Ranking Decision-Making Criteria for Early Adoption of Innovative Surgical Technologies
Bibliographic record
Abstract
Importance: There is no decision-making framework in the early-adoption stage of novel surgical technologies, putting the quality of health care and resource allocation of the health care system at risk. Objective: To investigate relevant weighted criteria that decision-makers may use to make an informed decision for the early adoption of innovative surgical technologies. Design, Setting, and Participants: This multi-institutional decision analytical modeling study used a mixed-methods multicriteria decision analysis (MCDA) and had 2 phases. First, a panel of 12 experts validated decision criteria in the literature and identified additional criteria. Second, 33 Canadian experts prioritized the main criteria (domains) using the composition pairwise-comparison weight-elicitation method (analytical hierarchy process model) and ranked their subcriteria using the direct-ranking elicitation method (Likert scale). Data were analyzed, and response consistency was estimated using the consistency ratio. Analysis of variance was used to assess for significant differences between expert responses. The MCDA was conducted at McGill University between 2021 and 2023. Data were collected nationally by inviting experts in Canada. Main Outcome and Measure: Criteria domain weights and subcriteria rankings. Priority vectors, which are priority scores analyzed and prioritized from expert responses, were used to rank criteria domains and subcriteria for decision-making on adopting new innovative surgical technologies. Results: A total of 45 experts (33 male [73.3%] and 12 female [26.7%]) were invited with different levels of education (22 experts with MD or equivalent, 13 experts with master's degree, and 12 experts with PhD degree) and years of experience (4 experts with <10, 12 experts with 11-20, 18 experts with 21-30, and 11 experts with >30 years). Surgeon experts (23 individuals) were from all surgical disciplines, and nonsurgeon experts (22 individuals) were administrative officers in surgical device procurement, health technology assessment experts, and hospital directors. A total of 7 domains and 44 subcriteria were identified. The MCDA model found that clinical outcomes had the highest priority vector, at 0.429, followed by patients and public relevance (0.135). Hospital-specific criteria (priority vector, 0.099), technology-specific criteria (priority vector, 0.092), and physician-specific criteria (priority vector, 0.087) were the next most highly ranked. The lowest priority vectors were for economic criteria, at 0.083, and finally policies and procedures, at 0.075. There was consensus in the responses (consistency ratio = 0.006), and there were no statistically significant differences between expert responses. Conclusions and relevance: This study weighted priority criteria domains in importance and established ranked subcriteria for decision-making of early adoption of surgical technologies. Putting these criteria into a framework may help surgeons and decision-makers make informed decisions for the early adoption of new surgical technologies.
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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 | 0.052 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".