Underdiagnosed and undertreated peripheral arterial disease: Using design thinking to establish priorities for peripheral arterial disease care
Bibliographic record
Abstract
ABSTRACT Introduction Design thinking (DT), a methodology for solving complex problems, has the potential to create powerful, human-centred healthcare improvement. We applied DT methodology to the context of peripheral arterial disease (PAD). PAD is increasingly prevalent globally and associated with significant morbidity and mortality. We fall short of achieving effective secondary prevention due to persistent underdiagnosis and undertreatment of this disease. In this study, we sought to identify novel and creative solutions to improve diagnosis and secondary prevention of PAD. Methods We describe the initial ‘Empathize’, ‘Define’, and ‘Ideate’ stages of the five-stage DT model proposed by the Hasso Plattner Institute of Design at Stanford University. We engaged patients with PAD, caregivers, clinicians, and other stakeholders in a co-design process using semi-structured interviews, a DT workshop, and post-workshop survey. Data from the interviews and workshop were analyzed using inductive thematic analysis, and data from the survey were analyzed using an idea prioritization matrix. Results Exploring the lived experience of those with PAD and those delivering PAD care emphasized the influence of system-level barriers. Many of the solutions proposed by workshop participants target evidence-based, system-level interventions through improved funding support, institutional support, outreach efforts and technological applications. The connections between insights derived in the ‘Empathize’ stage and solutions proposed during the ‘Ideate’ stage showed the success of the co-design process in inspiring empathy-driven solutions. Discussion This study demonstrates how DT methodology can be applied to complex healthcare problems such as PAD care, to systematically develop human-centred solutions. In the next stages of this study, we will use the results of this co-design process to iteratively implement, evaluate, and optimize the proposed solutions which were prioritized as being most feasible and high impact. KEY MESSAGES What is already known on this topic Peripheral arterial disease (PAD) is increasingly prevalent globally. The significant morbidity and mortality associated with PAD can be reduced with timely diagnosis and the effective use of secondary preventative therapies; however, PAD remains underdiagnosed and undertreated compared to other atherosclerotic diseases. What this study adds This study is novel in its application of design thinking methodology and a co-design approach to work together with people with lived experience of PAD, to establish priorities for PAD care. How this study may affect research, practice or policy – Insights from this study emphasize system-level barriers which prevent effective delivery and uptake of PAD care. Solutions that are human-centred and co-produced with patients and key stakeholders should improve institutional and governmental support for implementation of evidence-based best practices; this will be investigated further in the next stages of this study.
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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 | 0.082 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".