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
- Stephanie B. Baker
- Wei Xiang
- Dawn Beverley Branley
- Lynne M. Coventry
- Ian M. Atkinson
- Anil Pandit
- Nishita Mehta
In General Health Professions
- Stephanie B. Baker
- Wei Xiang
- Dawn Beverley Branley
- Lynne M. Coventry
- Ian M. Atkinson
- Anil Pandit
- Nishita Mehta
In Health Information Management
- Athanasios V. Vasilakos
- Stephanie B. Baker
- Wei Xiang
- Dawn Beverley Branley
- Lynne M. Coventry
- Ian M. Atkinson
- Anil Pandit
- Nishita Mehta
Citations in the corpus
- Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research (2019, background): “For instance, people can use IoT devices to remind them about appointments, changes in blood pressure, calories burnt and much more [142,143].”
- The Future of Healthcare Internet of Things: A Survey of Emerging Technologies (2020, background): “Authors in [167], [168] and [169] have identified the challenges of implementing big data analytics in H-IoT and IoT in general.”
- Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities (2017, background): “Cloud storage capable of storing high volumes of varying data was also shown to be essential to a big data healthcare system by several previous works [9], [11], [24].”
- Concurrence of big data analytics and healthcare: A systematic review (2018, background): “), audio transcripts, handwritten prescriptions and structured data from EMRs [45] − and its dynamicity and complexity makes it difficult to analyze them.”
- Internet of Things (IoT): A Review of Its Enabling Technologies in Healthcare Applications, Standards Protocols, Security, and Market Opportunities (2021, background): “A focus on how a large database could be used for data analysis and trend determination is further considered in [56] and [57] The cloud management structure for e-health, which creates an efficient, secure, seamless model in provisioning, processing, protecting, enforcing policies, and prediction…”
- Cybersecurity in healthcare: A narrative review of trends, threats and ways forward. (2018, background): “237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 6 European regulators have published high-level cybersecurity recomm”