A SYSTEMIC AI AND CYBER-PHYSICAL FRAMEWORK FOR REAL TIME REMOTE PATIENT MONITORING IN INDONESIAN RURAL HEALTH CLINICS (PUSKESMAS)
Article Sidebar
-
AI, Cyber-Physical Systems, Healthcare
Abstract
Access to healthcare in rural Indonesia remains a significant challenge due to limited medical resources and healthcare personnel, leading to delayed diagnosis and suboptimal patient care. Remote patient monitoring offers a potential solution by enabling real-time health assessments and reducing the need for long-distance travel to healthcare facilities. This study aims to design and implement a systemic Artificial Intelligence and Cyber-Physical Systems framework for real-time remote patient monitoring in rural primary health clinics in Indonesia to enhance patient care, support early disease detection, and optimize healthcare resource allocation. The research employed a hybrid AI–CPS approach that integrated wearable health devices, Internet of Things sensors, and cloud computing infrastructure to continuously monitor patient vital signs. Artificial Intelligence algorithms were utilized to analyze health data and identify early signs of potential health anomalies. Data were collected from multiple rural Puskesmas where remote monitoring devices were installed, and system performance was evaluated using metrics including data accuracy, response time, and user satisfaction. The results indicated that the system achieved a high level of accuracy, with a 92 percent success rate in predicting potential health anomalies, while feedback from healthcare workers and patients demonstrated positive perceptions, particularly in terms of convenience, efficiency, and time savings. Overall, the findings confirm that the AI and Cyber-Physical Systems-based remote patient monitoring framework is effective in improving healthcare delivery in rural Indonesian clinics and holds strong potential as a scalable solution to enhance accessibility and quality of rural healthcare services.
Full text article
References
Acharya, S., Khan, A. A., & Päivärinta, T. (2024). Interoperability levels and challenges of digital twins in cyber–physical systems. Journal of Industrial Information Integration, 42, 100714. https://doi.org/10.1016/j.jii.2024.100714
Aghazadeh Ardebili, A., Boscolo, M., Longo, A., Pourmadadkar, M., Ficarella, A., & Padoano, E. (2025). Resilience in Cyber-Physical Infrastructures: R-KPI prioritization, framework development, and case study insights. Journal of Safety Science and Resilience, 6(3), 100194. https://doi.org/10.1016/j.jnlssr.2024.12.005
Ahmadasas, M., Inanc, E., Ozkara, E., Siket, M., Rashid, M. M., Bilgic, M., Quinn, L., & Cinar, A. (2025). Cyber–physical–human systems in precision medicine: Advances in artificial pancreas for treatment of diabetes. Annual Reviews in Control, 60, 101033. https://doi.org/10.1016/j.arcontrol.2025.101033
Alabdulatif, A., Thilakarathne, N. N., & Lawal, Z. K. (2024). A Review on Security and Privacy Issues Pertaining to Cyber-Physical Systems in the Industry 5.0 Era. Computers, Materials and Continua, 80(3), 3917–3943. https://doi.org/10.32604/cmc.2024.054150
Al-Mhiqani, M. N., Alsboui, T., Al-Shehari, T., Abdulkareem, K. hameed, Ahmad, R., & Mohammed, M. A. (2024). Insider threat detection in cyber-physical systems: A systematic literature review. Computers and Electrical Engineering, 119, 109489. https://doi.org/10.1016/j.compeleceng.2024.109489
Alotaibi, S. R., Alkahtani, H. K., Aljebreen, M., Alshuhail, A., Saeed, M. K., Ebad, S. A., Almukadi, W. S., & Alotaibi, M. (2025). Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems. Alexandria Engineering Journal, 110, 490–505. https://doi.org/10.1016/j.aej.2024.09.115
Amadiaz, Y., Alfonso-Lizarazo, E., & Nait Sidi Moh, A. (2025). A systematic review of healthcare cyber–physical systems with associated innovative technologies for Alzheimer’s and Parkinson’s Diseases. Array, 28, 100575. https://doi.org/10.1016/j.array.2025.100575
Ansari, J. A., Ishak, M. K., & Ammar, K. (2025). Combined Architecture of Destination Sequence Distance Vector (DSDV) Routing with Software Defined Networking (SDN) and Blockchain in Cyber-Physical Systems. Computers, Materials and Continua, 82(2), 2311–2330. https://doi.org/10.32604/cmc.2025.057848
Aslam, M. M., Shafik, W., Hidayatullah, A. F., Kalinaki, K., Gul, H., Zakari, R. Y., & Tufail, A. (2025). Intelligent Transportation Systems: A Critical Review of Integration of Cyber-physical Systems (CPS) and Industry 4.0. Digital Communications and Networks. https://doi.org/10.1016/j.dcan.2025.06.014
Canaan, B., Makhlouf, Y., & Ould Abdeslam, D. (2025). Cyber-physical security for microgrids through Digital Twin technologies: A review and research outlook. Energy Conversion and Management: X, 28, 101406. https://doi.org/10.1016/j.ecmx.2025.101406
Canonico, R., Lista, F., Navarro, A., Sperlí, G., & Vignali, A. (2025). Threat detection in reconfigurable Cyber–Physical Systems through Spatio-Temporal Anomaly Detection using graph attention network. Computers & Security, 156, 104509. https://doi.org/10.1016/j.cose.2025.104509
Cao, Y., Yang, A., Li, H., Zeng, Q., & Gao, J. (2024). A comprehensive knowledge map for AI improving security management of cyber-physical system enabled smart manufacturing. Computers & Security, 137, 103650. https://doi.org/10.1016/j.cose.2023.103650
Cheng, M., Chen, Y., & Zhang, D. (2025). A Traffic Normalization Location Attention Network for cyber attack detection in Industrial Cyber-Physical Systems. Ad Hoc Networks, 178, 103965. https://doi.org/10.1016/j.adhoc.2025.103965
Devliyal, S., Goyal, H. R., & Sharma, S. (2025). iCDRET: A Dynamic Cyber Risk Estimation Technique for Intelligent Cyber-physical Systems in PCS. The Open Bioinformatics Journal, 18. https://doi.org/10.2174/0118750362380269250502062754
G, V., Gopikrishnan, S., Boulila, W., Koubaa, A., & Srivastava, G. (2025). Digital Twins and Cyber-Physical Systems: A New Frontier in Computer Modeling. CMES - Computer Modeling in Engineering and Sciences, 143(1), 51–113. https://doi.org/10.32604/cmes.2025.057788
Gabrecht, M. T., Janneck, M., Hellbrück, H., & Matthies, D. J. C. (2024). The Cyber-Placebo: Intelligent CPHS with Fake-AI? 5th IFAC Workshop on Cyber-Physical Human Systems, 58(30), 326–331. https://doi.org/10.1016/j.ifacol.2025.01.202
Ge, L., Zheng, J., & Yu, W. (2025). Chapter 4—Machine learning in cyber-physical systems. In W. Yu (Ed.), Edge Intelligence in Cyber-Physical Systems (pp. 71–99). Academic Press. https://doi.org/10.1016/B978-0-44-326572-3.00011-5
Hapsari, T. A. R., Rosyada, D., & Bariyah, N. O. N. (2025). Strategies for Improving Literacy and Numeracy in Computer-Based National Assessment at Madrasah Ibtidaiyah. Scientechno: Journal of Science and Technology, 4(1), 1–16. https://doi.org/10.70177/scientechno.v4i1.1954
Hassan, A. H., Ahmed, E. M., Hussien, J. M., Sulaiman, R. bin, Abdulhak, M., & Kahtan, H. (2025). A cyber physical sustainable smart city framework toward society 5.0: Explainable AI for enhanced SDGs monitoring. Research in Globalization, 10, 100275. https://doi.org/10.1016/j.resglo.2025.100275
Hazmi, M., Jiwon, S., & Kingh, R. (2025). Utilization of the Microbiome to Increase Food Security Throught Sustainable Biotechnology. Scientechno: Journal of Science and Technology, 4(1), 32–39. https://doi.org/10.70177/scientechno.v4i1.2116
Hossain, M. M., Kashem, M. A., Nayan, N. M., & Chowdhury, M. A. (2024). A Medical Cyber-physical system for predicting maternal health in developing countries using machine learning. Healthcare Analytics, 5, 100285. https://doi.org/10.1016/j.health.2023.100285
Irianti, E., Anis, N., & Aziz, S. (2025). Pattern Recognition System for Automating Medical Diagnosis Based on Image Data. Scientechno: Journal of Science and Technology, 4(1), 40–47. https://doi.org/10.70177/scientechno.v4i1.2126
Kaloudi, N., & Li, J. (2025). The ML-based sensor data deception targeting cyber–physical systems: A review. Computer Science Review, 57, 100753. https://doi.org/10.1016/j.cosrev.2025.100753
Karakaya, A. (2025). A novel post-quantum lightweight security model and directed acyclic graph based blockchain integration for mobile cyber-physical systems. Computers and Electrical Engineering, 125, 110415. https://doi.org/10.1016/j.compeleceng.2025.110415
Karunkuzhali, D., Arunachalam, K. P., Ramamoorthi, R., & Kadu, R. K. (2025). Cyber-physical system for enhanced WSN-IoT security using spherical graph triple convolutional neural network with planet optimization algorithm. Progress in Engineering Science, 2(3), 100108. https://doi.org/10.1016/j.pes.2025.100108
Kazimierczak, M., Habib, N., Chan, J. H., & Thanapattheerakul, T. (2024). Impact of AI on the Cyber Kill Chain: A Systematic Review. Heliyon, 10(24), e40699. https://doi.org/10.1016/j.heliyon.2024.e40699
Lyu, D., Li, Y., Zhang, Z., Arcaini, P., Zhang, X.-Y., Ishikawa, F., & Zhao, J. (2025). Fault localization of AI-enabled cyber–physical systems by exploiting temporal neuron activation. Journal of Systems and Software, 229, 112475. https://doi.org/10.1016/j.jss.2025.112475
Mitchell, D., Baniqued, P. D. E., Kivrak, H., Blanche, J., Bremner, P. A., Harper, S., Jiang, Z., Pulgarin, E. J. L., Paterson, J., Carrasco, J., Groves, K., Giuliani, M., Herrmann, G., Lennox, B., Lim, T., Watson, S., & Flynn, D. (2025). Cyber-physical-human systems for mobile robots in energy asset management: Current practices and future opportunities. Energy and AI, 21, 100570. https://doi.org/10.1016/j.egyai.2025.100570
Mohammed, M. A., Lakhan, A., Zebari, D. A., Ghani, M. K. A., Marhoon, H. A., Abdulkareem, K. H., Nedoma, J., & Martinek, R. (2024). Securing healthcare data in industrial cyber-physical systems using combining deep learning and blockchain technology. Engineering Applications of Artificial intelligence, 129, 107612. https://doi.org/10.1016/j.engappai.2023.107612
Mourtzis, D., & Angelopoulos, J. (2024). 12—Artificial intelligence for human–cyber-physical production systems. In D. Mourtzis (Ed.), Manufacturing from Industry 4.0 to Industry 5.0 (pp. 343–378). Elsevier. https://doi.org/10.1016/B978-0-443-13924-6.00012-0
Munawar, A., Ali, M. N., Qasim, A., & Kim, B.-S. (2025). GWO-LightGBM: A Hybrid Grey Wolf Optimized Light Gradient Boosting Model for Cyber-Physical System Security. CMES - Computer Modeling in Engineering and Sciences, 145(1), 1189–1211. https://doi.org/10.32604/cmes.2025.071876
Nandanwar, H., & Katarya, R. (2025). Securing Industry 5.0: An explainable deep learning model for intrusion detection in cyber-physical systems. Computers and Electrical Engineering, 123, 110161. https://doi.org/10.1016/j.compeleceng.2025.110161
Piardi, L., de Oliveira, A. S., Costa, P., & Leitão, P. (2025). Collaborative fault tolerance for cyber–physical systems: The detection stage. Computers in Industry, 166, 104253. https://doi.org/10.1016/j.compind.2025.104253
Qureshi, A. H., Manzoor, B., & Ibrahim, A. (2025). 4—Blockchain and cyber-physical systems in the smart healthcare. In T. A. Nguyen (Ed.), Blockchain and Digital Twin for Smart Healthcare (pp. 69–81). Elsevier. https://doi.org/10.1016/B978-0-443-30300-5.00006-3
Rani, S., Kataria, A., Kumar, S., & Karar, V. (2025). A new generation cyber-physical system: A comprehensive review from security perspective. Computers & Security, 148, 104095. https://doi.org/10.1016/j.cose.2024.104095
Rathee, G., Saini, H., Garg, S., Choi, B. J., & Hassan, M. M. (2024). A Secure Data E-Governance for Healthcare Application in Cyber Physical Systems. International Journal on Semantic Web and Information Systems, 20(1). https://doi.org/10.4018/IJSWIS.345934
Rehman, S. ur, Alhulayyil, H., Alzahrani, T., AlSagri, H., Khalid, M. U., & Gruhn, V. (2025). Intrusion detection system framework for cyber-physical systems. Egyptian Informatics Journal, 30, 100600. https://doi.org/10.1016/j.eij.2024.100600
Rivadeneira, J. E., Borges, G. A., Rodrigues, A., Boavida, F., & Sá Silva, J. (2024). A unified privacy preserving model with AI at the edge for Human-in-the-Loop Cyber-Physical Systems. Internet of Things, 25, 101034. https://doi.org/10.1016/j.iot.2023.101034
Routray, K., Bera, P., & Mishra, S. (2025). Efficient and Secure Fine-Grained Authorization for Fog-Enabled Cyber-Physical Systems: Towards Trust and Autonomy in Digital Sovereignty. International Conference on Digital Sovereignty (ICDS), 254, 240–249. https://doi.org/10.1016/j.procs.2025.02.083
Saheed, Y. K., & Misra, S. (2025). CPS-IoT-PPDNN: A new explainable privacy preserving DNN for resilient anomaly detection in Cyber-Physical Systems-enabled IoT networks. Chaos, Solitons & Fractals, 191, 115939. https://doi.org/10.1016/j.chaos.2024.115939
Sappaile, B. I. (2024). The Impact of Gamification Learning on Student Motivation in Elementary School Learning. Scientechno: Journal of Science and Technology, 3(2), 1–13. https://doi.org/10.55849/scientechno.v3i2.1050
Sarkar, A., & Jhamb, M. (2025). A novel ultra-low power post quantum approach using artificial intelligence based key generation for cyber physical system in Internet of things. Sustainable Computing: Informatics and Systems, 48, 101242. https://doi.org/10.1016/j.suscom.2025.101242
Shabani, F., Esmaeilyfard, R., & Nikseresht, A. (2025). An innovative framework for driving behavior Analysis: Privacy-Preserving, explainable, and adaptive Artificial intelligence in cyber-physical systems. Engineering Applications of Artificial intelligence, 160, 111974. https://doi.org/10.1016/j.engappai.2025.111974
Authors
Copyright (c) 2025 Ethan Tan, Ava Lee, Li Wei, Rustiyana Rustiyana
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
