The Predictive Turn: Next-Generation Sensor Ecosystems for Chronic Disease
DOI:
https://doi.org/10.63001/tbs.2026.v21.i02.S.I(2).pp566-571Keywords:
Chronic Disease Management, IoT Healthcare,, Wearable Sensors, Proactive Healthcare, Data Analytics,, Smart Healthcare Systems, The Predictive Turn, Proactive Healthcare,, Next-Generation Sensors, P4 Medicine,, Internet of Medical Things (IoMT),, Sensor Ecosystems, Body Area Networks (BAN),, Edge Computing, Ubiquitous Sensing, Predictive Analytics,, Digital Twins, Biomedical Signal Processing, Anomaly Detection,, Clinical Application, Chronic Disease Management (CDM),, Remote Patient Monitoring (RPM),, Continuous Monitoring, Precision MedicineAbstract
The increasing prevalence of chronic diseases has placed immense pressure on global healthcare
systems, necessitating a transition from reactive treatment models to proactive, preventive care
strategies. Sensor-based healthcare technologies, supported by advancements in Internet of Things (IoT),
cloud computing, and artificial intelligence, have enabled continuous patient monitoring and real-time
data-driven decision-making. This paper presents a comprehensive study of sensor-based chronic disease
management systems, focusing on their architecture, operational mechanisms, and clinical impact. A
detailed data analysis comparing traditional and sensor-enabled healthcare approaches is conducted to
evaluate improvements in patient outcomes, healthcare efficiency, and cost reduction. The findings
indicate that proactive monitoring significantly enhances early diagnosis, reduces emergency incidents,
and improves treatment adherence. The study concludes with a discussion on challenges, future
directions, and the transformative potential of intelligent healthcare ecosystems.
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