ReviewThe British journal of radiology2021
Factors predicting timely implementation of radiotherapy innovations: the first model.
Review in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The barriers and facilitators of radical innovation implementation in secondary healthcare: a systematic review.Journal of health organization and management · 2021Pooled it
- Evaluation of a quasi-automatic treatment planning workflow for head-and-neck cancer in radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Independent Testing of Published CT Models for PD-L1 Status in Non-Small Cell Lung Cancer.Radiology · 2026Article
- Toward a human-centric co-design methodology for AI detection of differences between planned and delivered dose in radiotherapy.Journal of applied clinical medical physics · 2025Article
- Multicentre prospective risk analysis of a fully automated radiotherapy workflow.Physics and imaging in radiation oncology · 2025Article
- Implementation Strategy for Artificial Intelligence in Radiotherapy: Can Implementation Science Help?JCO clinical cancer informatics · 2024Article
- Innovative Education Method for a More Effective, Faster, and Valued Training in Radiation Therapy Treatment Planning.Advances in radiation oncology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe improvement of radiotherapy depends largely on the implementation of innovations, of which effectivity varies widely. The aim of this study is to develop a prediction model for successful innovation implementation in radiotherapy to improve effective management of innovation projects.
methodsA literature review was performed to identify success factors for innovation implementation. Subsequently, in two large academic radiotherapy centres in the Netherlands, an inventory was made of all innovation projects executed between 2011 and 2017. Semi-structured interviews were performed to record the presence/absence of the success factors found in the review for each project. Successful implementation was defined as timely implementation, yes/no. Cross-tables, Χ
resultsFrom the 163 identified innovation projects, only 54% were successfully implemented. We found 31 success factors in literature of which 14 were significantly related to successful implementation in the innovation projects in our study. The prediction model contained the following determinants: (1) sufficient and competent employees, (2) complexity, (3) understanding/awareness of the project goals and process by employees, (4) feasibility and desirability. The area Under the curve (AUC) of the prediction model was 0.86 (0.8-0.92, 95% CI).
conclusionA prediction model was developed for successful implementation of innovation in radiotherapy. ADVANCES IN KNOWLEDGE: This prediction model is the first of its kind and, after external validation, could be widely applicable to predict the timely implementation of radiotherapy innovations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.