Implementing a complex hospital innovation: Conceptual underpinnings, program design and implementation of a Complex Innovation in an International Multi-Site Hospital Trial
Bibliographic record
Abstract
Abstract Background: Designing implementation programs that effectively integrate complex healthcare innovations is a fundamental aspect of knowledge translation. Good implementation design requires an active process. We describe a conceptually-grounded framework for the design of implementation programs for complex healthcare innovations and describe its implementation in pediatric hospital settings. Methods: We articulate the grounding implementation model and define overarching goals and principles of the implementation program design. Our Phased Reciprocal Implementation Synergy Model (PRISM) for implementation informed the articulation of the seven design principles: phased implementation approach, end-user engagement, responsive design, intentional integration with existing team processes, customizable implementation interventions, attendance to social mechanisms of influence, and inclusion of processes that support both learning and unlearning processes. These were then operationalized into implementation program, activities and innovation-specific tools. Results: The evidence informed and context responsive implementation program was applied to a complex, hospital wide implementation. Implementation interventions, education, communication and formative and summative evaluation tools were developed then customized for each participating hospital.Conclusion: Theoretically grounded and locally contexted implementation approaches are feasible for the adoption and integration of complex hospital wide innovations. Attention to the fitting of an innovation to local practices, the setting, organizational culture and end-user preferences can be achieved while maintaining the fidelity of the innovation.
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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 | 0.145 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".