Mothers’ Assistant for Labor analgesia (MALA): a novel artificial intelligence interactive avatar for patient education in obstetric anesthesia

Abstract

Background

Epidural analgesia is the gold standard for labor pain relief, yet performing patient education remains challenging due to time constraints, language barriers, and variable health literacy. Traditional educational approaches often fail to ensure adequate patient understanding. Artificial intelligence (AI) interactive avatars offer a novel solution for delivering standardized, empathetic, and accessible patient education. This study evaluates the content validity and usability of the Mothers’ Assistant for Labor Analgesia (MALA), a multilingual AI avatar designed for obstetric anesthesia education.

Methods

A cross-sectional, descriptive validation study was conducted using structured expert review. MALA, a digital interactive avatar supporting 28 languages was developed on the HeyGen, incorporating evidence-based content on epidural analgesia, alternatives, and frequently asked questions. Ten experts (5 obstetric anesthesiologists, 5 obstetricians) independently reviewed the avatar’s interaction and rated 10 domains on a 4-point Likert scale. Content Validity Indices (I-CVI and S-CVI/Ave) were calculated.

Results

All domains received high ratings (mean scores ≥ 3.2). The highest scores were for empathy (mean 3.7 ± 0.48), usefulness in decision-making (3.6 ± 0.52), and accuracy (3.4 ± 0.70). Eight domains achieved an I-CVI of 1.00; two domains scored 0.90. The S-CVI/Ave was 0.98, indicating strong expert consensus. Patient Education Materials Assessment Tool also showed high understandability (87.1 %) and actionability (83.3 %).

Conclusion

MALA demonstrated excellent content validity and usability as an educational tool for labor analgesia. Integration of AI avatars like MALA may standardize patient education and reduce clinician workload. Future research should assess patient-centered outcomes and clinical integration.