Exploring two-way text messages for post-discharge follow-up and quality improvement in rural Uganda
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".