Hospital Remote Care Assistance AI to Reduce Workload


  • Luís B. Elvas
  • Miguel Nunes
  • Joao C. Ferreira
  • Berit Irene Helgheim


In the ever-evolving healthcare landscape, the rapid advancement of Artificial Intelligence (AI) technology presents significant opportunities for enhancing patient care and streamlining operational workflows. This study adopts the action research methodology to explore the transformative impact of AI, specifically leveraging data fusion techniques, on remote chronic patient care. Our research yields promising results, achieving 94% accuracy in predicting heart rate abnormalities using Neural Networks and 87.6% for Facial Expression classification through Convolutional Neural Networks. A critical observation from the state-of-the-art indicates that existing studies predominantly focus on remote patient monitoring, leaving a substantial gap in addressing the needs of caregivers and medical staff. Our findings demonstrate that AI, integrated into remote home care monitoring systems, has the potential to alleviate caregiver workloads by enabling continuous patient monitoring and timely alerts. The automation of data extraction and analysis enhances healthcare professionals’ decision-making capabilities, ultimately improving patient outcomes while alleviating the burdens of manual data entry.


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How to Cite

Luís B. Elvas, Miguel Nunes, Joao C. Ferreira, & Berit Irene Helgheim. (2024). Hospital Remote Care Assistance AI to Reduce Workload . International Journal of Computer Information Systems and Industrial Management Applications, 16(2), 13. Retrieved from



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