MULTI-LEVEL FACTORS INFLUENCE THE IMPLEMENTATION AND USE OF COMPLEX INNOVATIONS IN CANCER CARE: A MULTIPLE CASE STUDY OF SYNOPTIC REPORTING IN NOVA SCOTIA
Bibliographic record
Abstract
Background: Moving knowledge into healthcare practice and the implementation of innovations in healthcare organizations remain significant challenges. The objective of this study was to examine the key interpersonal-, organizational-, and system-level factors that influenced the implementation and use of an innovation â synoptic reporting tools â in three specific cases of cancer care.\n\nMethods: Using case study methodology, this study examined three cases in Nova Scotia, Canada, wherein synoptic reporting tools were implemented within clinical departments/programs. Three theoretical perspectives guided the design, analysis, and interpretation of the study. Data were collected through semi-structured interviews with key informants across four units of analysis (individual user, implementation team, organization, and larger system), document analysis, nonparticipant observation, and examination/use of the synoptic reporting tools. Analysis involved production of case histories, an in-depth analysis of each case, and a cross-case analysis.\n\nResults: Numerous factors â which existed at multiple levels of the system and which were often related â were important to the implementation and use of synoptic reporting tools. The cross case analysis revealed five common factors that were particularly influential to implementation and use across the three cases studied: stakeholder involvement, managing the change process, administrative and managerial support, the presence of clinical champions, and attributes of the tools themselves. Key factors distinct to one or two of the cases were: implementation approach, project management, resources, culture, leadership, monitoring and feedback mechanisms, and components of the healthcare system (e.g., care delivery structures, system infrastructure, and socio-historical context). The analyses suggested that several contextual factors, including the timing of implementation and technical requirements of the tool, contributed to the differences across cases.\n\nDiscussion: This study contributes to our knowledge base on the multi-level factors, and the relationships amongst factors in specific contexts, that influence implementation and use of innovations such as synoptic reporting tools in health care. Importantly, the findings add to our understanding of several important issues that are under-developed in the existing literature in this area: organizational management; healthcare system components; interpersonal aspects of implementation, including stakeholder involvement; and the complex nature of implementation processes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".