Employee-Driven Innovation in Health Organizations: Insights From a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Employee-driven innovation (EDI) occurs when frontline actors in health organizations use their firsthand experience to spur new ideas to transform care. Despite its increasing prevalence in health organizations, the organizational conditions under which EDI is operationalized have received little scholarly attention. METHODS: This scoping review identifies gaps and assembles existing knowledge on four questions: What is EDI in health organizations and which frontline actors are involved? What are the characteristics of the EDI process? What contextual factors enable or impede EDI? And what benefits does EDI bring to health organizations? We searched seven databases with keywords related to EDI in health organizations. After screening 1580 studies by title and abstract, we undertook full-text review of 453 articles, retaining 60 for analysis. We performed a descriptive and an inductive thematic analysis guided by the four questions. RESULTS: Findings reveal an heterogeneous literature. Most articles are descriptive (n = 41). Few studies are conceptual and empirical (n = 15) and four are conference papers. EDI was often described as a participatory, learning innovation process involving frontline clinical and non-clinical staff and managers. Majority EDI were top-down, often driven by the organization's focus on participatory improvement and innovation and research-based initiatives. Five categories of methods is used in top-down EDI, two thirds of which includes a learning, a team and/or a digital component. Hybrid EDI often involves a team-based component. Bottom-up EDI emerged spontaneously from the work of frontline actors. Enablers, barriers, and benefits of EDI are seen at macro, organizational, team and individual levels; some benefits spread to other health organizations and health systems. CONCLUSION: This scoping review provides a comprehensive understanding of the organizational conditions under which EDI is operationalized. It offers insights for researchers, health organizations, and policy-makers about how and why frontline actors' involvement is crucial for the transformation of care.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".