Assessing the Utility of a Patient-Facing Diagnostic Tool Among Individuals With Hypermobile Ehlers-Danlos Syndrome: Focus Group Study
Notice bibliographique
Résumé
BACKGROUND: Hypermobile Ehlers-Danlos syndrome (hEDS), characterized by joint hypermobility, skin laxity, and tissue fragility, is thought to be the most common inherited connective tissue disorder, with millions affected worldwide. Diagnosing this condition remains a challenge that can impact quality of life for individuals with hEDS. Many with hEDS describe extended diagnostic odysseys involving exorbitant time and monetary investment. This delay is due to the complexity of diagnosis, symptom overlap with other conditions, and limited access to providers. Many primary care providers are unfamiliar with hEDS, compounded by genetics clinics that do not accept referrals for hEDS evaluation and long waits for genetics clinics that do evaluate for hEDS, leaving patients without sufficient options. OBJECTIVE: This study explored the user experience, quality, and utility of a prototype of a patient-facing diagnostic tool intended to support clinician diagnosis for individuals with symptoms of hEDS. The questions included within the prototype are aligned with the 2017 international classification of Ehlers-Danlos syndromes. This study explored how this tool may help patients communicate information about hEDS to their physicians, influencing the diagnosis of hEDS and affecting patient experience. METHODS: Participants clinically diagnosed with hEDS were recruited from either a medical center or private groups on a social media platform. Interested participants provided verbal consent, completed questionnaires about their diagnosis, and were invited to join an internet-based focus group to share their thoughts and opinions on a diagnostic tool prototype. Participants were invited to complete the Mobile App Rating Scale (MARS) to evaluate their experience viewing the diagnostic tool. The MARS is a framework for evaluating mobile health apps across 4 dimensions: engagement, functionality, esthetics, and information quality. Qualitative data were analyzed using affinity mapping to organize information and inductively create themes that were categorized within the MARS framework dimensions to help identify strengths and weaknesses of the diagnostic tool prototype. RESULTS: In total, 15 individuals participated in the internet-based focus groups; 3 (20%) completed the MARS. Through affinity diagramming, 2 main categories of responses were identified, including responses related to the user interface and responses related to the application of the tool. Each category included several themes and subthemes that mapped well to the 4 MARS dimensions. The analysis showed that the tool held value and utility among the participants diagnosed with hEDS. The shareable ending summary sheet provided by the tool stood out as a strength for facilitating communication between patient and provider during the diagnostic evaluation. CONCLUSIONS: The results provide insights on the perceived utility and value of the tool, including preferred phrasing, layout and design preferences, and tool accessibility. The participants expressed that the tool may improve the hEDS diagnostic odyssey and help educate providers about the diagnostic process.
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 | 0,020 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».