Exploring two-way text messages for post-discharge follow-up and quality improvement in rural Uganda
Bibliographic record
Abstract
INTRODUCTION: Automated messaging through text (SMS) and instant messaging services (IMS) offers low-cost solutions for patient follow-up in resource-constrained contexts. This study aims to evaluate a quality improvement (QI) initiative to improve caregiver response rates to an automated messaging system for post-discharge follow-up of children in rural Uganda. METHODS: From June 2022 to June 2024, caregivers of children triaged through the Smart Triage platform at Gulu Regional Referral Hospital were invited to participate in an automated follow-up program. Messages were sent seven days after discharge via SMS and IMS (WhatsApp), prompting caregivers to report if their child had "improved" or "not improved". Non-responders and "not improved" cases were followed up with a phone call from a study nurse. From April 2023 to November 2023, a QI initiative refined the messaging system to improve response rates and a post-QI period then continued the intervention with no changes until June 2024. Response rates were analyzed over three periods: historical (pre-QI, June 2022 - March 2023), QI intervention, and post-QI. Additionally, data on message delivery rates, improvement strategies, and health outcomes were analyzed. RESULTS: Of 6826 participants, 6469 (95%) messages were successfully delivered. Response rates improved from 20% in April 2023 to 40% in June 2024 and remained stable between 33% and 41% during the post-QI period. Compared to the historical period, post-QI response rates were significantly higher (95% CI: 12.5% to 18.2%, p < 0.001). This improvement reflected a statistically significant positive trend during the QI period. Overall, 1856 caregivers responded: 1244 (67%) 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 yielded high delivery and 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.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".