Appointments by Choice: An Implementation Pilot Study for Patient-Initiated Follow-Up Care in Rheumatoid Arthritis
Notice bibliographique
Résumé
Objectives The aim of the study was to conduct an implementation pilot for Appointments by Choice (ABC), a new patient-initiated follow-up model seeking to optimize follow-up efficiency and patient-centeredness of rheumatoid arthritis (RA) care. Objectives were to evaluate patient recruitment and early implementation outcomes. Methods The implementation pilot started in January 2024 at a single rheumatology clinic in Calgary. Eligible patients had (1) established RA, (2) well-controlled disease, (3) no major medication changes, and (4) no other active complex conditions. Patients and providers used a discussion tool for shared decision-making about moving from regular care to the ABC pathway.[1] This included scheduling a new follow-up interval of 12-24+ months, a reduction compared to usual care. Between rheumatologist appointments, patient care was managed through a pharmacist-led clinic. Self-care was encouraged using a flare action plan. Baseline demographics were collected via survey and chart review. Feasibility was measured through recruitment numbers, ABC pathway adherence, flare clinic workload, and implementation adaptations using the FRAME criteria.[1] Preliminary data on ABC recruitment and feasibility were summarized using descriptive statistics. Results Over 8 months, 38/108 (35.2%) eligible individuals with RA chose to adopt the ABC pathway (Figure). 28 participants provided reasons for declining. Common reasons included lack of time/interest in research (n=6), concerns about reduced care access (n=3), and preference for usual care (n=5). Mean participant age was 59.5±11.0 years, with 82.7% identifying as White and 10.3% as Southeast Asian. Mean RA duration was 13.3±9.1 years. Only 2/38 (5.3%) participants withdrew from the study and returned to usual care, due to a major RA flare or inability to complete the baseline questionnaire. 36/38 (94.7%) remained on the pathway. The pharmacist-led flare clinic conducted 2 flare-related follow-up calls, 2 medication renewals, and 8 calls for other medical needs. One participant required an in-person follow-up. 32 implementation challenges were noted, 8 of which resulted in minor adaptations. Adaptations include opening recruitment to individuals with (1) RA with minor medication changes and (2) those with palindromic rheumatism who were on treatment and had positive serology; (3) adjusting recruitment timing to align with biologic renewal schedules, and (4) improving physician-pharmacist communication using a standardized electronic health record “smartphrase” for detailing follow-up needs. Conclusion The ABC implementation pilot has provided valuable learnings for recruitment, implementation, and ongoing care when using patient-initiated follow-up models for RA care. Post-pilot analyses will provide additional insights into ABC safety, feasibility, and potential benefits. [1.] Wiltsey Stirman S. Implementation Science 2019;14:58. Supported by a CIORA grant
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 | 0,032 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».