Applying the principles of Design Thinking to the Intensive Care Environment
Bibliographic record
Abstract
Patient-centered care and evidence-based medicine are mantras that form the foundation of modern healthcare practice. Yet, most of the tools for designing healthcare solutions, including randomized control trials, quality improvement initiatives, and even qualitative research, are often better adapted to providing an evidence-based foundation for practice at the expense of patient, family, and healthcare worker needs. These approaches tend to focus on improving outcomes and processes (Grys, 2022). Stakeholders — particularly patients, but also staff – are objects being scrutinized by “experts” under the scientific or medical gaze, filtering out what the “expert” deems as irrelevant (O’Callaghan, 2022). However, outcomes and processes are not sufficient if they do not prioritize human voice, dignity, and participation. Design thinking provides a complementary approach to evidence-based medicine by engaging the person in experimenting, prototyping, giving feedback, and redesigning healthcare solutions centered around the needs of humans (Razzouk & Shute, 2012). This article describes the process of design thinking as an approach to the creation of human-centered solutions and makes reference to the implementation of the design thinking process in the intensive care unit (ICU) of SickKids, an academic paediatric hospital in Ontario.
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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 | 0.059 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.070 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".