TRENDS AND OPPORTUNITIES IN HEALTH ECONOMIC EVALUATIONS OF PROSTHETIC CARE INNOVATIONS
Bibliographic record
Abstract
Overcoming obstacles to prosthetic fittings requires frequent tryouts of sockets and components. Repetitions of interventions are upsetting for users and place substantial economic burden on healthcare systems. Encouraging prosthetic care innovations capable of alleviating clinical and financial shortcomings of socket-based solutions is essential. Nonetheless, evidence of socio-economic benefits of an innovation are required to facilitate access to markets. Unfortunately, complex decisions must be made when allocating resources toward the most relevant health economic evaluation (HEE) at a given stage of development of an innovation. This paper first, aimed to show the importance and challenges of HEEs of intervention facilitating prosthetic fittings. Next, the main trends in HEEs at various phases of product development and clinical acceptance of prosthetic care innovations were outlined. Then, opportunities for a basic framework of a preliminary cost-utility analysis (CUA) during the mid-stage of development of prosthetic care innovations were highlighted. To do this, fundamental and applied health economic literature and prosthetic-specific publications were reviewed to extract and analyse the trends in HEEs of new medical and prosthetic technologies, respectively. The findings show there is consensus around the weaknesses of full CUAs (e.g., lack of timeliness, resource-intensive) and strengths of preliminary CUAs (e.g., identify evidence gaps, educate design of full CUA, fast-track approval). However, several obstacles must be overcome before preliminary CUA of prosthetic care innovations will be routinely carried out. Disparities of methods and constructs of usual preliminary CUA are barriers that could be alleviated by a more standardized framework. The paper concludes by identifying that there are opportunities for the development of a basic framework of preliminary CUA of prosthetic care innovations. Ultimately, the collaborative design of a framework could simplify selection of the methods, standardise outcomes, ease comparisons between innovations and streamline pathways for adoption. This might facilitate access to economical solutions that could improve the life of individuals suffering from limb loss. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/36364/28327 How To Cite: Frossard L. Trends and opportunities in health economic evaluations of prosthetic care innovations. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.9. https://doi.org/10.33137/cpoj.v4i2.36364 Corresponding Author: Laurent Frossard, PhD, Professor of BionicsYourResearchProject Pty Ltd, Brisbane, Australia.E-Mail: laurentfrossard@outlook.comORCID number: https://orcid.org/0000-0002-0248-9589
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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 | 0.138 | 0.340 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".