How to measure innovation in radiotherapy: an application of the Delphi method
Bibliographic record
Abstract
Objective: Innovation is an important driver for improving the quality of health care, yet a tension exists between innovation and providing cost-effective health care. To develop strategies that promote innovation, parameters are needed that are indicative of innovation. However, no recognised indicators of innovation in radiotherapy are currently available. The aim of this study is to fill that gap by providing a framework for measuring innovation. This should facilitate future multi-centric studies on strategies aimed at promoting innovation in radiotherapy.Methods: We applied the Delphi method in four rounds. The chairpersons of all Dutch radiotherapy departments were asked to suggest indicators. The resulting inventory was assessed by a number of Dutch radiation oncologists, medical physicists and managers. After implementig a cut-off score on suitability and measurability, we asked Dutch professors on innovation to assess the remaining indicators. Finally, the chairpersons reached consensus.Results: On the basis of the Delphi study, we derived 13 indicators in four categories, more specific product innovation, technology innovation, market innovation and organisational innovation, for measuring both incremental and radical innovations in radiotherapy; these indicators are also suitable for measuring the generation and adoption of innovations.Conclusions: We were successful in reaching consensus amongst the experts on indicators that measure innovations in radiotherapy. The developed tool will be used to investigate the relation between innovation and possible factors inhibiting or stimulating successful innovation and between the level of innovation and its effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".