Implementation of a Newborn Clinical Decision Support Software (NoviGuide) in a Rural District Hospital in Eastern Uganda: Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Lack of trained health care workers and nonadherence to national guidelines are key barriers to achieving high-quality newborn care in health care facilities in low- and middle-income countries. Traditional didactic approaches addressing these barriers fail to account for high staff turnover rates and result in temporary behavior change. NoviGuide, a clinical decision support software designed to standardize neonatal care through point-of-care assessments, has the potential to align bedside practice to national guidelines in settings lacking subspecialty neonatal providers. OBJECTIVE: This study aims to determine the adaptation, adoption, feasibility, acceptability, and sustainability of NoviGuide and its impact on nurse-midwives' knowledge in a rural hospital in eastern Uganda. METHODS: This mixed methods observational study was guided by the Proctor framework. Experts reviewed the clinical content of NoviGuide to ensure fidelity to Uganda guidelines. We enrolled nurses and midwives providing newborn care at Tororo District Hospital, trained them on NoviGuide use, and followed them for 12 months. We assessed adoption, feasibility, acceptability, and sustainability by analyzing NoviGuide use data, comparing it with maternity registry data and administering the System Usability Scale (SUS) and the Center for Health Care Evaluation Provider Satisfaction Questionnaire. We compared the mean knowledge assessment score at baseline, 6 months, and 12 months using a two-tailed t test. RESULTS: Five Ugandan experts suggested two minor changes to NoviGuide: the inclusion of an unsterile birth environment as an indication for empiric antibiotics and the addition of a reminder to follow-up with newborns with temperatures between 37.7°C and 37.9°C. Of the 19 nurse-midwives enrolled in February 2017, 74% (n=14) completed the follow-up in March 2018. The participants entered a total of 1705 assessments of varying newborn characteristics into NoviGuide throughout the day, evening, and night nursing shifts. The SUS score at the end of the study was very high (93.5, above the average of 68). Participants had a positive perception about NoviGuide, reporting that NoviGuide saved time (mean 5, SD 0) and prevented mistakes (mean 5, SD 0), and that they felt more confident in taking care of newborns when they used NoviGuide (mean 5, SD 0). Participants were highly satisfied with NoviGuide (mean 4.86, SD 0.36), although they lacked medical supplies and materials needed to follow NoviGuide recommendations (mean 3.3, SD 1.22). The participants' knowledge scores improved by a mean change of 3.7 (95% CI 2.6-4.8) at 6 months and 6.7 (95% CI 4.6-8.2) at 12 months (P<.001). CONCLUSIONS: NoviGuide was easily adapted to the Uganda guidelines. Nurse-midwives used NoviGuide frequently and reported high levels of satisfaction despite challenges with medical supplies and high staff turnover. NoviGuide improved knowledge and confidence in newborn care without in-person didactic training. NoviGuide use has the potential to scale up quality newborn care by facilitating adherence to national guidelines.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".