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How can Machine Learning help remote patient care – A Case in Point
How can Machine Learning help remote patient care – A Case in Point

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The global healthcare sector has been in the center of a flux, with revolutionary changes in patient care amid a dynamic landscape. Advances in AI/Machine Learning, enabled by the huge amount of data collection, are helping further these changes faster than ever before.

The recent onset of doctors and patients monitoring current health conditions with instant feedback has resulted in patients becoming the informed consumers. These new-age healthcare consumers give more importance to one crucial thing above all — convenience. With this changed patient behavior and care delivery modes, remote monitoring is an important enabler for healthcare providers to monitor their patients quickly, effectively, remotely and seamlessly.

The Evolution of Remote Patient Monitoring

The journey of remote patient monitoring started in the 1900s with a medical consultation on radio. In its early days, it was limited to temperature, blood monitoring, and transmission of X-ray images. With the dawn of the internet in the 1990s, remote patient monitoring skyrocketed, with the use of mobile apps landing in patients’ hands during the 2010s. Moving ahead, IoT, 5G, AI/ML and edge computing are set to revolutionize the remote patient monitoring industry, with patients receiving quality healthcare from the comfort of their homes.

As an example, Medicare systems are increasingly using remote patient monitoring to bring care management into the patient’s home. From reducing the total cost of care to improving patient experience, and managing symptom analysis, Medicare systems are getting innovative and advanced with how they manage critical patients.

How does Machine Learning (ML) in this scenario? ML is the application of computer algorithms that can learn patterns in the data and draw inferences, as well as make predictions. This allows real-time monitoring and precautionary doctor-patient interactions, enabling a more proactive-preventive approach, saving time, improving outcomes, and lowering the expense.

While this may seem like a great idea theoretically, real-world implementation is not straightforward, and can often involve expertise across various technologies. Here’s a great use case of ML to illustrate how this can play out in the real-world.

Boosting Remote Patient Monitoring for Cancer Patients

The biggest challenge for cancer patients undergoing chemotherapy is the need for frequent urine tests. These urine samples are then delivered to experts for analysis in labs which results in delays and may affect clinical outcomes. Instead, Machine Learning (ML) can now deliver these reports much faster and without much manual effort.

This is what we are doing for one of our clients. The ML-based remote monitoring would simply require the patient to click a photograph of the test and leave the rest to ML algorithms to work their magic. The urine analyzer (mobile app) will then evaluate the critical diagnostic parameters and provide instant reports, thereby reducing the overall diagnosis time for the patient.

 

Here are the key benefits of using ML for remote patient monitoring:

  • Hassle-free processes and instant reports, without manual effort
  • Immediate data availability to doctors for prompt diagnosis, minimizing delays in treatment

Our application offers several other benefits, such as –

  • Seamless UX for non-tech-savvy people
  • Data access controls and secured data storage
  • Data available at a single click

With ML delivering accurate detections and helping patients deal with critical conditions, this product can be scaled up to allow more frequent testing, thereby creating an enriched database for future data analytics and predictive use.

Unlocking ML in Healthcare

Not only is ML a force to reckon with, but it is also the key to unlocking infinite possibilities in healthcare. The changes in this fast-paced sector demand swift delivery, fast adaptions, and faultless implementations. ML can make this possible, improving operational efficiency, reducing the turnaround time for prognosis and diagnosis, and ensuring that the reports, notes and test results are available at doctors’ and patients’ fingertips.

Unlock the future of healthcare with machine-learning algorithms and the technology of tomorrow. All you need is a trusted healthcare solution implementation partner who can wave the magic of ML and help introduce your business and end-consumers to infinite possibilities with ML.

 

Author:

Mandar Gadre, Director of Engineering,

Healthcare & Manufacturing | GS Lab


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GS Lab | GAVS is a global AI-led digital transformation company focused on creating business impact for its 200+ customers across the USA, Europe, APAC, and the Middle East. It offers digital product engineering, AI-led managed services, and digital transformation services to customers across Healthcare, BFSI, and Hi-tech segments. With 4000+ technologists spread across 10+ global delivery centers and a robust talent-nurturing culture, it is a trusted growth partner to its customers. Known for its innovative win-win business models, customer success focus, and deep tech engineering skills, the company invests heavily in emerging technologies such as 5G, edge computing, AI/ML, cloud, and IoT. Its IPs, such as ZIF, zDesk, Rhodium, and zIrrus help accelerate technology adoption for enterprises.

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