47 Increase Clinic Attendance Among Adolescents and Young Adults: A novel cost-effective method
Notice bibliographique
Résumé
Abstract Background Transition clinics have been introduced to address the unique needs of adolescent and young adult (AYA) populations, however clinic attendance continues to be an issue. Although poor clinic attendance among the AYA population has been well known, solutions to address this has been limited. Some factors that have been associated with missed appointments include forgetfulness, negative previous clinic experiences and clinic schedules. With widespread use of digital technologies among AYA, the application of digital solutions to increase attendance at healthcare appointments has been explored, but little is known on its effectiveness. Objectives To determine the effect of text messaging appointment reminders on uninformed no show rates in an AYA transition clinic. Uninformed no show rate was defined as an absence from clinic (not related to a medical emergency) without communication with the clinic. Design/Methods A pilot prospective cohort study with a retrospective control group was conducted in an AYA general hematology transition clinic. In order to establish the current no show rate at the clinic, a retrospective review of AYA patients who attended the clinic between April 2013-August 2015 was conducted. Thereafter, all patients who had an appointment scheduled between February 2016 and December 2017 were included in the study and received a text message reminder of their appointment 48 hours prior to their appointment. Monthly uninformed no show rates were collected, and a student’s t-test was conducted to determine if there was a significant difference in uninformed no show rates before and after the introduction of text message reminders. Results Eighty-six participants consented to participate in the study and received a text message reminder of their appointment. From April 2013- August 2015 a total of 51 clinic days with 236 appointments occurred. During this time the mean uninformed no show rate was 39.9%. SMS appointment reminders were sent from February 2016 to December 2017 for 48 clinic days for a total of 206 clinic appointments. The mean uninformed no show rate after the introduction of text message appointment reminders was 22.6%. The introduction of text messaging appointment reminders significantly (p<0.01) decreased uninformed no show rates by 17.3%. Conclusion Text message reminders are an effective low cost method in reminding AYA patients about their appointments. By using innovative, cost effective and practical strategies like text messaging reminders to increase clinic attendance, we not only improve the care of our patients but also reduce the financial and clerical burden to the system resulting from missed appointments.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,002 |
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 ».