Title: State-of-the-Art Integration of Mobile and Wearable Sensors for Data-Driven Enterprise Operations in the Health Sector
Authors: Emmanuel Yusuf Attah Sheidu Audu Yakubu
Volume: 9
Issue: 5
Pages: 84-95
Publication Date: 2025/05/28
Abstract:
The integration of mobile and wearable sensors into healthcare enterprise operations represents a transformative shift toward data-driven, proactive care delivery. This empirical investigation evaluates the clinical, operational, and technological implications of deploying sensor technologies across three large U.S. healthcare systems, encompassing 1,532 patients with chronic conditions. Utilizing a mixed-methods approach, the study combined continuous physiological data from medical-grade and consumer devices-including the Apple Watch Series 7, Dexcom G6 CGM, and Zio XT patch-with electronic health records (EHRs) and qualitative insights from 300 clinician and patient interviews. Results demonstrated a 23.7% reduction in 30-day hospital readmissions (p < 0.001), a 41.2% decrease in diabetes-related emergency visits (p = 0.002), and a median 6.2-day acceleration in arrhythmia detection compared to standard care. Machine learning models achieved an AUC-ROC of 0.91 for hypoglycemia prediction, enabling median 6.4-hour early warnings. Operational efficiencies included a 76.6% reduction in time-to-intervention (p < 0.001) and 19.4% fewer redundant diagnostic tests (p = 0.003). However, challenges emerged in interoperability, with 21.6% of sensor alerts deemed non-actionable, and disparities in adoption, as uninsured patients showed 23.4% lower adherence (p = 0.02). Qualitative findings revealed clinician alert fatigue (67%) and patient privacy concerns (34%), underscoring the need for adaptive workflows and equitable implementation strategies. The study validates the potential of sensor-EHR integration to enhance value-based care but highlights critical barriers in data governance, cybersecurity, and health equity. As healthcare enterprises navigate digital transformation, these findings advocate for tiered sensor ecosystems, standardized interoperability protocols, and AI-driven prioritization frameworks to balance clinical utility with operational sustainability.