Organizational Climate and Decision Aid Sustainability in Lupus Care: Mixed Methods Study
Notice bibliographique
Résumé
Background: Digital decision aids (DAs) are increasingly used in health care to support shared decision-making and promote patient engagement. In the context of systemic lupus erythematosus (SLE), a complex autoimmune disease characterized by diverse symptoms and uncertain prognoses, DAs offer guidance to patients in navigating treatment risks and benefits. Although numerous studies have examined the initial implementation of evidence-based tools, there is limited evidence on the organizational factors that influence their long-term sustainability in clinical practice. This gap is particularly salient for digital interventions, where integration into routine workflows and ongoing use require alignment with clinic readiness and culture. This study focuses on an evidence-based, electronic DA designed to support patients with lupus and investigates how dimensions of organizational climate, particularly learning climate and change readiness, are associated with the tool's sustained use across diverse practice settings. Objective: This study aims to examine the relationship among the learning climate, change readiness climate, and perceived permanence of a DA for patients with lupus in 15 geographically diverse rheumatology clinics in the United States. Methods: This study was conducted as part of a broader multisite implementation project. We used a concurrent mixed methods design, integrating longitudinal quantitative survey data with qualitative interviews. Quantitative data were collected via web-based surveys at 3 time points (6-, 12-, and 24-mo postimplementation) from physicians, nurses, medical assistants, and administrative personnel (n=204 responses across rounds). The primary outcome was perceived DA permanence, measured with a validated 5-item scale. Independent variables included internal and external learning climates, change commitment, and change efficacy. Data were aggregated to the clinic level and analyzed using generalized estimating equations with clustered SEs. Qualitative data were collected through 36 semistructured interviews with clinic staff to explore contextual factors affecting sustainment. Results: Quantitative findings revealed that change efficacy climate was significantly associated with greater perceived permanence of the DA (β=4.00; P<.001) while internal (β=-1.39; P<.05) and external learning climates (β=-2.11; P<.01) were negatively associated. Change in commitment was not statistically significant. Qualitative data highlighted challenges to sustainment, including poor workflow integration, lack of physician buy-in, and limited applicability of the DA to certain patient populations. Conclusions: Sustaining digital health tools like DAs requires not only technical integration but also a supportive organizational climate. This study demonstrates that perceptions of a clinic's collective ability to sustain change (change efficacy) are critical while learning climates may expose barriers that hinder long-term use. These findings underscore the importance of assessing organizational readiness and tailoring implementation strategies to foster DA sustainment in real-world settings.
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 | 0,057 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».