How Big Data is Taking the Healthcare Industry by Storm

The Healthcare industry is a massive/huge/enormous (you can fill in any adjective you want) industry.

As per a recent report, national spending on healthcare in the US reached US$3.81 trillion in 2019 before increasing further to reach US$4.01 trillion by 2020. The report also goes on to state that healthcare spending in the US would reach US$6.19 trillion by the year 2028, accounting for 19.7 percent of gross domestic product (GDP).

We are not even talking in billions here. The statistics I just stated are only for the US. Now imagine this on a worldwide scale.

Even if the other countries don’t spend as much as the US on healthcare, the size of the industry is massive! When an industry is this big and loaded with data, why not bring in analytics? Oh wait, even better! Why not introduce Big Data Analytics here?!

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That is exactly what has happened and big data analytics is taking the healthcare industry by storm.

I am just going to point out a few ways as to how Big Data is changing the Healthcare industry:

To err is human, not with Big Data Analytics

We have come across various cases wherein wrong medications have been prescribed or dispatched to patients which has resulted in their condition becoming worse or even death.

Take the case of a hospital. With the number of records they have, keeping track of each and every patient requires a system and errors can happen just because of the sheer volume of data.

Leveraging Big Data Analytics to analyze user data and the prescription made to the particular patient can help reduce the errors.

Big Data Analytics can corroborate the data and rule out, out of place prescriptions which will reduce errors and save lives.

A tool or software for this would be ideal in a place which has many patients walking in on a daily basis.

Personalized medicines are the in thing

Personalized medicines are all the rage right now in the healthcare industry. Take a person’s genetic blueprint and lifestyle information and integrate it with thousands of other people.

This will help predict ailment and identify the best possible method of treatment. I just explained to you as to what personalized medicines are and how big data works in this space.

In case of an epidemic outbreak, big data can help track population movement with mobile geo location.

The actionable insights acquired from big data will give a fair idea as to where treatment centers should be placed or which areas need to be cordoned off.

The AT-RISK Factor

A combination of Big Data and Predictive Analytics can be used to classify people based on major health conditions.

The classification can be made based on the people who visit hospitals regularly and the high risk health conditions that are likely to affect a population in a particular geography.

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In the case of the US, obesity and heart problems are prevalent. With the BMI records of people and their visits to hospitals clubbed with their past medical records, big data and predictive analytics can tell us who may be at risk of a cardiac arrest or any other health condition like high cholesterol.

This will help in providing customized care to the patients.

At all COSTS

One huge problem that all hospitals or medical facilities face is the problem of staffing.

They are either under staffed or over staffed. Predictive Analytics combined with Big Data is an extremely powerful tool.

It comes to the rescue with this issue as well. Predicting the admission rates along with the attrition rate will help with staff allocation.

In turn, Rate of Investment is drastically reduced and investment can be utilized to the maximum.

Care in Real Time

Providing proactive care to patients is the need of the hour in the healthcare industry.

Constantly monitoring vital signs of a patient brings us one step closer to that. This data can be analyzed in real-time and alerts can be sent when there is a substantial reading change in the vital signs. 

Machine Learning algorithms can trigger these alerts and intervention can be made the right time as we get to know instantly when there is a change in the patient’s vitals.

The use of wearable sensors or devices helps interact with the patient in a new and more meaningful way. This makes healthcare more convenient and persistent.

Supply Expenditure

With the increasing number of hospitals and patients, the supply for tools in the medical facilities has also increased.

budget is prepared for the procurement of these tools prior to the purchase order. A common occurrence is over-stocking, where tools and supplies are bought in excess.

With the use of Big Data Analytics, predictions based on past year’s records can be made as to what the estimate for this year will be.

Predictive analytics enables hospitals to save a large chunk of their money by forecasting the demand for medical supplies accurately.

The saved amount can be reinvested to yield higher profits and or can be used as additional revenue.

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Evidence based medicine is where the world is heading today. This requires making use of all available clinical data and factoring that into advanced analytics. 

Capturing and bringing together all the information about a particular patient give a more complete view to attain actionable insights.

This will help reduce expensive testing, wasting of resources and will aid in the right prescription of drugs and saving more lives.

Big Data and Predictive Analytics play a massive role in the healthcare industry as not only agents that save lives of patients.

It helps hospitals and medical facilities reduce their overheads and also contribute more to the economy by making use of their investments in a sensible way.

In this article I have mentioned a few ways as to how Big Data Analytics helps the healthcare industry.

The benefits are not limited to these points. The opportunities are endless when it comes to Big Data and newer ways to help out the healthcare industry are going to keep following.

Author: Abhimanyu Sundar
Abhimanyu is a sportsman, an avid reader with a massive interest in sports. He is passionate about digital marketing and loves discussions about Big Data.