Tailoring Rheumatoid Arthritis Visit Timing Based on mHealth App Data: Mixed Methods Assessment of Implementation and Usability
Notice bibliographique
Résumé
Background: Visits to medical subspecialists are common, with follow-up timing often based on heuristics rather than evidence. Unnecessary visits contribute to long wait times for new patients. Specialists could enhance visit timing and reduce frequency by systematically monitoring patients' symptoms between visits, especially for symptom-driven conditions like rheumatoid arthritis (RA). We previously designed an intervention using a mobile health (mHealth) app to collect patient-reported outcomes (PRO). One of several aims of the app was to assist rheumatologists in determining visit timing for patients with RA. The intervention did not reduce visit frequency. Objective: To explore possible reasons for the lack of association between the intervention and visit frequency, we describe app usage, assess usability, and identify barriers and facilitators for using between-visit PRO data to reduce visits when patients' symptoms are stable. Methods: We analyzed patients' use of the app by reporting adherence (percent of PRO questionnaires completed during the 12-month study) and retention (use in the last month of the study). To examine rheumatologists' experiences, we summarized views of the electronic health record (EHR)-embedded PRO dashboard and EHR inbox messages suggesting early or deferred visits. We assessed app usability using the interactive mHealth App Usability Questionnaire for Ease of Use and Usefulness for patients and the System Usability Scale for rheumatologists. We assessed rheumatologist-level effects of intervention usage using Kruskal-Wallis rank sum and equality of proportion tests. We identified barriers and facilitators through interviews and surveys. Results: The analysis included 150 patients with RA and their 11 rheumatologists. Patients answered a median of 53.3% (IQR 34.1%-69.2%) of PRO questionnaires; this proportion varied by rheumatologist (range 40.7%-67%). Over half of the patients used the app during the final month of the study (56%, range 51%-65%, by rheumatologists); the median number of months of use was 12 (IQR 9-12). Rheumatologists viewed the dashboard 78 times (17.6% of 443 visits) with significant differences in viewing rates by rheumatologist (range 10%-66%; P<.01). There were 108 generated messages sent to rheumatologists suggesting a deferred visit (24.4% of 443 visits) with significant differences in message counts received per visit by rheumatologist (range 10.8%-22.6%; P=.03). Rheumatologists' reported barriers to offering visit deferrals included already scheduling as far out as they were comfortable and rescheduling complexities for staff. Based on 39 patient interviews and 44 surveys, patients reported 2 main barriers to app usage: questionnaire frequency not being tailored to them and reduced motivation after not discussing PRO data with their rheumatologist. A total of 5 interviewed patients received the option to defer their visits, of which 3 elected to defer the appointment and 2 chose to keep it. Conclusions: While an mHealth app for reporting RA PROs was used frequently by patients, using these data to reduce the frequency of unneeded visits was not straightforward. Better engagement of clinicians may improve the use of PRO data.
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 | 0,119 | 0,124 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».