Using design-thinking to investigate and improve patient experience
Bibliographic record
Abstract
Understanding and enhancing the patient experience can lead to improved healthcare outcomes. The purpose of this study was to capture a comprehensive and nuanced understanding of the patient experience on an inpatient medical teaching unit in order to identify key deficiencies and unmet needs. We then aim to implement a design-thinking methodology to find innovative ways to solve these deficiencies. Here we present the first two phases of this four-phased study. We retrospectively and prospectively collected quantitative data about patient experience with the Canadian Patient Experiences Survey-Inpatient Care. We then used this data to guide patient interviews. We identified several key deficiencies including call bell response times, noise levels at night, pain control, education about medication side effects, communication between healthcare team members, and how well healthcare team members remain up to date about patient care. In the final two phases of our study, we will select one or more of these deficiencies and collaborate with patients and other stakeholders to rapidly create, employ, and assess the impact of prototypes through an iterative action cycle until effective and sustainable solutions are found. Experience Framework This article is associated with the Innovation & Technology lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens
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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 | 0.086 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".