Introduction of new technologies and decision making processes: a framework to adapt a Local Health Technology Decision Support Program for other local settings
Bibliographic record
Abstract
PURPOSE: Introducing new health technologies, including medical devices, into a local setting in a safe, effective, and transparent manner is a complex process, involving many disciplines and players within an organization. Decision making should be systematic, consistent, and transparent. It should involve translating and integrating scientific evidence, such as health technology assessment (HTA) reports, with context-sensitive evidence to develop recommendations on whether and under what conditions a new technology will be introduced. However, the development of a program to support such decision making can require considerable time and resources. An alternative is to adapt a preexisting program to the new setting. MATERIALS AND METHODS: We describe a framework for adapting the Local HTA Decision Support Program, originally developed by the Department of Surgery and Surgical Services (Calgary, AB, Canada), for use by other departments. The framework consists of six steps: 1) development of a program review and adaptation manual, 2) education and readiness assessment of interested departments, 3) evaluation of the program by individual departments, 4) joint evaluation via retreats, 5) synthesis of feedback and program revision, and 6) evaluation of the adaptation process. RESULTS: Nine departments revised the Local HTA Decision Support Program and expressed strong satisfaction with the adaptation process. Key elements for success were identified. CONCLUSION: Adaptation of a preexisting program may reduce duplication of effort, save resources, raise the health care providers' awareness of HTA, and foster constructive stakeholder engagement, which enhances the legitimacy of evidence-informed recommendations for introducing new health technologies. We encourage others to use this framework for program adaptation and to report their experiences.
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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 | 0.158 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".