Text-Based Messaging to Support Rheumatoid Arthritis Care: An Analysis of the Frequency and Content of Text Messages
Notice bibliographique
Résumé
Objectives Rheumatoid arthritis (RA) requires regular follow-up appointments with rheumatologists to monitor disease activity, however, this may be difficult to achieve due to physician and patient schedules and a limited rheumatology workforce.[1] Virtual care has been used to improve health care delivery by connecting patients with healthcare providers (HCPs).[2] In this study, it was used to enhance RA care by allowing patients to connect with their rheumatology team in between appointments on a secure two-way text-messaging platform called WelTel. The objectives of this study were to 1) analyze the frequency and content of text messages sent by patients to their HCPs, and 2) to determine patient characteristics that were associated with higher texting frequency. Methods Seventy participants diagnosed with RA participated in a 6-month pilot using the WelTel platform. Automated “How are you?” texts were sent monthly, and participants were encouraged to respond according to their current situation. Participants could also initiate messages if they had questions/concerns between appointments. Text messages were monitored and answered primarily by the clinical pharmacist for the rheumatology clinic. Qualitative content analysis was conducted to thematically categorize and quantify common words and phrases. Once categories were quantified and themes were established, regression analysis was conducted to determine if a relationship existed between the number of text messages and age, sex, care complexity level, number of medications, and burden of comorbidities. Care complexity was measured using the Intermed Self-Assessment, a validated patient-reported instrument that assesses biopsychosocial complexity. Results A total of 1404 text messages were sent by patients with 257 messages requiring a response from the participating pharmacist. Three main content themes were identified: RA symptoms, medication questions, and COVID-19 concerns. There was a significant association between text messaging frequency and patient care complexity levels (p = 0.025), however, no association was identified between text messaging frequency and age, sex, number of medications, or burden of comorbidities. Conclusion The present study piloted the novel use of text messaging using the WelTel virtual care platform for providing additional RA care in between rheumatologist visits. Our analysis identified the common concerns that patients raise with their care team via messaging. The content of text messages received was highly relevant and directly related to patient care needs. Patient care complexity was associated with significantly more text messages to discuss health concerns, highlighting a population who may benefit in particular from the intervention. [1.] Smolen J. Ann Rheum Dis 2010;69:631-7. [2.] Gajarawala S. J Nurse Pract 2021;17:218-2.
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 | 0,004 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».