The Actionable Innovation Day Approach: Participatory Model for Advancing Critical Care Innovation
Bibliographic record
Abstract
Unlabelled: Health care innovation is essential for improving patient outcomes, enhancing system efficiency, and preparing for future challenges; however, meaningful progress is often hindered by entrenched barriers such as resistance to change, fragmented interdisciplinary collaboration, and constrained financial and human resources. These persistent obstacles make it difficult for health care systems to translate creative ideas into sustainable, real-world improvements, underscoring the need for structured approaches that support collaboration and reduce implementation friction. To address these challenges, we developed the Actionable Innovation Day (AID) approach, a structured, participatory model designed to generate consensus-based, low-cost recommendations that are feasible for system improvement. The first regional AID event in Eastern Ontario gathered 57 multidisciplinary participants, including clinicians, administrators, patient partners, and industry leaders, for a full-day series of presentations, facilitated discussions, and targeted breakout sessions focused on critical care. Through guided deliberation and collaborative analysis, participants synthesized diverse perspectives into a prioritized set of improvement opportunities. The process yielded 28 actionable recommendations across 4 domains: health care innovation, regionalized care, critical care practices, and the use of artificial intelligence. A postevent survey (86% response rate) showed strong agreement, with 23 recommendations rated above 4 on a 5-point scale. The highest-ranked proposals emphasized the value of strengthening research-industry-clinical partnerships, integrating families more intentionally into intensive care unit rehabilitation and recovery processes, and implementing centralized regional coordination to optimize critical care capacity. Together, these findings illustrate not only the feasibility of the AID model but also the AID model's ability to surface strategic, context-appropriate solutions that resonate across stakeholder groups. The AID process offers a scalable and adaptable template for advancing health care innovation through collaborative, real-world problem-solving. While this initial event focused on critical care, the underlying principles of structured engagement, iterative consensus building, and interdisciplinary co-design are broadly applicable to many sectors of health care. We encourage institutions, regional networks, and health system leaders to adopt and tailor the AID framework to their own local priorities, recognizing that inclusive innovation processes can accelerate system improvement even in resource-limited settings. Ultimately, the AID approach serves as both a methodology and a call to action: by empowering teams to collectively identify, refine, and champion actionable ideas, health care organizations can build the capacity and culture necessary to drive meaningful and sustained innovation across diverse clinical and operational domains.
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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 | 0.005 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 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".