Overview of Health Technology Assessment Processes for Time-Limited Recommendations
Bibliographic record
Abstract
Several regulatory agencies have implemented processes to facilitate timely access to innovative therapies for patients with rare or life-threatening diseases with high unmet medical needs, typically with additional data-generation requirements to address important uncertainties in the existing evidence base at the time of regulatory approval. Many health technology assessment (HTA) agencies have adapted their processes to include time-limited recommendations (TLRs) for such therapies, which allows patients access to these medications while additional data are collected to verify the clinical efficacy, real-world effectiveness, or economic value of the new therapeutic. The objective of this Environmental Scan is to identify what HTA agencies have processes in place for TLRs and to describe these processes, if applicable. HTA agencies in North America, Europe, Australia, and New Zealand were considered for this report based on commonalities to the Canadian context in terms of geography and relevant regulatory or HTA and reimbursement processes. Seven HTA agencies (the National Institute for Health and Care Excellence, the Scottish Medicines Consortium, France’s Haute Autorité de Santé, the Dutch National Health Care Institute, the Italian Medicines Agency, the Belgium National Institute for Health and Disability Insurance, and the Australian Pharmaceutical Benefits Advisory Committee) that have implemented formal TLR processes, through managed entry agreements, special access funds, or other related programs were identified. Most TLR programs are referred to as managed access or entry agreements, but other types include interim acceptance, conditional inclusion, assessment contingent on additional evidence development, and additional specialized programs or pathways. TLRs are primarily used for promising (and sometimes high-cost) medicines intended to treat a condition with an important unmet medical need; some specify that the disease must be serious and life threatening (regardless of prevalence), others indicate that the process is used for orphan drugs intended to treat rare conditions, and others consider multiple criteria or do not have prerequisite criteria for eligible indications. Evidence generation requirements for TLRs vary across HTA agencies and are often dependent on the nature of the clinical and economic uncertainties identified in the initial assessment; sources include later follow-up from ongoing clinical trials, new clinical trials, real-world data, or a combination of both clinical and real-world studies. Additionally, most TLR processes have explicit maximum durations for the time-limited period, which range from 2 to 7 years across the included HTA agencies (although some allow for time adjustments or renewals); however, the French and Scottish HTA agencies determine the duration of the time-limited period on a case-by-case basis. Overall, this report provides an overview of HTA processes for TLRs across HTA agencies in Europe and Australia, many of which may be useful to inform the implementation of future TLR processes in Canada, as well as to refine existing ones.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.331 | 0.497 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.029 | 0.036 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".