Underdiagnosed and undertreated peripheral arterial disease: Using design thinking to establish priorities for peripheral arterial disease care
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,082 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,018 |
| Communication savante | 0,014 | 0,008 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».