Big Data Analytics in Healthcare Industry

You may be surprised to hear that big data analytics can be a good thing in the healthcare industry, but it turns out that it’s the best thing to happen to healthcare since penicillin was discovered in 1928.

The old ways of analyzing medical data are inefficient and prone to error, but with big data analytics, you can bring order to the chaos by uncovering information you never knew was there and turning it into actionable data you can use to do your job better.

Here’s how big data analytics works in the healthcare industry and why it matters so much more than most people realize.

What is big data in healthcare?


Big data is defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.

Large volumes of patient data, detailed clinical records, genomic sequences and other health information can all be considered big data resources when managed effectively. As such, big data can have significant implications for healthcare.

For example, enabling a better understanding of individuals’ genes, doctors might one day tailor patient care based on their particular genetic makeup—and perhaps avoid a misdiagnosis because more thorough genetic testing was performed earlier on.

On a larger scale, big data offers healthcare systems an opportunity to improve diagnoses and treatment plans for people suffering from similar medical conditions across multiple institutions.

How does the healthcare industry use big data?


The healthcare industry has some of its own unique challenges when it comes to data. The sheer amount of data produced by healthcare companies is staggering. In fact, one study showed that a top healthcare provider produced more than 500 terabytes (TB) of information per day! For reference, Google and Facebook together produce about 3 TB per day.

The challenge isn’t just getting all that data—it’s figuring out what to do with it all. And now big data analytics are helping to guide treatments and pinpoint efficiencies based on knowledge gained from analyses.

Healthcare is often a case study for some of our most complex issues, so developing a better understanding of how we can improve patient care through big data analytics only makes sense.

data analytics in healthcare examples


To help deliver better healthcare, forward-thinking hospitals are increasingly adopting big data analytics. These health systems have been collecting piles of information on their patients and are now looking to analyze it to improve diagnosis, treatment and disease prevention.

The sheer volume of patient data—which includes everything from clinical lab results to social media updates—has caused many institutions to turn to big data analytics as a means of gaining insights that can improve care delivery and lead to greater efficiencies.

What do Healthcare Analytics companies analyze?

Data Analytics in Healthcare is growing rapidly as an industry.

Speaking of potential customers, there are many. Everyone on the planet earth whose health data is recorded and stored will be greatly benefitted.

  • Continous monitoring of health data using wearable devices, mining irregularities can be used for preventive health care.
  • Patterns and correlations on several factors can be drawn using history data and relevant treatment can be prescribed for a patient with similar symptoms and factors.
  • Feature engineering, pattern recognition can be used to figure out root cause for the disease.

Hospitals, Physician centers can utilize data to maintain sufficient facilities, resources.

Insurance companies can use to develop different insurance policies with different premiums.

These are few areas out of many.

Personally, I feel extensive and proper usage of analytics all over the world with adequate data can make our world healthier.

Why do healthcare companies use these tools?


There are numerous reasons why healthcare companies use big data analytics. One reason is to reduce fraud, waste and abuse. Another reason that healthcare companies use big data analytics is to improve patient outcomes and their own productivity.

By using these tools, healthcare companies can make better decisions about future business prospects and products. All of these help to create a more efficient and productive organization for these companies as well as their patients..

Big data analytics tools are becoming more popular with businesses in general because they allow for quicker data analysis processes than other forms of analytical software.

These tools require less time from workers and produce faster results which makes them ideal for hospitals where time is of a critical importance. This can be especially useful when dealing with emergencies or unplanned events because it allows hospital staff to make decisions more quickly.

What kind of information does the healthcare industry gather?


Health care organizations collect massive amounts of data from various sources. The more data they have on their patients, the better they can predict and manage future health risks.

For example, doctors may be able to use health insurance claims to determine what diseases are prevalent among specific populations, or how frequently particular treatments are needed by certain individuals.

Similarly, hospitals can use emergency room records to understand which types of injuries and illnesses frequently occur among different patient populations.

But there is much more information to gather than a doctor’s notes or hospital records: Behavioral trends and public health reports can all provide important insights into population health. In some cases, tracking non-medical information could even help doctors recommend preventative measures.

Who are these professionals in this field?


Biomedical engineers, data scientists, epidemiologists and healthcare professionals are important players in big data analytics. The social media has also contributed to Big Data Analytics as a movement. People feel more comfortable sharing their health-related information online through various channels such as blogs, Twitter and other social networks.

Big data analytics can be applied to various aspects of healthcare such as disease prevention and management, medicine discovery, diagnostics tools development among others.

What are some examples of big data use cases in healthcare?


There are a number of healthcare use cases for big data, but these three examples stand out: Wearable technology. Health care providers have been using wearable technology like Fitbit to track heart rate, steps taken, calories burned and quality of sleep.

They can then collect that data and analyze it to determine if an intervention is needed or if something as simple as a light jog will help. Genetic information.

Perhaps one of the most interesting applications of big data in healthcare is using patient genetic information to predict which medications might work best for certain individuals. If doctors know ahead of time which medication will work best for each patient, they can prescribe it more easily, leading to fewer complications and better outcomes.

Future trends


Predictive analytics is a highly sought-after skill and can easily be applied to healthcare. Employers want workers who have an understanding of statistics, machine learning and data mining algorithms.

With big data analytics, computer systems are able to identify patterns in data that may not be visible to humans (like picking out tumors on medical images). Big data techniques can also be used for health records, lifestyle choices and insurance claims.

If you work with large datasets or develop new predictive models, big data analytics could be a great career move for you. The field is growing fast and employers are looking for skilled workers with excellent communications skills.

Being able to effectively present your results will only add value to your candidacy when applying for jobs at top-tier companies.

Conclusion


In order to make big data analytics implementation a reality, healthcare companies need to start from somewhere. The best place to start is by implementing small-data practices—installing basic security software and patching systems against outside threats.

Then, you can move on to higher-level IT maintenance tasks such as managing your backup policy or implementing virtualization software on your network. Following these suggestions will help you pave a path toward a big data analytics implementation that will be truly impactful for your company.

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