The Impact of Digital Therapeutics on Current Health Technology Assessment Frameworks
Bibliographic record
Abstract
Historically healthcare has been delivered offline (e.g., physician consultations, mental health counseling services). It is widely understood that healthcare lags behind other industries (e.g., financial, transportation) whom have already incorporated digital technologies in their workflow. However, this is changing with the recent emergence of digital therapeutics (DTx) helping to bring healthcare services online. To promote adoption, healthcare providers need to be educated regarding the digital therapy to allow for proper prescribing. But of equal importance is affordability and many countries rely on reimbursement support from the government and insurance agencies. Here we briefly explore how national reimbursement agencies or non-profits across six countries (Canada, United States of America, United Kingdom, Germany, France, Australia) handle DTx submissions and describe the potential impact of digital therapeutics on current health technology assessment (HTA) frameworks. A targeted review to identify HTA submissions and guidelines from national reimbursement agencies or non-profits was conducted. We reviewed guidelines from the Institute for Clinical and Economic Review (ICER) in the USA, the Canadian Agency for Drugs and Technologies in Health (CADTH) in Canada, the National Institute for Health and Care Excellence (NICE) in the United Kingdom (UK), the Institute for Quality and Efficiency in Health Care (IQWIG) in Germany, Haute Autorité de Santé (HAS) in France, and the Pharmaceutical Benefits Advisory Committee (PBAC) in Australia. Our review identified one set of guidelines developed by NICE in the UK. The guidelines by NICE outlined an evidence standards framework for digital health technologies (DHT). Depending on the organizational impact, financial commitment, and economic risk for the payer, different economic analyses are required. Economic analyses levels are separated into 3 categories, basic, low financial commitment, and high financial commitment. All economic analyses levels require a budget impact analysis. A cost-utility analysis is recommended for DHTs categorized in the high financial commitment category. Whereas, for DHTs that are in the low financial commitment category, a cost-consequence analysis is typically recommended. No HTA guidelines for DTx submissions were identified for the remaining countries (Canada, USA, Germany, France, and Australia).
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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 | 0.261 | 0.554 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".