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
BACKGROUND: Designing implementation programs that effectively integrate complex healthcare innovations into complex settings is a fundamental aspect of knowledge translation. We describe the development of a conceptually grounded implementation program for a complex healthcare innovation and its subsequent application in pediatric hospital settings. METHODS: We conducted multiple case observations of the application of the Phased Reciprocal Implementation Synergy Model (PRISM) framework in the design and operationalization of an implementation program for a complex hospital wide innovation in pediatric hospital settings. RESULTS: PRISM informed the design and delivery of 10 international hospital wide implementations of the complex innovation, BedsidePEWS. Implementation and innovation specific goals, overarching implementation program design principles, and a phased-based, customizable, and context responsive implementation program including innovation specific tools and evaluation plans emerged from the experience. CONCLUSION: Theoretically grounded implementation approaches customized for organizational contexts are feasible for the adoption and integration of this complex hospital-wide innovation. Attention to the fitting of the innovation to local practices, setting, organizational culture and end-user preferences can be achieved while maintaining the integrity 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 | 0.161 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".