validation.

Step 5: Document the Workflow

Record important assumptions, data sources, model behaviour, and changes.

Step 6: Monitor Performance

Review whether the AI system continues to perform appropriately as data and models change.

This approach helps organisations scale AI without treating governance as an afterthought.

What AI Should Not Do in Market Access

AI should not be treated as an autonomous decision-maker for complex reimbursement questions.

It should not independently determine:

• Whether a medicine provides sufficient value

• Whether an evidence package is adequate

• Whether a comparator is appropriate

• Whether an economic model is methodologically sound

• Whether a payer will reimburse a product

• Whether a submission is ready

The Future of Payer Evidence Generation

Payer evidence generation is likely to become increasingly continuous rather than project-based.

Instead of creating an evidence package only when a submission approaches, teams can maintain continuously updated evidence environments.

New studies can be incorporated.

Real-world evidence can be refreshed.

Competitor developments can be monitored.

Economic assumptions can be reviewed.

Payer and HTA decisions can be analysed.

AI can help identify what has changed and where the evidence base needs attention.

This creates a more dynamic model:

Monitor → Discover → Extract → Analyse → Validate → Generate → Update

The advantage is not simply speed.

It is the ability to keep evidence aligned with a changing healthcare environment.

Conclusion

AI has the potential to significantly accelerate payer evidence generation by reducing the manual effort involved in literature review, evidence extraction, economic analysis, real-world evidence analysis, and dossier development.

The five major opportunities are clear:

1. Faster literature review

2. More efficient evidence extraction and synthesis

3. Accelerated economic evidence development

4. Improved analysis of real-world evidence

5. Faster payer and HTA dossier preparation

The strongest use cases combine AI automation with expert HEOR and market access judgment.

A market access analytics platform can provide the infrastructure needed to connect evidence, analytics, and workflows. HEOR AI tools can accelerate research and analysis. Payer evidence generation software can help transform validated evidence into structured outputs.

But speed should never be the only objective.

Evidence needs to remain transparent, traceable, reproducible, and appropriate for the decision being supported. ISPOR's work on AI in HEOR highlights both the potential for greater efficiency and the importance of methodological transparency. [2]

Pienomial can help life sciences organisations build toward this evidence-connected model by bringing AI-powered intelligence into complex research and decision-support workflows.

The future of market access will not simply belong to teams that generate evidence faster. It will belong to teams that can generate better-connected, more current, more transparent, and more decision-relevant evidence while maintaining the expert judgment required to turn that evidence into credible payer value.