Adoption of an innovation to repair aortic aneurysms at a Canadian hospital: a qualitative case study and evaluation
Bibliographic record
Abstract
BACKGROUND: Priority setting in health care is a challenge because demand for services exceeds available resources. The increasing demand for less invasive surgical procedures by patients, health care institutions and industry, places added pressure on surgeons to acquire the appropriate skills to adopt innovative procedures. Such innovations are often initiated and introduced by surgeons in the hospital setting. Decision-making processes for the adoption of surgical innovations in hospitals have not been well studied and a standard process for their introduction does not exist. The purpose of this study is to describe and evaluate the decision-making process for the adoption of a new technology for repair of abdominal aortic aneurysms (endovascular aneurysm repair [EVAR]) in an academic health sciences centre to better understand how decisions are made for the introduction of surgical innovations at the hospital level. METHODS: A qualitative case study of the decision to adopt EVAR was conducted using a modified thematic analysis of documents and semi-structured interviews. Accountability for Reasonableness was used as a conceptual framework for fairness in priority setting processes in health care organizations. RESULTS: There were two key decisions regarding EVAR: the decision to adopt the new technology in the hospital and the decision to stop hospital funding. The decision to adopt EVAR was based on perceived improved patient outcomes, safety, and the surgeons' desire to innovate. This decision involved very few stakeholders. The decision to stop funding of EVAR involved all key players and was based on criteria apparent to all those involved, including cost, evidence and hospital priorities. Limited internal communications were made prior to adopting the technology. There was no formal means to appeal the decisions made. CONCLUSION: The analysis yielded recommendations for improving future decisions about the adoption of surgical innovations. ese empirical findings will be used with other case studies to help develop guidelines to help decision-makers adopt surgical innovations in Canadian hospitals.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 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".