Exploring two-way text messages for post-discharge follow-up and quality improvement in rural Uganda
Notice bibliographique
Résumé
Background: Automated messaging through text (SMS) and instant messaging services (IMS) are low cost solutions for follow-up of patients in resource constrained contexts. This study aims to evaluate a quality improvement (QI) initative to improve caregiver response rates to an automated messaging system facilitating follow-up after hospital discharge of children in rural Uganda. Methods: This initative was implemented at Gulu Regional Referral Hospital in Northern Uganda from June 2022 to June 2024. Caregivers of children who were triaged through the Smart Triage digital platform were offered an automated follow-up program as part of routine care during this time period. SMS and IMS (WhatsApp) messages prompting caregivers to report if their child had “improved” or “not improved” were sent seven days post-discharge. Non-responders and "not improved" cases were escalated to a phone call from a health worker. From April 2023 to June 2024, a QI initiative refined the messaging system to improve response rates. Data on message delivery, response rates, improvement strategies, and health outcomes were analyzed. Results: Of 6826 participants, 6469 (95%) messages were successfully delivered. Response rates improved from 20% to 40%. In total, 1856 caregivers responded to the messages. Among the responses, 1244 (67%) of caregivers reported improvement and 612 (33%) reported no improvement. Follow-up phone calls for those “not improved” revealed 58 (9%) sought care, 12 (2%) were readmitted, and no deaths occurred. For non-responders, 206 (5%) sought care, 33 (0.7%) were readmitted, and 3 (0.07%) deaths occurred. Discussion: Automated two-way text messages for post-discharge pediatric follow-up in Uganda yielded high delivery but moderate response rates. Iterative QI efforts increased response rates, highlighting the importance of tailored communication strategies. Automated messages can facilitate timely intervention for high-risk children and enable efficient collection of health outcomes offering a viable alternative to in-person follow-up in resource poor settings. Data Collection Methods: Responses to the automated messages were automatically recorded in a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada) in real-time, ensuring accurate and timely data entry. During the follow-up phone calls, caregivers were asked a standardized set of questions about their child’s health and the study nurses manually entered the responses into REDCap immediately following the interaction. Ethics Declaration: This study was approved by the institutional review boards at Makerere University School of Public Health in Uganda (SPH-2021-41) and the Uganda National Council for Science and Technology (HS1745ES). Ethics approval for this study was not required by the University of British Columbia’s (UBC) Research Ethics Board. UBC adheres to the Canadian government’s Tri-Council Policy 2 (TCPS2) Statement which states that quality assurance and quality improvement (QA/QI) studies, program evaluation activities, and performance reviews, or testing within normal educational requirements, when used exclusively for assessment, management or improvement purposes, do not constitute research under the TCPS 2 and do not fall under the scope of REB review.
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 | 0,029 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».