Data analytics in healthcare is an exponentially growing field. Today, it is estimated that applying big data analytics on a system-wide basis could reduce healthcare spending in the US alone by a whopping $300-450 billion annually[1]. That’s because data analytics in healthcare can improve the whole field on several different levels. In this article, we are going to examine how data analytics works in healthcare and how it’s used to streamline the work of doctors and researchers.

When we talk about data analytics in healthcare, there are several different areas to examine. However, today, we want to focus strictly on three crucial ones:

  • Diagnostics
  • Robot-assisted surgery
  • R&D

Let’s start with the first point:

How data science improves diagnostics

When it comes to diagnostics, we have to mention identifying cancer cells. Standard, radiological methods are not sufficient today. They simply take too much time, and sometimes they are not effective enough in terms of accuracy. That’s why research centers all over the world have been working for some time now on the improved method that involves AI-based technologies and data analytics.

data analytics in healthcare

In early 2020, Nature magazine[2] mentioned an interesting project called FocalNet. This AI-fueled system has been developed at the University of California to help physicians better classify prostate cancer cases. The authors of this solution used 400 MRI scans of people who qualified to have surgery to remove their prostate. Based on that input (along with the tumor Gleason score), the system learned how to spot patterns in the MRI scans, indicating a risk of developing prostate cancer. FocalNet managed to find over 79.2% of cancer lesions and, this way, matched the accuracy of human radiologists.

It may be interesting for you: 7 ways Data Science is Revolutionizing Healthcare

Robot-assisted surgery

Robots are becoming a significant part of modern operating rooms. These devices vitally help surgeons do their job and assist in the most complex surgical procedures. The benefits are apparent–machines are never tired, they never go on a vacation or sick leave, and can easily operate 24/7, all year round. With the lack of workforce in the global healthcare sector, they are more than useful during surgery.
There are some vital benefits for the patient as well. According to Mayo Clinic[3], robot-assisted surgery offers:

  • Fewer complications, such as surgical site infection
  • Less pain and blood loss
  • Quicker recovery
  • Smaller, less noticeable scars

The same clinic informs that currently, the most extensively used robotic surgical system comprises a camera arm and several mechanical arms with surgical instruments. This system gives a high-definition, clear 3D view of the site. The surgeon operates the machine while other team members help them during the procedure. The perfect example of such a solution is Da Vinci Robotic Surgery Program:

Research and development

Concerning data analytics in healthcare, you have to understand that big data in healthcare consists of billions of entries about patients, treatments, drugs, surgical procedures, research results, and many more. That’s why R&D in healthcare is so complex and challenging. Analyzing all that information manually is simply impossible. That’s why we use data analytics techniques in order to streamline R&D processes in healthcare.

Nowadays, data analytics is supporting work on new drugs and clinical trials thanks to the ability to analyze all data instead of the selection of the test samples. With AI-powered technologies, you can easily identify specific patients with wanted biological characteristics who will participate in specialized clinical trials. As a result, healthcare companies can come up with new drugs and treatments faster and more effectively.

And it goes further! You see, data analytics in healthcare can even help in uncovering unknown disease correlations and hidden patterns in treatment results. By examining large data sets, analytics algorithms can find new cures for specific diseases or prescribe the best treatment tailored to a particular patient’s needs.

Read more about Computer Vision in Healthcare

Exscentia – pharma tech startup

Again, let’s use an example. Exscentia is a British pharma tech startup. They are currently working on an AI-fueled drug discovery platform known as Centaur Chemist. Centaur relies on machine learning methods to search for potential drugs in the same manner as pharmaceutical chemists. Of course, this solution is so much faster than human chemists!
If you are interested in Excentia’s projects, take a look at this video:

Data analytics truly transforms the modern healthcare industry, and that’s a good thing! If you want to find out how data analytics can be applied in your company, don’t hesitate to contact us! Addepto provides comprehensive data analytics services for companies representing almost all sectors and industries. We are at your service!


[1] Arkenea.com. The Ultimate List of Healthcare IT Statistics For 2020. URL: https://arkenea.com/healthcare-statistics/. Accessed July 20, 2021
[2] Nature.com. How AI is improving cancer diagnostics. URL: https://www.nature.com/articles/d41586-020-00847-2. Accessed July 20, 2021
[3] MayoClinic.org. Robotic Surgery. URL: https://www.mayoclinic.org/tests-procedures/robotic-surgery/about/pac-20394974. Accessed July 20, 2021

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