Frequently Asked Questions: Patient Experience Data and AI in Payer Decision-Making

Patient experience data influences payer decision-making by revealing dimensions of value not captured in traditional clinical evidence and therefore shifting perceptions of benefit. These dimensions include quality of life, treatment convenience, mode of administration (e.g., home vs. hospital), burden on families and caregivers, and impact on daily functioning.

For example, differences between home-based and hospital-based care delivery can significantly affect perceived value in real-world settings, adding dimensions of patient convenience and quality of life, as well as cost optimization, in addition to clinical efficacy considerations.

In HTA and payer decision-making, patient data can help to clarify which outcomes are truly patient-relevant and translate into meaningful real-world benefit, differentiating between otherwise comparable treatments and showing how a therapy fits within the care pathway.

Patient experience data can also support value-based contracting by linking reimbursement to patient-relevant outcomes beyond purely clinical or financial measures.

During the Global Payer Forum 2026, several key barriers to acceptance emerged:

• Uncertainty about evidence quality: patient evidence is often dismissed as subjective unless it is gathered using clear methods with transparency and representativeness
• Misalignment of value: patients prioritize quality of life, functioning, convenience, and affordability, while payers often focus on budget impact, clinical efficacy as demonstrated in clinical trials, and cost-effectiveness thresholds
• Operational and resource constraints: inclusion of capability, cost, and process challenges in the PRO implementation across the treated population
• Lack of institutional frameworks: limited formal structures to systematically incorporate PROs and real-world data into decision-making

As a result, many healthcare systems continue to use patient input in ad hoc rather than systematic ways.

Healthcare systems need to shift from static, trial-based evidence generation to continuous, real-world evidence generation. This evolution requires credible methods, including both qualitative and quantitative research, not simply increased data volume.

Healthcare systems also need dedicated processes and institutional capabilities to capture, structure, and use these data, as many systems remain organized around delivery rather than continuous learning. AI can support the transformation of real-world data into actionable evidence, but this must be paired with standardized frameworks, robust methodology, and patient involvement from study design through implementation.

Global Payer Forum speakers highlighted AI as a tool with the potential to improve patient outcomes across clinical, operational, and evidence-generation settings.

Potential benefits introduced by AI:

• Earlier diagnosis
• Improved triage
• Enhanced clinical trial design and execution
• More responsive patient interfaces
• Operational efficiencies, including faster booking, claims handling, and care coordination
• Transformation of unstructured patient experience data (e.g., narratives, wearables, home monitoring) into structured insights that support earlier intervention and more personalized care

At the same time, AI introduces several risks:

• Poor-quality data may lead to unreliable or biased decisions
• Algorithmic bias may reinforce or worsen existing health inequities
• Limited transparency in “black box” models can undermine clinician and patient trust

Global Payer Forum 2026 featured a conversation on AI as a tool to support more efficient and evidence-based payer decision-making.

This includes the ability to:

• synthesize more detailed patient-centered datasets.
• identify relevant subpopulations.
• translate real-world data into real-world evidence.
• support post-launch monitoring.

Payer organizations are increasingly under pressure from high-cost treatments, constrained budgets, and limited staff capacity. Speakers at the Global Payer Forum 2026 highlighted that AI may support a shift from paying for boxes or units toward outcome-based reimbursement, provided the underlying evidence is robust and trustworthy. AI can also reduce administrative friction across procurement, claims, and follow-up processes, supporting the implementation of value-based contracts.

However, realizing these benefits requires internal capabilities, governance frameworks, and explainability; without these, AI may increase complexity rather than reduce
burden.

Trust in AI systems is primarily driven by transparency, explainability, and robust data governance rather than technical sophistication alone. Patients and clinicians need to understand what data are being used, how they are protected, and on what basis an AI-supported recommendation is made. Consent, anonymity, and the secure handling of sensitive health data are essential foundations.

In addition, transparency must extend to AI algorithms, not only the data, so that outputs are explainable to clinicians and interpretable for patients in clear, accessible terms.

Trust is further reinforced when stakeholders see tangible value, such as improved outcomes, safer decisions, or more efficient care delivery.

The answer is to involve patients from the start, not after the endpoints have already been chosen. Patients should help define what success means, which outcomes are meaningful, and which instruments genuinely reflect their lived experience.

At the Global Payer Forum 2026, speakers highlighted that outdated or poorly designed measures can fail to capture what matters to patients, and that even widely used questionnaires may become misaligned with current patient realities.

High-quality qualitative research is not secondary evidence but a necessary component for understanding patient priorities, context, and trade-offs. AI can support the capture and structure of these outcomes at scale, but it cannot determine their relevance independently. Ensuring relevance requires co-design, fit-for-purpose outcome selection, and formal frameworks that integrate patient-defined value into evidence generation and payer review.

AI may enable earlier and broader patient involvement in healthcare decision-making, while also supporting its operationalization. Rather than being consulted only at the end of HTA or reimbursement processes, patients are increasingly involved in shaping study design, endpoints selection, care pathway design, and post-launch evidence generation. Patients bring unique embodied, system-level, and community knowledge, which should be integrated into both research and participatory decision-making processes.

AI can support the continuous and scalable capture of patient experience data from narratives, wearables, and home-based tools. The direction is toward more structured patient involvement, with AI helping to surface patient-relevant insights while formal frameworks ensure that patient input remains influential rather than symbolic.