5 Examples of How Big Data has Revolutionized Healthcare

Big data – what does it mean for us? As healthcare has rapidly developed, it’s essential to understand how big data can enhace our health. Through the power of huge amounts of information, data analysis can better understand the world around us. Organizations like Clinical ink are paving the way for the future of healthcare by employing the latest data and technology, writes Angela Scott-Briggs of TechBullion.

Important Insights Gained from Big Data

Many people are using to social media for personal and business reasons. So it’s not astonishing that many are also starting to use social media data for public health goals. Some examples contain analyzing tweets about symptoms of illnesses and using Twitter updates to forecast disease outbreaks.

By integrating real-time data from the CDC with other relevant outside information, we can reasonably understand how diseases spread and indicate where they’re going next. This is important because it permits us to use limited resources better. For example: if there’s a flu eruption in one area but not another, we know where our time would be reasonably spent educating people on precluding instead of treatment after they have already taken ill.

The Influence of Big Data and Machine Learning

Machines can interpret data and identify patterns that doctors may not be able to notice or even know what to look for. Many of the issues that healthcare organizations face are based on holding so much data at hand – it’s challenging to make sense of it all.

Machine learning can be employed in diagnostics and treatment. For example, it can help doctors in diagnosing diseases such as cancer and heart disease and help decide which kind of treatment will perform best for a patient based on their medical history.

Google DeepMind has been conducting with clinicians at Royal Free Hospital in London for various years to create an app called Streams that enables nurses and doctors to furnish better care by sharing information faster between them when they need it most. This technology authorizes patients suffering from sepsis (an often fatal illness) or kidney failure to be regaled faster.

Improved Health Outcomes Through Genomic Sequencing

In addition to better experiencing the effectiveness of specific treatments, genetic testing has also opened up great avenues for research and clinical practice. In 2013, actress Angelina Jolie wrote an article about her determination to undergo a double mastectomy after DNA tests showed a high risk of developing breast cancer owing to inherited BRCA1 gene mutations.

While there is some controversy among scientists about the reality of certain BRCA1 tests and whether or not their importance is overstated, such testing gives people essential information about their health that can assist them make better decisions for themselves and their households.

How Can Big Data Benefit?

Big data can help health facilities in numerous ways. Here are just a few of them:

  • Healthcare institutions can use big data to enhance their efficiency and profitability.
  • Employers can utilize big data to create effective wellness programs and enhance employee health.
  • Researchers, scientists, and medical device manufacturers can efficiently utilize big data to develop pristine pharmaceuticals and medical devices to treat diseases.
  • Scientists, researchers, and medical device manufacturers can also employ big data to develop better treatments for diseases that are challenging to cure by other means, such as cancer or infectious diseases like HIV/AIDS or Ebola.
  • Big data analytics is employed extensively in today’s healthcare industry because it enables downsized costs while simultaneously improving the service quality.

Innovative Use of Existing Health Data to Develop New Treatments

Thanks to big data, new cures for diseases like cancer, diabetes, and Alzheimer’s are within reach.

By following in the footsteps of IBM’s “Jeopardy”-winning supercomputer, Watson, researchers use data mining and predictive analytics for rushing up the process by which drugs make it from the lab to patients. The goal is to cut down on trial-and-error drug development strategies that can take years or decades before they are ready for market. This expensive and time-consuming process could be lowered thanks to artificial intelligence.