The Challenges in Conducting Economic Evaluations for Rehabilitation Technologies
Bibliographic record
Abstract
Background: Health technology assessment (HTA) is an important evidentiary component in the decision-making process for the adoption of new healthcare technologies to the healthcare system. Economic evidence is an important consideration in HTAs. Recent systematic reviews in rehabilitation have shown a limited number of economic evaluations and high levels of uncertainty in the results. It is unclear whether there are challenges related to the field of rehabilitation and the technologies used in rehabilitation that inhibit the development of economic evidence. Methods: In this study, economic evaluations in rehabilitation were reviewed. This was followed by a summary of the latest evidence on the challenges of conducting HTA for medical devices and the relationship with rehabilitation technologies. Finally, several considerations are suggested to improve the HTA of technologies that target rehabilitation. A literature review of Google Scholar and PubMed was conducted to identify reviews in economic evaluations in rehabilitation. A recent review on the barriers to HTA of medical devices in general was also examined to identify similar concerns with rehabilitation technologies. Results: The challenges identified include the lack of high-quality studies, the interaction between the technology and the user, the short product life cycle, and estimation of efficacy in technologies with multiple target populations. Conclusion: Overall, many of the challenges in evaluating medical devices also apply to rehabilitation interventions. Further research and discussion on these issues are necessary to increase the clinical evidence for rehabilitation technologies, strengthen the development of HTAs, and facilitate the use of technologies to improve the health of individuals requiring rehabilitation.
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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 | 0.665 | 0.854 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".