Healthcare Is About to Leap

Here’s how to leverage IoT in healthcare to promote a paradigm shift.

May 23, 2024

7 Min Read
Healthcare IoT
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At a Glance

  • There’s great potential for IoT in diagnostics, telemedicine, and advanced operational processes within smart hospitals.
  • Edge and cloud computing offer complementary benefits supporting real-time and trending functions of IoT.
  • AI, machine and deep learning, and neural networks could help advance diagnostics and more.

The healthcare industry is the “perfect patient” for IoT enhancements. The abundance of versatile data it generates every instant requires excessive processing and analytical power to produce valuable insights regarding health and business. Today, we are witnessing how health monitoring, pharmacy, hospital management, and diagnostics have started to rely heavily on IoT support. Its top-tier precision and time-saving nature make IoT critical for medicine. Meanwhile, ongoing cost reduction of electronic components enables bold experiments in the medical field. It seems like IoT has firmly entrenched itself in the healthcare industry and has the chance to drive its foreseeable future. 

Real-world IoT-based apps are far from being just all-round connectivity tools—they are a convergence of top technologies balanced for optimal industry deliverables. In fact, it’s impossible or meaningless to implement AI, machine learning, cloud computing, and other buzzword concepts circulating in healthcare without a robust IoT foundation. That’s why IoT is at the core of smart healthcare. 

IoT in Healthcare Today: What the Medical World Is Focused On

Speed of decision-making coupled with the precision of medical reports remains the primary direction for IoT in healthcaredevelopments. The latest research reveals a keen interest of the medical community in IoT-related developments, defining the following points of application under IoT supervision:

Primary

  • AI capabilities

  • Advanced connectivity (5G)

  • Data analysis

  • Computing Technologies

Secondary

  • Authentication Methods

  • Fog computing

  • Cloud integration technologies

A close look at this data reveals high hopes for IoT in diagnostics, telemedicine, and advanced operational processes within smart hospitals. Thus, we have every reason to believe that IoT is expected to turn from a medical assistant to a full-fledged decision-maker to address medical challenges. Moreover, excessive analytics allow healthcare to shift focus from the emergency ambulance and after-the-fact visits to predictive medical help, thus revolutionizing the healthcare paradigm.

However, healthcare authorities and institutes shouldn't be the only target for IoT in healthcare. IoT unleashes opportunities for patients’ self-care, and they are willing to utilize them. As per statistics, more than half of smartphone users have installed heath-tracking apps while 300M+ downloads of such apps are detected annually. More targeted and precise wearables together with more profound and versatile analytics about your health available on tap could ease the burden on medical facilities and improve overall recovery statistics.

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IoT Could Elevate Healthcare 

Balancing Computing Power Within IoT in Healthcare

The ever-growing demand for smart management of medical data and real-time clinical assistance has naturally resulted in intense and almost equal interest in both cloud and edge computing in medical society. 

The surge in edge computing positions AI to be deployed near the source of data generation and thereby deliver precious analytical insights in real-time. For healthcare, it has already unlocked body position and movement monitoring with deviation alarm, rapid screening, AI-supported surgery tools for minimized invasion, smart video stream analysis during surgery, DNA sequencing in real-time, and many more applications. The minimized latency makes real-time edge apps much more reliable within hospital settings where substantial data needs to be processed simultaneously for multiple distant units. Thus, by focusing on capacious hardware with multiple connectivity opportunities, you lay the foundations for integrated smart healthcare systems.

Cloud is extensively adopted as a platform for end-to-end management of the varied and sensitive data daily produced by any medical facility. Thanks to cloud’s potential flexibility and scalability, it helps alleviate the challenges of the increasing complexity of IoT in healthcare systems caused by incremental edge adoption as well. Cloud is ideal for storing and processing large operational, financial, patient, and drug data providing ongoing cost estimation, schedule, and inventory management with data backups and convenient access from any site. Those who offer more-effective data management and precise and variable analytics within seamless data integration opportunities are ensured success.

Today, when strategic planning is highly prioritized, healthcare reveals a tangible demand for integrated systems with balanced computing capabilities to address comprehensive industry challenges. For instance, smart health monitoring systems can leverage more edge capacities to automate drug delivery or bed adjustment during health deviations, while cloud collects data in medical history and analyzes it after to perform health forecasts. The following trends regarding Cloud-Edge tandem are at the forefront now:

  • Edge + Cloud hybrid for maximized efficiency with distributing workloads and coherence between plenty of distributed assets. Such a trend positions the cloud as an overall management center for increased transparency and naturally improves controllability in case of an asset failure. 

  • 5G for maximized throughput within time-critical applications for heavy data transfer. 5G allows for connection of up to 100 more devices than 4G or LTE while decreasing energy consumption. The enhanced connectivity also enables cloud management of MEC (multi-access edge computing) applications.

  • Strengthened authentication measures, such as network segmentation, full-duplex authentication integration, and regular updates, are vital to protect sensitive data. As edge infrastructures are more secure by default, it’s better to avoid the transition of redundant data in the cloud.

A thought-out approach to balancing computing capacities allows healthcare organizations and authorities to develop optimized strategies for maintaining data confidentiality, failure resistance, and highest efficiency and to create new models for patient care.

Neural Networks Adoption for Diagnostics Assistance

When it comes to diagnostics where the quality of the results is prioritized, the duo of IoT and AI produces the most impressive results. One example is how it manages visual analysis. For instance, proven deep learning methods provide chest disease identification at an accuracy of 98% by analyzing X-rays. The same turns to CT scans that can be successfully analyzed for the presence of brain tumors. By the way, recent research found out that a neural network can identify melanoma 10% more accurately than a medical expert. Thus, deep learning models effectively interpret medical shots by combining multiple aspects of visualization, such as the size, volume, and shape of the tissue.

Likewise, neural networks analyze multiple types of data and identify, for instance, risk for heart attacks or voice pathology. The scope of the application is only expanding. Their true potential lies in ever more complicated analytical applications to consider more factors for current state and risk estimation, profound diagnostics, and error identification in previous diagnoses. Symptoms, complaints, medical history, and current tests combined for IoT analysis can deliver groundbreaking opportunities for autonomous diagnostics. Accurate diagnostics and forecasts can potentially help the US healthcare system save up to $450 billion annually spent on unnecessary medical care, interventions, and medical mistakes.

One more word about neural networks at the edge: Today, we can start moving from dream to life about complex instant diagnostics in real-time and advanced detection of potentially dangerous states of health. Strikingly, neural networks achieved such impressive heights that allow us to save on IoT tools responsible for data collection.

IoT in Healthcare Goes Beyond Traditional Monitoring of Vital Signs

First, IoT-based systems reveal opportunities for augmented patient monitoring to identify any tiny detail critical to detecting dangerous states. The combination of real-time vital sign monitoring, environmental data collection, and patient health history within one analytical tool allows for instant and accurate detection of any suspicious state for a particular patient as well as prescribing personalized medical treatment.

Second, IoT in healthcare promotes total transparency and real-time control, which allows for remote health monitoring both for hospital and in-home supervision. IoT-based tools possess comprehensive data and excessive analytical capabilities, which help eliminate unnecessary visits to health facilities. At the same time, they can promote medical visits in advance before the disease turns into an acute form. All the interested stakeholders (physicians, insurance agents) can be provided with access to such data. Generally, IoT allows turning the healthcare paradigm from ambulance-focused help to predictive treatment, thus significantly unloading medical facilities. 

Promoting IoT in Healthcare Today: Summing Up

Healthcare is mastering IoT very rapidly and striving to make the most out of the technology. That’s why PSA focuses on IoT in healthcare first, providing robust integration with cutting-edge technologies to uncover opportunities we never experienced before. 

To make the IoT journey as fruitful as possible, take note of the following: 

  • Think strategically. Coherent full-fledged systems for comprehensive manageability of distributed units is where IoT in healthcare is headed. Thus, any development should be performed under interoperability and versatile connectivity.

  • Focus on quality and accuracy. Next-gen analytics embraces data from various sources utilizing the latest achievements in neural network development to provide a valuable tool for monitoring, diagnostics, and health forecasts.

  • Think out the edge infrastructure to enable various medical scenarios in real-time to precisely meet the target set. 

  • Promote accessibility and transparency for remote patient care and treatment by developing cloud infrastructures. Real-time access and ongoing monitoring are the basis for healthcare to leap to the new reality. 

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