Big data is the art of extracting information from a complex and large database. But what can it do in healthcare? Marvels! The right collection and analysis of big data from different sources with utmost concentration on details improve your system. A well-maintained data is the key to a great system, and big data analytics does just the same for healthcare as well. So, how big data analytics is used in healthcare?
What is Big Data In Healthcare?
To be precise, big data collects all the data from a healthcare system, be it offline or online. This data is then stored and analyzed to gather any useful information related to a patient or a disease. With this information, a healthcare firm can make better and cost-effective decisions.
Big data analytics in healthcare collect data from IoT sensors, clinical, and other medical records. This data is securely stored in a data warehouse, where it is further analyzed. The analytics of this data helps the physicians get better insight and foresight to the various issues they encounter. Advanced analytical models can even predict a pandemic outbreak or a potential diagnosis. However, this collection of data needs a lot of time. The bigger the data, the better the prediction model will perform.
Sources of Big Data in Healthcare
Now that you know that the first step in big data analytics is collecting the data, how does one do that? The sources of big data in healthcare are numerous. IoT, digital medical records, traditional prescriptions, and what not!
Medical records are a classic example of data related to patients. With digitalization, the medical records are converted into a digital form, which is easier to process, even if there is a hand-written form. This is one of the most genuine foundation data for a big data analytics system for healthcare.
IoTs are things connected to the internet, be it a sensor, a fridge, or maybe your car. With connectivity to the internet, IoTs can interact with us and gather data for which it is deployed and then transmit it. Health-tracking wearable devices, biosensors, vital signs trackers are all examples of IoTs used in healthcare.
Some institutes conduct big data in healthcare case study/studies, and they publish the raw data on a public platform. This data can serve as a nice source for your data reservoir as well.
Applications of Big Data in Healthcare
The most direct applications of big data in healthcare include improving staffing, constant tracking of patient’s health conditions, better system development, etc. However, the most pivotal among these applications are the ones listed below.
Collecting such large volumes of data is a hectic task. Big data strategies make it possible to sort even the largest volumes of data, and then find trends from them. This data, the cornerstone, feeds the entire system to make predictive analysis and all the other functions.
The name says it all. How will a patient feel when the system can predict their medical condition after two years? Or what if the system can list out the potential diagnosis a patient might confirm in the future? This is what predictive analytics does. The vast amount of aggregated data helps the analytical model to make predictions on a patient, and hence the better will be the diagnosis.
Well, it is great to predict the medical conditions of a patient in the near future. But, what if the system can predict any possible pandemic? Advanced risk modeling algorithms in big data takes the game to another level. There are software simulations that can predict the spread of diseases. We even have models that can predict the spread of COVID-19 as well.
Challenges Involved in Implementation
With all its beauty, big data is a hectic process. It needs the expertise to stack all the available data and make an analysis. You need developers, analysts, a completely secure system, and wonderful infrastructure. Let’s take a look at the challenges of big data in healthcare.
Even though it is the strength of big data analytics, you will be searching for a needle in a haystack without the right expertise. The data required to set up a data warehouse is distributed throughout different departments. So, data aggregation integrates data from the different departments under healthcare. And then, big data ensures accuracy through data cleansing, which is aided by data aggregation.
It would be best if you had a whole new team to deploy your big data analytics system. Then comes the investment for proper maintenance, including training your professionals with industry standards. There will be more staff, and hence an increased expense for salaries.
With such sensitive data of your patients, including their contacts & addresses, your system should be completely secure. Any data leak can result in serious damage to the patients and your reputation as well. Some hackers purposefully attack healthcare organizations and leak information online. Therefore, you must keep your security always up, which is very difficult.
As said earlier, safeguarding confidential data is pivotal. And that is why the government has developed data safety regulations to protect the concerned data. So, your big data system should meet the data privacy regulations of your country. The USA follows HIPAA and India have DISHA as their data privacy regulations. These regulations are exclusively made for healthcare data privacy.
Commercial Platforms for Healthcare Data Analytics
Hadoop, MapReduce, PIG, & Hive are some of the big names in big data analytics. These platforms are databases that collect and sort data through various strategies. Modern markets have pressured the resources to keep up with the system. Therefore, you can find experts in big data analytics rather easily. They can be your one-stop solution for your own big data system.
Even with the field’s challenges, big data in healthcare can do wonders for your firm and the patients. More interesting is its predictive nature and the ability to deliver advanced diagnoses for the patients. These features ensure a highly safe life for the patients, which generates better revenue for your firm. Get in touch with us at firstname.lastname@example.org for any further queries!