Utilizing Gamification, Artificial Intelligence, and mHealth for the Professional Development of Maternal Care Providers: Exploratory Pilot Cross-Sectional Study Assessing Providers' Satisfaction in Primary Health Care Centers in Lebanon
Bibliographic record
Abstract
Background: High maternal morbidity and mortality rates globally, especially in low-income and lower-middle-income countries, highlight the critical role of skilled health care providers (HCPs) in preventing pregnancy-related complications among disadvantaged populations. Lebanon, hosting over 1.5 million refugees, is no exception. HCPs face significant challenges, including resource constraints and limited professional development opportunities, underscoring the need for continuous learning and innovative educational interventions. Artificial intelligence (AI) and gamification show promise in enhancing clinical performance and evidence-based practice. Objective: Considering the limited evidence on the effectiveness of integrating gamification and AI in a mobile app for professional development of HCPs providing maternal health services, this pilot study aims to assess the satisfaction and acceptability of HCPs with a novel mLearning tool, titled the "GAIN MHI" app (gamification, artificial intelligence, and mHealth network for maternal health improvement), at selected primary health care centers in Lebanon. Methods: This is a cross-sectional study that presents data collected from 12 participating HCPs, primarily obstetricians and midwives who have been using the GAIN MHI mobile app for professional development and learning. The survey used included Likert scale questions to assess HCPs' satisfaction, engagement, and evaluation of the gamification and AI components of the app. Open-ended questions gathered qualitative feedback on app preferences and potential improvements. Statistical analysis was performed to derive insights from the quantitative data collected. Subsequently, a descriptive analysis was performed, presenting the frequencies and percentages of various participant characteristics, as well as responses to the survey across all sections. Results: A total of 85% (n=10) of the HCPs, including midwives and doctors, were satisfied with the GAIN MHI mobile app, the user interface, and various content features. Engagement levels were robust (64.6%, SD 6.2%), notably impacting clinical routines and theoretical knowledge. The gamification and AI components garnered strong positive feedback, enhancing learning enjoyment (11/12, 92%). From a qualitative perspective, users expressed appreciation for the app's diverse content, user-friendliness, and motivation for continuous learning. Suggestions for expanding the content included a wide range of health topics, highlighting the app's potential applicability in various health care fields. Conclusions: HCPs, especially those practicing in underserved areas, face challenges in accessing professional development opportunities, highlighting the need for innovative pedagogical approaches using mobile technologies. This pilot study underlines the potential of using AI-based digital solutions for professional development with the aim of improving the quality of health services-in this case, maternal health services-through continuous learning and updates on the most recent evidence-based clinical guidelines. Future research should investigate the feasibility of applying similar solutions on a larger scale to reach a wider range of HCPs and cover other health topics. The applicability of such solutions in different contexts and low-resource settings should also be explored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".