Medical Internet of Things and Big Data in Healthcare

By D. Dimitrov. Healthcare Informatics Research, 2016

803 citations recorded by the source corpus.

Objectives A number of technologies can reduce overall costs for the prevention or management of chronic illnesses. These include devices that constantly monitor health indicators, devices that auto-administer therapies, or devices that track real-time health data when a patient self-administers a therapy. Because they have increased access to high-speed Internet and smartphones, many patients have started to use mobile applications (apps) to manage various health needs. These devices and mobile apps are now increasingly used and integrated with telemedicine and telehealth via the medical Internet of Things (mIoT). This paper reviews mIoT and big data in healthcare fields. Methods mIoT is a critical piece of the digital transformation of healthcare, as it allows new business models to emerge and enables changes in work processes, productivity improvements, cost containment and enhanced customer experiences. Results Wearables and mobile apps today support fitness, health education, symptom tracking, and collaborative disease management and care coordination. All those platform analytics can raise the relevancy of data interpretations, reducing the amount of time that end users spend piecing together data outputs. Insights gained from big data analysis will drive the digital disruption of the healthcare world, business processes and real-time decision-making. Conclusions A new category of "personalised preventative health coaches" (Digital Health Advisors) will emerge. These workers will possess the skills and the ability to interpret and understand health and well-being data. They will help their clients avoid chronic and diet-related illness, improve cognitive function, achieve improved mental health and achieve improved lifestyles overall. As the global population ages, such roles will become increasingly important.

Subfields: Applied Psychology, General Health Professions, Health Information Management

View Paper: https://www.semanticscholar.org/paper/9cacbc50b5a94e28b9de139c6c3abd1f00bd632f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Psychology

  1. Stephanie B. Baker
  2. Wei Xiang
  3. Dawn Beverley Branley
  4. Lynne M. Coventry
  5. Ian M. Atkinson
  6. Anil Pandit
  7. Nishita Mehta

In General Health Professions

  1. Stephanie B. Baker
  2. Wei Xiang
  3. Dawn Beverley Branley
  4. Lynne M. Coventry
  5. Ian M. Atkinson
  6. Anil Pandit
  7. Nishita Mehta

In Health Information Management

  1. Athanasios V. Vasilakos
  2. Stephanie B. Baker
  3. Wei Xiang
  4. Dawn Beverley Branley
  5. Lynne M. Coventry
  6. Ian M. Atkinson
  7. Anil Pandit
  8. Nishita Mehta

Citations in the corpus