Multi-level factors influence the implementation and use of complex innovations in cancer care: a multiple case study of synoptic reporting
Bibliographic record
Abstract
BACKGROUND: The implementation of innovations (i.e., new tools and practices) in healthcare organizations remains a significant challenge. The objective of this study was to examine the key interpersonal, organizational, and system level factors that influenced the implementation and use of synoptic reporting tools in three specific areas of cancer care. METHODS: Using case study methodology, we studied three cases in Nova Scotia, Canada, wherein synoptic reporting tools were implemented within clinical departments/programs. Synoptic reporting tools capture and present information about a medical or surgical procedure in a structured, checklist-like format and typically report only items critical for understanding the disease and subsequent impacts on patient care. Data were collected through semi-structured interviews with key informants, document analysis, nonparticipant observation, and tool use/examination. Analysis involved production of case histories, in-depth analysis of each case, and a cross-case analysis. Numerous techniques were used during the research design, data collection, and data analysis stages to increase the rigour of this study. RESULTS: The analysis revealed five common factors that were particularly influential to implementation and use of synoptic reporting tools across the three cases: stakeholder involvement, managing the change process (e.g., building demand, communication, training and support), champions and respected colleagues, administrative and managerial support, and innovation attributes (e.g., complexity, compatibility with interests and values). The direction of influence (facilitating or impeding) of each of these factors differed across and within cases. CONCLUSIONS: The findings demonstrate the importance of a multi-level contextual analysis to gaining both breadth and depth to our understanding of innovation implementation and use in health care. They also provide new insights into several important issues under-reported in the literature on moving innovations into healthcare practice, including the role of middle managers in implementation efforts and the importance of attending to the interpersonal aspects of implementation.
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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 | 0.038 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".