The Epistemological Paradox of EU-HTA Joint Clinical Assessments: Certainty Without Judgment, Evidence Without Context
This paper argues that the EU-HTA Regulation's mandate for Joint Clinical Assessments to quantify certainty while prohibiting value judgments is epistemologically incoherent, as assessing certainty inherently requires scientific judgment and contextual interpretation, necessitating a shift toward explicitly structured frameworks like GRADE rather than the current narrative reporting format.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the world of modern medicine, a critical question often arises when a new treatment is proposed: how sure can we be that it will work for the average patient? This is the domain of health technology assessment, a process where experts weigh the evidence to decide if a new drug or device deserves a place in healthcare systems. To make these decisions, they rely on a concept called "certainty." In everyday terms, certainty is not just a feeling of confidence; it is a measure of how much we trust the scientific data to tell the truth about a treatment's effects. However, determining this trust is rarely simple. It requires scientists to look at messy, imperfect data and make difficult choices about what the numbers actually mean for real people. This process is inherently subjective, requiring experts to interpret the evidence through the lens of clinical reality and specific patient needs.
A new analysis by a team of researchers from universities in France, Bulgaria, and a specialized consultancy group examines a major new European Union regulation that attempts to standardize this process across forty-five countries. The regulation, which began full application in early 2025, mandates that experts perform joint clinical assessments to determine the "degree of certainty" regarding how well a new health technology works compared to existing options. Yet, the same regulation strictly forbids these experts from making any value judgments. They are told to remain purely factual, to avoid ranking health outcomes, and to refrain from deciding where a new treatment fits into a patient's overall care strategy. The researchers argue that this creates a fundamental logical contradiction. They suggest that it is impossible to measure the certainty of evidence without making the very judgments the rules prohibit, effectively asking scientists to describe the reliability of a map while being forbidden from looking at the terrain it covers.
The core of the problem lies in the nature of scientific evidence itself. The researchers explain that uncertainty in medicine comes in different forms. There is the randomness of biology, where patients react differently to the same treatment, and there is the uncertainty of knowledge, where we simply do not have enough data yet. To assess the "degree of certainty," experts must synthesize these different types of uncertainty into a single conclusion. This requires them to decide which pieces of evidence matter more, how to handle conflicting results, and what level of risk is acceptable for a specific disease. The European regulation attempts to strip away this interpretive layer, demanding a report that is purely factual and free of context. The authors of the study point out that this is like asking someone to describe the weight of an object without ever being allowed to touch it or know what it is made of. Without the ability to make contextual judgments, the assessment of certainty becomes a hollow exercise, as the very act of judging how certain we are requires the kind of interpretive thinking the rules ban.
A significant part of this tension involves the difference between how a drug works in a controlled experiment versus how it works in the real world. Most new treatments are tested in highly controlled studies where patients are carefully selected and monitored closely to prove the drug works under ideal conditions. This is known as efficacy. However, health technology assessments are supposed to measure "relative effectiveness," which refers to how well a treatment performs in the messy, unpredictable reality of everyday clinics. The researchers note that the new European regulation uses the term "effectiveness" but relies almost entirely on data from those idealized, controlled studies. This creates a gap between what the data shows and what the assessment claims to measure. To bridge this gap, experts would need to make assumptions about how the controlled trial results apply to the general population, but the regulation prohibits the kind of judgment needed to make those assumptions safely.
The study also highlights that the current method for reporting these findings lacks a clear, structured way to handle the necessary judgments. In other parts of the scientific world, frameworks exist that force experts to explicitly state their reasoning and the limits of their confidence, making the process transparent and reproducible. The European approach, by contrast, relies on long narrative reports where judgments are buried in paragraphs of text. The researchers argue that this format makes it difficult for decision-makers to see exactly where the experts have made assumptions or where the evidence is weak. Instead of clarifying the science, the current system obscures the inevitable human choices involved in interpreting data. The authors conclude that the goal of a completely neutral, context-free assessment is an illusion. Certainty cannot be measured without judgment; the only honest path forward is to acknowledge that judgment is necessary and to create a system that makes those judgments clear, structured, and open to scrutiny, rather than pretending they do not exist.
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