Beyond clinical endpoints: HTA has a measurement problem
The purpose of health technology assessment (HTA) is to help payers and health systems decide what delivers value – but too often, that value is reduced to what is easiest to count. In this Guest Column, Tara Cowling (Founder and President, Medlior Health Outcomes Research Ltd) argues that the biggest barrier to HTA isn’t a lack of data, but a lack of decision-grade measurement of what matters most to patients, caregivers, and society. Tara explores why quality-of-life evidence so often falls short, how preference-based methods and real-world comparators can close the gap, and what a more fit-for-purpose evidence infrastructure could look like.
Measuring what matters
“HTA does not have a data problem. It has a measurement problem, and it is already affecting decisions.”
For years, we have acknowledged that clinical endpoints alone do not capture the full value of healthcare interventions. Yet decisions continue to be driven by them. The result is a persistent and consequential gap between what is measured and what matters to patients, to caregivers, and to society.
HTA is evolving. But unless measurement evolves with it, that progress risks being more rhetorical than real. Societal impact cannot remain a “nice to have.” It must become decision-grade evidence. And yet, decisions are still being made every day without it.
Clinical outcomes are necessary, but no longer sufficient
Randomized controlled trials (RCTs) remain essential for establishing efficacy and safety. But they were never designed to answer the questions decision-makers now face:
- Does this treatment meaningfully improve how people live?
- Does it reduce the impact on caregivers?
- Does it enable people to remain independent, productive, and engaged?
In many conditions, particularly chronic and rare diseases, these are the outcomes that define value.
“If we only measure clinical change, we do not just miss part of the story. We risk systematically undervaluing real-world benefit and, in doing so, misinforming decisions.”
HRQoL: Critical, central, and consistently underdelivered
Health-related quality of life (HRQoL) sits at the center of societal value. Yet it is one of the least reliable inputs in HTA. Too often, HRQoL data are:
- Inconsistently collected
- Underpowered
- Poorly aligned with HTA expectations
- Lacking meaningful comparator context
This creates a fundamental contradiction. Utilities underpin cost-effectiveness models, yet the data underpinning them are often not fit for decision-making.
For HRQoL to support credible decisions, it must be:
- Longitudinal, not cross-sectional
- Anchored to baseline and disease progression
- Collected alongside meaningful comparators
- Designed explicitly for HTA use
Without this, utilities are not evidence. They are structured assumptions.
From description to value: Why preference methods matter
Accurately measuring HRQoL is not enough; describing change does not always capture its value. Preference-based methods allow us to measure value directly.
- Time Trade-Off (TTO) forces explicit trade-offs, anchoring health states to utility values that reflect real-world choices.
- Discrete Choice Experiments (DCEs) go further, revealing what drives value by quantifying trade-offs between symptoms, function, and side effects.
If HRQoL tells us what changes, these methods tell us what those changes are worth. Without them, we risk building economic models on incomplete interpretations of value.
The missing comparator: The cost of context-free evidence
One of the most persistent weaknesses in HTA is not the quality of data, but the absence of context. We ask how patients improved yet we rarely ask compared to what.
Without understanding, natural disease progression, outcomes under standard of care and real-world variability we cannot estimate incremental benefit with confidence.
Real-world evidence (RWE) offers baseline, comparator, and context that RCTs lack. Without context, data describe, but with it, data becomes evidence that can inform decisions and shift outcomes.
Cost-effectiveness without societal outcomes is incomplete
HTA is ultimately about value for money. But value cannot be fully understood through clinical endpoints alone.
When societal outcomes are properly measured:
- Quality-adjusted life years (QALYs) reflect real patient experience, not proxy assumptions
- Economic models capture functional and social gains
- Decisions align more closely with system and societal priorities
This matters most where traditional endpoints fall short: rare diseases, women’s health, chronic conditions and preventive interventions.
If societal outcomes are missing, cost-effectiveness is not just uncertain, it is incomplete.
The real problem: Evidence is not designed for decisions
The issue is not that we lack methods; it is that we rarely design evidence generation around how decisions are made. Too often, societal outcomes are added late, measured inconsistently, or treated as supplementary.
Decision-grade evidence requires a different approach involving deliberate and intentional planning:
- Designing studies with societal endpoints from the outset
- Integrating HRQoL with preference-based methods
- Embedding real-world comparators
- Building data systems that support longitudinal measurement
Many evidence strategies fail, not in analysis but in architecture. The difference is not just technical. It is how evidence is conceived in the first place. This evidence generated must be intended for decision-making from the start, rather than being retrofitted later.
Case studies in practice: Where this is already working
We are already seeing this being applied in practice.
Alzheimer’s disease: From fragmented studies to coherent strategy
Alzheimer’s disease exposes the limitations of traditional evidence models. Clinical endpoints alone cannot capture the progression of functional decline or the growing impact on caregivers.
A more integrated approach is emerging which includes:
- Disease trajectory mapping to understand when change occurs
- Anchor-based methods to define what change means
- Caregiver impact frameworks to quantify societal impact
- Global data mapping to identify comparator-ready datasets
Individually, these are useful studies. Together, they form a coherent evidence strategy that links clinical outcomes to real-world value.
Caregiver impact: From narrative to evidence
Although the effects on caregivers have been recognized for some time, they have seldom been assessed in ways that influence decision-making. This trend is now starting to shift.
Digital, prospective approaches now allow:
- Real-time capture of caregiver experience
- Longitudinal linkage to patient outcomes
- Integration into economic models as indirect costs
When measured rigorously, caregiver impact moves from context to evidence.
Moving beyond studies: The need for infrastructure
Even well-designed studies have limits. They are time-bound, fragmented, and often retrospective. HTA, however, is moving toward continuous, real-world, and system-level decision-making. This creates a gap that individual projects cannot fill. What follows is not another method, it is infrastructure.
EvidaHealth Foundation: Designing evidence for decisions
A new class of approaches is emerging to address this gap, including initiatives such as the EvidaHealth Foundation. EvidaHealth is building a patient-centered, prospective registry-based infrastructure designed to align with modern HTA needs by:
- Capturing longitudinal clinical, quality-of-life, and societal outcomes
- Embedding caregiver-reported data as a core component
- Enabling federated data models that support linkage and governance
- Providing a scalable, disease-agnostic platform
By combining prospective data collection with patient- and caregiver-centered outcomes, this approach reflects a broader shift toward designing evidence generation upfront to better capture real-world clinical and societal impact and support healthcare decision-making.
A practical path forward
As explored in this column, what is becoming increasingly apparent is the need to:
- Integrate clinical and societal outcomes
- Quantify value using preference-based methods
- Anchor evidence in real-world context
- Invest in infrastructure that supports longitudinal insight
Many of the methodological tools already exist. The challenge now lies in how they are implemented and embedded into evidence generation strategies from the outset. For organizations navigating this shift, the opportunity is not simply to generate more evidence, but to generate evidence that remains meaningful when applied to real-world healthcare decisions.
A necessary shift
The question is no longer whether societal impact matters in healthcare decision-making. That question has been answered. The real question is whether we are measuring it well enough to influence decisions.
“Ultimately, HTA does not fail when evidence is missing. It fails when the wrong evidence is used to make the right decisions. And in that gap between evidence and decision, value is either realized or lost.”
Author
Tara Cowling
Founder and President, Medlior Health Outcomes Research Ltd
Tara Cowling is the Founder and President of Medlior Health Outcomes Research Ltd., a Canadian consultancy specializing in real-world evidence, health economics, and outcomes research. With over 20 years of experience, she focuses on advancing methods that translate data into decision-grade evidence for healthcare systems.
Disclaimers:
The opinions expressed in this feature are those of the author and do not necessarily reflect the views of The Evidence Base® or Becaris Publishing Ltd.
Sponsorship for this Guest Column was provided by Medlior Health Outcomes Research.