Diffusing Healthcare Innovations: A Case Study of the Care Delivery Network
Bibliographic record
Abstract
This article describes the experiences of the Care Delivery Network (CDN) Project, particularly as they relate to the diffusion of knowledge in healthcare settings. After outlining the history of the CDN Project, several propositions are tested and findings presented. The CDN's experience suggests that for innovations to be voluntarily adopted by health service delivery organizations dispersed throughout a large geographical region, key factors such as professional champions, information technology, trust, communication, and boundary-spanning individuals are necessary. 2 , and the prevailing view was that service access and quality varied depending on distance from major centres. The Care Delivery Network (CDN) Project, a multi-year joint initiative of Queen's University and a private healthcare partner, grew out of this commitment to pursue the team's concerns. The CDN's objective was to foster research and development that would improve the integration of, and equitable access to, health service delivery across southeastern Ontario. To do this, the network depended on a team of academics and researchers from Queen's University's Faculty of Health Sciences and School of Business, and a broad range of healthcare practitioners in the region engaged in direct care delivery. The initial contact among the people who developed the CDN occurred through the professional and non-professional associations among them. Together, they formed a group of researchers, physicians, and knowledge managers who were prepared to contribute their time and combined effort. Initial informal conversations rapidly became the foundation for more formal networks. Specifically, the CDN was founded as a separate legal entity, and it in turn faced the challenge of spreading information across a wide range of geography, professionals, and existing healthcare networks. In this article, first we describe the diffusion of knowledge in organizations, with a focus on healthcare settings. Then we outline the history of the CDN project (1998-2001), followed by a discussion of findings based on the literature and the CDN experience. We close by summarizing and highlighting what we have learned.
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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 | 0.001 | 0.000 |
| 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 | 0.002 |
| 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".