Change Management in Radiology: A Contemporary Primer for Effective and Sustainable Practice
Bibliographic record
Abstract
Radiology is experiencing rapid and interconnected change, including rising imaging volumes, expanding access demands, and the introduction of artificial intelligence into daily practice. However, many radiologists have limited exposure to structured approaches for leading change in complex clinical environments. Change management research provides a practical vocabulary and set of concepts that can help radiology leaders design and sequence change more effectively. Organizational readiness encompassing cognitive, operational, trust, and resource dimensions is consistently associated with successful transitions. Classic frameworks such as Lewin's change stages, Kotter's 8-step model for mobilizing teams, the ADKAR model for individual adoption, and Armenakis' evidence-based change-messaging principles offer radiology-specific value when planning workflow adjustments, introducing new processes, or shaping departmental culture. Attention to workflow reality, early engagement of key groups, understanding human responses to change, appropriate pacing, particularly during leadership transitions, and clarity of communication further support sustainable change. Applying contemporary change management concepts can help radiology departments and leaders navigate evolving demands while maintaining coherence, stability, and high-quality patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.016 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".