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Journal of Advanced Health Informatics Research
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856124     DOI : https://doi.org/10.59247/jahir.v1i1
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering Medicine & Pharmacology Public Health
Journal of Advanced Health Informatics Research (JAHIR) is a scientific journal that focuses on the application of computer science to the health field. JAHIR is a peer-reviewed open-access journal that is published three times a year (April, August and December). The scientific journal is published by Peneliti Teknologi Teknik Indonesia (PTTI). The JAHIR aims to provide a national and international forum for academics, researchers, and professionals to share their ideas on all topics related to Informatics in Healthcare Research
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 42 Documents
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Development of Smartotc Mobile Application with Clinical Decision Support System (CDSS) to Support Pharmacists in Managing Common Cold Ikhwan Yuda Kusuma; Khaniva Khalilia Alazhar; Peppy Octaviani; András Érszegi; Muhammad Iqbal; Muhammad Syaiful Aliim
Journal of Advanced Health Informatics Research Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v4i1.423

Abstract

Common cold is a highly prevalent self-limiting respiratory illness and a frequent reason for self-medication and community pharmacy consultations. Variability in pharmacists’ clinical decision-making may affect the quality of care, highlighting the need for evidence-based decision-support tools. This study developed SmartOTC, a mobile application incorporating a Clinical Decision Support System (CDSS) to support pharmacists in managing common cold cases through symptom assessment, over-the-counter medication recommendations, patient education, and referral support. A pilot quasi-experimental before-and-after study was conducted among 10 community pharmacists in Indonesia as part of a preliminary evaluation of the application. The application was developed using Android Studio and a Python-based CDSS algorithm and evaluated through functional feasibility, task performance, usability, user experience, and expert validation assessments. SmartOTC demonstrated high technical performance, achieving a 92.73% success rate, a 0% crash rate, and a mean response time of 2.89 seconds. Significant improvements in task performance and response times were observed following implementation. However, these findings should be interpreted within the context of a pilot study with a limited sample size. The application demonstrated positive user experience and strong content validity, while usability outcomes suggested opportunities for further improvement. The findings are further limited by the single-site study setting and the absence of patient-level outcome evaluation. SmartOTC shows promise as a digital decision-support tool to support pharmacists in the management of common cold cases and warrants further evaluation in real-world pharmacy practice.
Explainable AI for Mental Health and Biomedical Decision Systems: A Comprehensive Review Pramesti Dewi; Purwono Purwono; Annastasya Nabila Elsa Wulandari; Indah Trivilia
Journal of Advanced Health Informatics Research Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v4i2.376

Abstract

The application of Artificial Intelligence (AI) in mental health is experiencing rapid development, while algorithmic transparency and clinical translation readiness still face fundamental obstacles. This review synthesizes empirical findings related to the use of Explainable Artificial Intelligence (XAI) in mental health and biomedical decision systems, focusing on three evaluative aspects, namely explainability architecture, validation strength, and clinical integration. The literature search followed the PRISMA 2020 guidelines across five major databases for publications from 2020 to 2026 and yielded nine studies that met the inclusion criteria. The synthesis results show the dominance of post-hoc approaches, particularly SHAP, which are commonly applied to ensemble and boosting models, while intrinsic models and counterfactual approaches are still rarely used. The majority of studies rely on internal validation, while independent external validation and prospective application in real clinical workflows are relatively limited. User-based evaluation of explainability has also been understudied, with algorithmic transparency more often understood as technical feature attribution rather than as a verified mechanism in clinical decision-making. These findings indicate a persistent gap between methodological advances and the level of clinical translation maturity. Explainability has not been systematically integrated with robust validation designs or user-oriented evaluations. This review proposes a translation evaluation framework that combines technical and clinical dimensions to assess the readiness for XAI implementation more comprehensively. The development of XAI in mental health requires evaluation standardization, strengthened external validation, and prospective testing focused on clinical impact and user trust.

Page 5 of 5 | Total Record : 42

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