Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal HEALTH FRONTIERS (MULTIDISCIPLINARY JOURNAL FOR HEALTH PROFESSIONALS)
Kencana Efendi, Sekar Galih
Unknown Affiliation
Dentistry Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Decentralizing Healthcare Referrals: How Graph Neural Networks and Blockchain Can Bridge Gaps in LMICs for Equitable Care Budiono, Christiano Evan; Nul Arif, Hendri Lukman; Kencana Efendi, Sekar Galih; Ramadani, Nadia Ayu; Hendrawan, Nailyaa Faza; Mirachel Prakusya, Nabila Levi; Zannah, Qoridhatul; En Nabhan, M. Zuhair; Dwianti , Aigies Siska
Health Frontiers: Multidisciplinary Journal for Health Professionals Vol. 3 No. 1 (2025): Health Frontiers
Publisher : Tarqabin Nusantara Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62255/mjhp.v3i1.147

Abstract

Healthcare referral systems are pivotal for equitable care delivery, yet inefficiencies persist across low- and middle-income countries (LMICs), exacerbating disparities in access to specialist care. This mixed-methods study integrates longitudinal data from Portugal’s National Health Service (12M consultations, 2017–2022) and Indonesia’s National Health Insurance claims (1.7M beneficiaries) to evaluate referral mechanisms. Graph neural networks (GNNs) reveal power-law distributed referrals (?=2.3), where 20% of specialists handle 78% of cases, driven by professional affiliations (?=0.67, p<0.001) that disproportionately marginalize rural providers. Logistic regression identifies urban deprivation (OR=1.72, 95% CI: 1.45–2.04) and fragmented e-referral systems (OR=2.10, 95% CI: 1.88–2.35) as key compliance barriers, particularly among youth (18–44 years) with 54% higher odds of nonattendance. A PRISMA-guided systematic review of 63 studies highlights 10 systemic gaps, including inadequate patient tracking (58% of studies), provider workload (49%), and patient mistrust (37%). While e-referral integration reduces median care delays by 67% (21 vs. 7 days, p=0.003) and duplicate referrals by 41%, adoption challenges persist in 62% of Indonesian primary clinics due to fragmented IT infrastructure. Policy recommendations emphasize (1) decentralizing referral networks through GNN-driven analytics to prioritize underserved populations, (2) scaling interoperable e-referral platforms with blockchain-backed tracking, and (3) implementing community-led digital literacy programs to address youth and rural disparities. These reforms align with Sustainable Development Goal (SDG) 3.8, offering a roadmap to mitigate inequities and optimize referral efficiency in LMIC health systems.
From Sensors to Safety: IoT-Enabled Smart Helmets as a Game-Changer for Worker Protection in High-Risk Industries Budiono, Christiano Evan; Nul Arif, Hendri Lukman; Kencana Efendi, Sekar Galih; Ramadani, Nadia Ayu; Hendrawan, Nailyaa Faza; Mirachel Prakusya, Nabila Levi; Zannah, Qoridhatul; En Nabhan, M. Zuhair; Dwianti , Aigies Siska
Health Frontiers: Multidisciplinary Journal for Health Professionals Vol. 3 No. 1 (2025): Health Frontiers
Publisher : Tarqabin Nusantara Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62255/mjhp.v3i1.164

Abstract

The integration of wearable technology into workplace safety systems has emerged as a transformative solution for mitigating risks in hazardous environments. This study evaluates the effectiveness of IoT-enabled smart helmets equipped with real-time monitoring and early warning systems to enhance worker safety in industries such as mining, construction, and chemical processing. The smart helmet system integrates multiple sensors, including GPS modules for location tracking, gas detectors for environmental monitoring, temperature and humidity sensors for ambient condition assessment, and health monitoring sensors such as heart rate monitors and concussion detectors. Advanced edge AI algorithms are embedded to enable local data processing, ensuring low latency and rapid decision-making. The performance of the system was rigorously evaluated under controlled and simulated hazardous conditions, demonstrating high accuracy in location tracking (mean absolute error of 2.3 meters), gas detection (thresholds of 5 ppm for methane and 5,000 ppm for CO2), and health monitoring (97% accuracy for heart rate sensors). Battery efficiency was optimized through low-power hardware design and energy-saving strategies, achieving a continuous operational lifespan of up to 10 hours. Robust privacy and security measures, including AES-256 encryption and multi-factor authentication, ensured the protection of sensitive data. Despite these advancements, challenges such as scalability, adaptability to dynamic scenarios, and emerging cybersecurity threats remain areas for further exploration.
Co-Authors Budiono, Christiano Evan Dwianti , Aigies Siska En Nabhan, M. Zuhair Hendrawan, Nailyaa Faza Mirachel Prakusya, Nabila Levi Nul Arif, Hendri Lukman Ramadani, Nadia Ayu Zannah, Qoridhatul