Big data – what does it mean for you? As healthcare has rapidly evolved, it’s important to understand how big data can improve our health. Through the power of massive amounts of information, data analysis can give us a better understanding of the world around us. Companies like Clinical ink are paving the way for the future of healthcare by using the latest data and technology.
If this interests you, read on for some examples of how big data has revolutionized healthcare.
Critical Insights Gained from Big Data
Many people are turning to social media for all kinds of personal and business reasons. So it’s not surprising that many are also starting to use social media data for public health purposes. Some examples include analyzing tweets about symptoms of illnesses and using Twitter updates to predict disease outbreaks.
By combining real-time data from the CDC with other relevant outside information, we can get a better idea of how diseases spread and predict where they’re going next. This is important because it allows us to use limited resources better. For instance: if there’s a flu outbreak in one area but not another, we know where our time would be better spent educating people on prevention instead of treatment after they’ve already taken ill.
The Power of Big Data and Machine Learning
Machines can analyze data and identify patterns that doctors may not be able to see or even know what to look for. Many of the challenges that healthcare organizations face are based on having so much data at their fingertips – it’s challenging to make sense of it all.
Machine learning can be used in diagnostics and treatment. For example, it can assist doctors in diagnosing diseases such as cancer and heart disease and help determine which kind of treatment will work best for a patient based on their medical history.
Google DeepMind has been working with clinicians at Royal Free Hospital in London for several years now to create an app called Streams which enables nurses and doctors to provide better care by sharing information more quickly between them when they need it most. This kind of technology allows patients suffering from sepsis (an often fatal illness) or kidney failure to be treated faster than ever before.
Improved Health Outcomes Through Genomic Sequencing
In addition to better understanding the effectiveness of specific treatments, genetic testing has also opened up new avenues for research and clinical practice. In 2013, actress Angelina Jolie published an article about her decision to undergo a double mastectomy after DNA tests showed that she had a high risk of developing breast cancer due to inherited BRCA1 gene mutations.
While there is some debate among scientists about the validity of certain BRCA1 tests and whether or not their significance is overstated, such testing does give people crucial information about their health that can help them make better decisions both for themselves and their families.
How Can Big Data Help?
Big data can help health facilities in many ways. Here are just a few of them:
- Healthcare organizations can use big data to improve their efficiency and profitability.
- Employers can use big data to create effective wellness programs and improve employee health.
- Researchers, scientists, and medical device manufacturers can use big data to efficiently develop new pharmaceuticals and medical devices for treating diseases.
- Researchers, scientists, and medical device manufacturers can also use big data to develop more efficient treatments for diseases that are difficult to cure by other means, such as cancer or infectious diseases like HIV/AIDS or Ebola.
- Big data analytics is used extensively in the healthcare industry today because it helps reduce costs while improving the quality of service simultaneously.
Innovative Use of Existing Health Data to Develop New Treatments
New treatments for diseases like cancer, diabetes, and Alzheimer’s are within reach, and it’s all thanks to big data.
By following in the footsteps of IBM’s “Jeopardy”-winning supercomputer, Watson, researchers are using data mining and predictive analytics to speed 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 procedures that can take years or decades before they’re ready for market – a costly and time-consuming process that could be reduced thanks to artificial intelligence.