The Actionable Innovation Day (AID) Approach: An Option for Driving Healthcare Innovation (Preprint)
Bibliographic record
Abstract
BACKGROUND Innovation is essential to address healthcare challenges like resource limitations, overcrowded emergency departments, healthcare provider burnout, and more. Implementing innovation in healthcare faces barriers, including resistance to change, lack of multidisciplinary communication and alignment, resource constraints and more. The Actionable Innovation Day (AID) approach was developed to address these barriers, creating a model for distilling multidisciplinary, immediately applicable, consensus-based recommendations for actionable change. OBJECTIVE To evaluate the Actionable Innovation Day (AID) model as a structured approach for fostering multidisciplinary collaboration and generating actionable, system-level healthcare innovations. To assess the feasibility and impact of the AID model in identifying and prioritizing practical, low-cost solutions for improving critical care coordination, patient-centered care, and AI integration. METHODS Stakeholders, including multidisciplinary healthcare workers, administrators, patient partners, and industry participants within the Eastern Ontario region were invited to a first regional AID event focussed on acute and critical care. Conducted both in-person and virtually, AID featured (1) morning presentations from individual presenters offering recommendations, and (2) afternoon problem-oriented small-group breakout sessions to explore regional critical care solutions. Actionable recommendations were generated, and later rated with a 5-point agreement Likert scale (with email follow-up survey). RESULTS The regional AID event had 57 participants, including 16 multidisciplinary leaders offering a total of 42 recommendations. Each small group generated 5-8 recommendation, synthesized in discussion and analysis, leading to 28 recommendations were generated across four domains: Innovation in Healthcare, Regionalized Care, Critical Care Practices, and AI in Healthcare. 49 (86%) participants responded to the post-event survey, rating their level of agreement. 23 (82%) recommendations had a score > 4. Top recommendations included fostering partnerships between research, industry, and healthcare (Rating: 4.50/5), involving families in patient physical therapy in ICU and in home rehabilitation (4.46), and establishing centralized critical care coordination (4.44). CONCLUSIONS The AID approach emphasizes multidisciplinary and multilevel input, incremental and sustainable change, and practical, low-cost solutions, aligning with the Agile Innovation framework. This approach provides an optional model for coordinating meeting to catalyze 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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".