Smart Healthcare IoT Applications Using AI

Globally, healthcare professionals strive to diagnose, monitor, and save human lives. An application that advances the medical field to the next level is the need of the hour. Smart healthcare systems using IoT help in the process of monitoring human health by minimizing human intervention. Taking care and monitoring of human health has a significant contribution in declining the mortality rate as well. IoT in healthcare has aided smarter communications and prompt treatment to save lives. Patient data are sensed by sensors/microcontrollers, sent over the internet, stored in the cloud, and received by healthcare professionals during emergencies. Applications of such smart healthcare using IoT are blood glucose meters, medical vehicles, sphygmomanometer, pulse oximeter, Holter monitor, etc. This chapter elucidates several smart healthcare IoT applications using artificial intelligence and cloud computing technology. The chapter also elaborates the importance and functions of various cloud and AI components in designing a smart healthcare application.

AI-Based IoT Analytics on the Cloud for Diabetic Data Management System

It is very evident by looking at the current technological advancements that the interrelation and association of artificial intelligence (AI) and IoT in the Cloud have transformed the way healthcare has been working. AI and Cloud-empowered IoT boosts operational efficiency enhanced risk management. This combination creates products and services by enhancing the existing products while increasing scalability. To reduce costs, data analytics on the Cloud is much preferred in the current formation of technologies. This chapter focuses on the integration of different AI techniques in Cloud datasets for IoT data analytics. Analyzing, predicting, and making decisions by comparing the current data with historical data. The theory of AI-based IoT analytics will be much investigated with a healthcare application. Different approaches to implementing data analytics on the Cloud for a diabetic management system will be explored (human body). Finally, future trends and possible areas of research are also discussed.

The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application

South Tangerang Health Office had the responsibility in giving outstanding healthcare services to its resident’s despite of its limitation due Covid-19 pandemic. Some programs were initiated to reduce maternal, babies and toddler mortality, and in the same time reduce the number of malnourished children. The integrated healthcare mobile application called Si Pandai Kemas TangSel had been launched and can be downloaded easily through smartphone. The study is expected to measure the effectiveness of Si Pandai Kemas TangSel using UTAUT approach by measuring the influence of performance expectancy, effort expectancy, facilitating condition and habit toward behavior intention in using Si Pandai Kemas TangSel mobile application. The study showed that the effort expectancy and habit can increase the intention to use Si Pandai Kemas TangSel application. Meanwhile, performance expectancy and facilitating conditions did not affect behavioral intention in using Si Pandai Kemas TangSel application.

The Effectiveness of a Healthcare Application and Human Coaching Program in Primary Care Clinics: A Non-randomized Controlled Trial (Preprint)

BACKGROUND As the global burden of chronic conditions increases, effective management for these are a concern. There is an increasing need for chronic condition management using mobile self-management healthcare applications. OBJECTIVE This study evaluated the effectiveness of a mobile self-management healthcare application combined with human coaching for primary care services in patients with chronic conditions. METHODS A total of 110 patients with hypertension, diabetes, dyslipidemia, and/or metabolic syndrome who visited one of 17 participating primary care clinics from September 2020 to November 2020 were included in this study. Data regarding changes in body weight, sleep conditions, quality of life, depression, anxiety, stress, body mass index, waist circumference, blood sugar levels, blood pressure, and blood lipids levels were recorded. The intervention group (N=65) used a mobile self-management healthcare application with human coaching for 12 weeks, and the control group (N=45) underwent conventional, self-managed health care. RESULTS Patients in the intervention group reported significantly more weight loss than those in the control group (P=.002). The weight loss was markedly greater after using application for nine weeks than using it for four weeks or five to eight weeks (P=.002). Patients in the intervention group reported better sleep quality (P=.04) and duration (P=.004) than those in the control group. CONCLUSIONS The combination of a mobile self-management healthcare application and human coaching in primary care clinics results in better management of chronic conditions. The observed weight loss was greater and sleep quality improved than conventional primary care for patients with at least one chronic condition.