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Record W4415096947 · doi:10.3389/fphar.2025.1700196

Editorial: Translation of health technology reassessment and its outputs of disinvestment, de-implementation and de-adoption of drugs and medical devices in practice-what have we learned?

2025· editorial· en· W4415096947 on OpenAlexaff
Rosmin Esmail, Nora Ibargoyen-Roteta, Maximilian Otte, Iñaki Gutiérrez‐Ibarluzea, Roberta Joppi, Hans-Peter Dauben

Bibliographic record

VenueFrontiers in Pharmacology · 2025
Typeeditorial
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsAlberta Bible CollegeUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsHealth technologyDirectiveScrutinyHealth careBiomedical technologyProduct (mathematics)Process (computing)Medical deviceEmerging technologies

Abstract

fetched live from OpenAlex

Health technology assessment (HTA) has long been associated with the adoption and evaluation adoption of innovations and new technologies. The current HTA definition includes the idea of 'determining the value of a health technology at different points during its lifecycle' (1). Furthermore, healthcare and its sustainability depends on the ability to reassess technologies during their lifecycle to ensure they are being used optimally. Health Technology Reassessment (HTR) has gained increasing attention as a tool to re-evaluate technologies that are currently being used in the health care system. HTR is defined as "a structured, evidence-based assessment of the clinical, social, ethical, and economic effects of a technology, currently used in the health care system, to inform the optimal use of that technology in comparison to its alternatives" (2). Reassessment could lead to an increased use or adoption, no change, decreased use, de-adoption1, disinvestment2, or de-implementation3 (3). However, the implementation of the recommendations from a HTR process requires regulatory, cultural, political, ethical, and real-world evidence to succeed. In this editorial, we reflect on five case studies and highlight lessons learned to illuminate achievements and persistent challenges with HTR. Regulation as a Foundation for Safe Practice One lesson is that regulatory frameworks shape what reaches patients. The study of Ala®sil, an ophthalmic irrigation solution withdrawn from the European market in 2016, demonstrated how insufficient scrutiny under the Medical Devices Directive (MDD) allowed a product with limited safety data to be marketed (Andrés-Iglesias C et al, 2024). Under the current Medical Devices Regulation (MDR 2017/745), such a device would not have been authorized. This example underlines how regulatory evolution enables proactive de-adoption of unsafe or low-value technologies before widespread use occurs. Moreover, this study highlights the need to have HTA activities occur in close collaboration with regulatory authorities to ensure synergies among organizations with diverse objectives. Natural Disinvestment Through Innovation Not all disinvestment requires a formal policy decision: sometimes it happens naturally. The history of trastuzumab in the Veneto Region, Italy, is a clear example of that (Becchetti AG et al, 2024). With the arrival of biosimilars and new formulations, per capita costs decreased, and resources were reallocated. Regional authorities used HTR methodologies to monitor these changes, ensuring continuity of care while guiding spending. This study demonstrates how competition, if accompanied by structured oversight, can act as a mechanism for "natural disinvestment", freeing resources for innovation. The Cost of Delayed Reassessment Delays in disinvestment or adoption can translate directly into worse outcomes, as depicted in the case of the use of rituximab in the treatment of follicular lymphoma. A nationwide 16-year cohort study in Brazil showed that patients treated with rituximab-containing regimens had better survival than those on older chemotherapy schemes. This study showed that limited public access to this treatment meant that thousands of patients were treated sub optimally (Azevedo PS et al, 2024). Subsequent delays in the disinvestment of outdated therapies avoids the adoption of superior treatments, which can save lives. Rare Diseases and the Challenge of Optimization In rare diseases, HTR faces the challenges of high costs of treatments and variable outcomes. A comprehensive retrospective study of Gaucher disease in Brazil showed that enzyme replacement therapies improved survival, but those benefits varied with dosing adequacy and early treatment initiation (Borin MC et al, 2024). Given the extraordinary costs of these therapies, reassessment does not necessarily mean withdrawal, but optimization of use and tailoring of investment to maximize benefit. In such contexts, HTR becomes a tool for balancing sustainability with ethical obligations toward patients with rare conditions to ensure optimal treatment. Real-World Evidence as a Driver of Reassessment Randomized controlled trials remain essential, but real-world data are increasingly shaping reassessment. A large observational study in China found that SGLT2 inhibitors significantly slowed kidney disease progression and reduced renal events compared with other glucose-lowering drugs (Xiao X et al, 2024). These findings provide a compelling rationale for the progressive de-implementation of less effective alternatives in favor of SGLT2 inhibitors and depict how real-world evidence can accelerate translation of trial results into practice. Key Lessons Across these diverse studies, several lessons emerge for HTR: 1. Regulation is needed. Unsafe practices need to be monitored and regulated to ensure they are meeting patient needs through the HTR process. Further information should be obtained on all the drugs approved with specific commitments on efficacy. 2. Room for innovation. Natural reassessment can occur if the system allows for competition and innovation. 3. Reassessment needs to be timely. Evidence alone is not enough: cultural, political, and/or financial barriers can delay change. 4. Reassessment is about optimization. It may result in withdrawal, substitution, dose optimization, or resource reallocation among its several outputs to ensure optimal use of a technology. 5. Real-world evidence can be a driver in HTR. Using clinical trials to complement, can ground HTR decisions in everyday practice and within diverse populations. Moving Forward The translation of HTR into practice requires consideration of implementation (4). Health systems should embed reassessment cycles into HTA agencies, invest in infrastructures for continuous monitoring, and build implementation science capacity. Importantly, we must also shift the culture of medicine to embrace reassessment as a process of learning and progress within the life cycle of a technology (5). These five cases emphasize that HTR is not merely a technical process. It requires robust regulation, innovation, timely decision-making, optimization, coordination among disciplines, and cultural acceptance through real-world evidence. They illustrate the breadth of HTR's importance and relevance in managing the finite and limited resources of today's health care systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.065
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0040.002
Science and technology studies0.0040.007
Scholarly communication0.0100.006
Open science0.0050.002
Research integrity0.0210.021
Insufficient payload (model declined to judge)0.0210.012

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.185
GPT teacher head0.567
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2025
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