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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Kefarmasian Indonesia
Rassyifa, Aloina Jean
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Engineering Health Professions Medicine & Pharmacology Public Health

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Effectiveness of artificial intelligence-driven chatbot responses in diabetes knowledge: a readability and reliability assessment Ghozali, Muhammad Thesa; Supadmi, Woro; Maharani, Fadya Bella Suci; Rassyifa, Aloina Jean
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2379-2388

Abstract

Patient education is vital in diabetes management, empowering patients with necessary knowledge and skills to manage condition effectively. However, traditional educational methods often face challenges such as limited access to healthcare professionals and variability in information quality. This study aimed to assess the reliability and readability of artificial intelligence (AI)-driven chatbot responses in disseminating diabetes knowledge. Technically, the diabetes knowledge questionnaire (DKQ-24) was administered to evaluate the effectiveness of AI-driven chatbot in disseminating diabetes-related information. Responses were evaluated for reliability and quality applying the modified DISCERN (mDISCERN) scale and global quality scale (GQS), and readability was assessed using the Flesch reading ease (FRE) score, Flesch-Kincaid grade level (FKGL), gunning fog index (GFI), Coleman-Liau index (CLI), and simple measure of gobbledygook (SMOG). The mean mDISCERN score was 31.50±2.89, indicating generally reliable responses. The median GQS score was 4, reflecting the high overall quality. The readability assessment revealed a mean FRE score of 66.30, indicating that the text was fairly easy to read. FKGL mean score was 6.54±3.19, suggesting the text was suitable for readers at a sixth-grade level. In conclusion, AI-driven chatbot provides reliable and high-quality information on the diabetes self-management, but it requires improvements to enhance accessibility.
An analysis of the unified theory of acceptance and use of technology (UTAUT) to the adoption of electronic medical records in hospital settings Ghozali, Muhammad Thesa; Rassyifa, Aloina Jean; Faradiba, Faradiba; Damanik, Ferina Septiani; Sari, Seftika; Satibi, Satibi; Fortwengel, Gerhard
JURNAL ILMU KEFARMASIAN INDONESIA Vol 23 No 2 (2025): JIFI
Publisher : Faculty of Pharmacy, Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/jifi.v23i2.1693

Abstract

Electronic Medical Records (EMRs) are increasingly recognized as vital tools for enhancing the efficiency, accuracy, and quality of healthcare delivery. Despite regulatory mandates in Indonesia, the adoption of EMRs remains uneven, particularly in rural healthcare settings. This study applied the Unified Theory of Acceptance and apply of Technology (UTAUT) to investigate the behavioral intention of healthcare professionals working in private hospitals to use electronic medical records. A quantitative, cross-sectional design was implemented involving 90 participants selected through purposive sampling in an Indonesian hospital. The study's data were gathered between October 2024 and January 2025 using a validated 18-item UTAUT-based questionnaire. Data analysis was conducted with SPSS and SmartPLS software. Results indicated that all four UTAUT construct – Performance Expectancy (β = 0.200, p = 0.016), Effort Expectancy (β = 0.353, p < 0.001), Social Influence (β = 0.291, p < 0.001), and Facilitating Conditions (β = 0.262, p = 0.008 – had statistically significant positive effects on Behavioral Intention. The model demonstrated moderate explanatory power (R² = 0.655) and strong predictive relevance (Q² = 0.512). These results validate the UTAUT model's suitability in this context and provide practical insights for strengthening EMR implementation strategies. Future research should consider longitudinal approaches and multi-site comparisons to enhance generalizability and policy relevance.
Co-Authors Faradiba Faradiba Ferina Septiani Damanik Fortwengel, Gerhard Maharani, Fadya Bella Suci Muhammad Thesa Ghozali Satibi Satibi Woro Supadmi