A SYSTEMIC AI AND CYBER-PHYSICAL FRAMEWORK FOR REAL TIME REMOTE PATIENT MONITORING IN INDONESIAN RURAL HEALTH CLINICS (PUSKESMAS)

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.