520
B. Farahani et al.
adaptive framework for healthcare. In our view, the most direct way to develop this
framework is to construct an IoT-based solution, in order to facilitate breakthroughs
in all areas mentioned above.
Advances in the IoT help create significant advances in healthcare. For instance,
technologies such as microfluidic biochips and wearable biosensors can improve
clinical diagnostics in a variety of applications, from the laboratory to the hospital.
In the foreseeable future, IoT-enabled devices will allow health providers to
routinely assess patients who suffer from breast, lung, and colorectal cancers and
perform point-of-care molecular testing as an aspect of standard care. This will
help provide physicians with the information they need to create truly data-driven
treatment plans, significantly improving the chance of a successful recovery.
When these tests are time-stamped, location-tagged, and also tagged with data
on the testing environment and other situational information, as well as personal
information such as age, weight, height, and gender, a data fabric will begin to take
shape, spotlighting not only the patient’s condition but also overall patterns in the
population as a whole (e.g„ helping predict an outbreak of an epidemic). In short,
IoT-enabled healthcare (e-Health) can move disease research forward, enable more
accurate diagnoses at the point of care, and speed up the development of beneficial
pharmaceuticals. It should be noted that IoT e-Health is not just the simple stack
of different worlds. Instead, these pieces are networked together in order to assess,
predict, and adapt in close to real time.
As the above vision is realized, the benefits of adopting IoT e-Health can be
summed up as follows (see Fig. 11.1) [1, 9]:
• All-encompassing – IoT e-Health has a holistic solution for everyone needs and
it is useful in diverse areas including beauty, health monitoring, exercise, and
patient safety.
• Resiliency – The e-Health framework can also be self-learning and resilient to
inaccuracies.
• Seamless fusion and integration – IoT e-Health overcomes technological barriers
to enable diverse or complex technologies to collaborate.
• Big Data processing and analytics – IoT e-Health can effectively process,
analyze, and manipulate the tsunami of multi-modal, multiscale, distributed, and
heterogeneous data created by network sensors within a reasonable timeframe,
resulting in the timely receipt of actionable information.
• Personalized service or content – Big Data analytics and IoT can widen the
possibilities to meet the needs of personalized healthcare or treatments, playing
a key role in personal well-being. For example, Big Data analytics and machine
learning can be utilized to predict health conditions such as heart attacks, cancer
development, or infections before they actually occur, enabling patients and
physicians to act quickly.
• Lifetime monitoring – Patients can benefit from the receipt of comprehensive,
past, present, and future health data.
B. Farahani et al.
adaptive framework for healthcare. In our view, the most direct way to develop this
framework is to construct an IoT-based solution, in order to facilitate breakthroughs
in all areas mentioned above.
Advances in the IoT help create significant advances in healthcare. For instance,
technologies such as microfluidic biochips and wearable biosensors can improve
clinical diagnostics in a variety of applications, from the laboratory to the hospital.
In the foreseeable future, IoT-enabled devices will allow health providers to
routinely assess patients who suffer from breast, lung, and colorectal cancers and
perform point-of-care molecular testing as an aspect of standard care. This will
help provide physicians with the information they need to create truly data-driven
treatment plans, significantly improving the chance of a successful recovery.
When these tests are time-stamped, location-tagged, and also tagged with data
on the testing environment and other situational information, as well as personal
information such as age, weight, height, and gender, a data fabric will begin to take
shape, spotlighting not only the patient’s condition but also overall patterns in the
population as a whole (e.g„ helping predict an outbreak of an epidemic). In short,
IoT-enabled healthcare (e-Health) can move disease research forward, enable more
accurate diagnoses at the point of care, and speed up the development of beneficial
pharmaceuticals. It should be noted that IoT e-Health is not just the simple stack
of different worlds. Instead, these pieces are networked together in order to assess,
predict, and adapt in close to real time.
As the above vision is realized, the benefits of adopting IoT e-Health can be
summed up as follows (see Fig. 11.1) [1, 9]:
• All-encompassing – IoT e-Health has a holistic solution for everyone needs and
it is useful in diverse areas including beauty, health monitoring, exercise, and
patient safety.
• Resiliency – The e-Health framework can also be self-learning and resilient to
inaccuracies.
• Seamless fusion and integration – IoT e-Health overcomes technological barriers
to enable diverse or complex technologies to collaborate.
• Big Data processing and analytics – IoT e-Health can effectively process,
analyze, and manipulate the tsunami of multi-modal, multiscale, distributed, and
heterogeneous data created by network sensors within a reasonable timeframe,
resulting in the timely receipt of actionable information.
• Personalized service or content – Big Data analytics and IoT can widen the
possibilities to meet the needs of personalized healthcare or treatments, playing
a key role in personal well-being. For example, Big Data analytics and machine
learning can be utilized to predict health conditions such as heart attacks, cancer
development, or infections before they actually occur, enabling patients and
physicians to act quickly.
• Lifetime monitoring – Patients can benefit from the receipt of comprehensive,
past, present, and future health data.