Developing a Quality Improvement Framework to Enhance the Health System User Experience for Individuals Living With Type 1 Diabetes: The Reshape T1D Study
Bibliographic record
Abstract
INTRODUCTION: User experience design aims to create products and services that are accessible, usable, and enjoyable. The Reshape T1D study aims to apply these principles to understand how individuals living with T1D interact with and experience healthcare to inform T1D clinical quality improvement. METHODS: Using a community-based participatory research design, we involved four patients and four clinicians as co-researchers throughout the research. A questionnaire and virtual semi-structured interview were applied across a purposeful sample of 41 adults living with T1D across Alberta, Canada, between September 2021 and May 2022. Audio recordings were transcribed verbatim and de-identified before coding. Thematic analysis was conducted on coded participant discourse through multiple coders. RESULTS: Participants indicated the need for a centralized hub that provides consistent, reliable, and up-to-date T1D education and resources and an emphasis on access to mental health resources within T1D care settings. Providing greater flexibility for appointment types (ie. in-person, virtual, etc.) and after-hours access contributed to better self-management and prevented emergency room visits. Participants desired a choice as to who comprises their T1D care team and for teams to address patient needs specific to their reality. We identified that medical trauma had long-term impacts on perceptions of healthcare and contributed to a reluctance to seek future care. Women expressed challenges in discussing reproductive health with their clinicians. Diabetes online communities provide an adjunct to clinical care through peer support. Cost and access to the latest technology are ongoing barriers for many participants, especially concerning publicly funded programmes that use advanced insulin pump therapy, continuous glucose monitoring, and automated insulin delivery systems. A quality improvement framework emerged through data analysis, and findings were synthesized into actionable recommendations for ongoing clinical quality improvement. CONCLUSION: Our findings highlight how important health system user suggestions are for more equitable, accessible, and empathetic healthcare for individuals living with T1D. Further work is needed to explore health system user experiences with clinicians and healthcare administrators to effectively carry out T1D clinical quality improvement.
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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 | 0.079 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".