Indigenous and Disruptive Remote Patient Monitoring Devices - A Case Study on AI in Healthcare

Abstract

The evolution of Industry 4.0 technologies has facilitated the growth of technologically driven healthcare solutions, disrupting, and significantly challenging the way the sector works and moving towards Healthcare 4.0. The global interest in Artificial Intelligence (AI) in the healthcare sector is increasing tremendously in comparison to other sectors. A rapidly ageing population with increasing health complications has led to the rise of AI-driven Remote Patient Monitoring (RPM) devices, where a patient can be monitored in the comfort of a home, using the latest communication and sensor technologies. This study aims to understand the usage of Artificial Intelligence (AI) as a healthcare disruptor, capturing the ever-increasing demands concerning the remote patient monitoring industry, making huge improvements, and redefining the way how healthcare can be provided, for timely and cost-effective solutions. The analysis of these remote monitoring devices has been done through a case study approach. For this purpose, two AI-enabled remote patient monitoring devices Dozee.ai and Qure.ai, have been taken which have been assisting patients and doctors in the diagnosis of health vitals remotely. Data has been taken from secondary sources to analyze the concept of indigenous and disruptive innovations. Both the apps have been quite successful in their diagnosis of Covid positive patients and have assisted both patients and healthcare personnel during critical times. Despite the huge advantages of AI-enabled RPM devices, they are vulnerable to data hacking and privacy issues. Any errors in these devices can pose potential risks to patients’ health.