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Big Data in Healthcare Explained

What is Big Data in Healthcare?

Big data in healthcare refers to medical practices collecting, storing, and analyzing data to understand patients better and provide them a higher standard of personal care.

Medical practices now possess more data than they’ve ever done before, not least because digital programs, apps, and tools are more prevalent and are increasing in use.

Because of this, it’s simply not viable for humans to go through information themselves, so instead hospitals are using digital means to capture and assess data, then providing practitioners and other healthcare professionals actionable understandings to help their decision-making.

Why is Big Data So Important Today?

When we say that practices have more data than ever before, we don’t just mean a little bit more, we mean an industry-changing amount more—in the last four years the amount of data held by healthcare providers has increased nine-fold.

Healthcare organizations have seen an explosive health data growth rate of 878% since 2016, according statistics compiled by Dell EMC.

While this figure may be a shock, it is in fact mostly in line with what has been occurring in virtually every industry over the last 10 years.

Whether it’s manufacturing or retail, businesses in every industry are being affected by the massive growth in big data.

When you combine this with the relative cost-effectiveness and ease of implementation for systems that can make use of big data sets that organizations now possess, then you have a business landscape that is being somewhat upended by big data.

Healthcare is no different, and its importance will grow, just as it has with other industries, as we go forward into the new decade.

Global healthcare industry data analytics market 2016 vs 2025

How Are Healthcare Providers Using Big Data?

Staff management

Managing staff and shifts is absolutely crucial for providers. With costs rising, overstaffing can have significant effects on your bottom line.

At the same time, an understaffed provider won’t be looked at favorably by patients if their care is affected.

By establishing a system which can assess historical admission rates, big data sets can be automatically analyzed, allowing you to see when you’re busiest down to the minute.

Hospitals are already doing this—in one instance a provider used machine learning to trawl 10 years’ worth of admission data.

The analysis they received showed shift managers the predicted admission rates on any given day for the following two weeks, which they then used to more efficiently allocate shifts to staff.

Patient engagement

Most of us are familiar with wearables, but the information they provide and their use in healthcare as a means for primary care physicians to assess their patients is another way providers are using big data.

There are wearable devices available to consumers for a vast array of uses. Here’s a few examples:

  • Fitness trackers: FitBits are probably the best-known example, equipped with sensors to help users track their physical activity and monitor their heart rate
  • ECG devices: Help users monitor their heart rate, heart rate variability, respiratory rate, temperature, and activity
  • Blood pressure monitors: Use oscillometric technology to measure blood pressure

What’s the significance of these?

Well, they can report data directly to a patient profile, which your PCP has on record.

If something is abnormal, like heart rate, temperature, or blood pressure, the doctor can be alerted, and in turn he can contact the patient and arrange a consultation.

This is especially useful for older or at-risk patients, and will become even more important as aging populations continue to grow.

Early prevention

Big data first and foremost relies on structured data in order to conduct automated crawls.

Of course, the issue with this is that healthcare (like many industries), is inundated with unstructured data that makes it difficult for humans to make the most effective use of.

80% of medical data remains unstructured and untapped after it is created (e.g., text, image, signal, etc.) Since it is hard to handle this type of data for Electronic Medical Record or most hospital information system, it tends to be ignored, unsaved, or abandoned in most medical centers for a long time.

And this is why providers are turning to machine learning, which can go through your data and give you actionable insights for physicians to use.

In essence, what this means is that unused data that may have previously been missed is now available, and can be used to identify medical conditions that weren’t apparent before.

Big data is important for healthcare in 2020

Error reduction

One of the biggest benefits of data analytics implementation is the reduction in human error wherever possible.

Many administrative tasks that are vital to the running of a healthcare provider, such as documentation for bills, records, and statements, are susceptible to human error.

Most healthcare organizations use human review to manually classify and extract data from Medicaid documentation, such as hospital bills, tax forms and bank statements. This is a costly and time-consuming process, with error rates for data entry as high as 4%. That comes out to 400 errors per 10,000 data points, a significant number that could jeopardize care.

It’s no secret that healthcare organizations want to remove even the smallest possibility of errors; in fact, 91% of executives rank reducing medical errors as a high or very-high priority compared to other clinical initiatives at their health system.

Providers are increasingly turning to digitization solutions to help alleviate this, using document capture processes that read forms and other data mediums, automatically entering the information into your database rather than having a human manually process it.

AI and machine learning are helping these document processes by removing the human element and implementing digital document extraction that is more accurate. Machine learning improves with every capture, as it learns how to deal with validating data by drawing from an ever-extensive pool of previous captures.

Obstacles to Using Big Data in Healthcare

While there’s no doubt that big data in healthcare is resurgent is several aspects—namely in adoption and the relative cost-effectiveness as opposed to previous years—there are also obstacles to its implementation, as well as skepticism about its benefits.

As we previously noted, while investment in big data from providers has seen huge increases over the last few years, use of data as a whole is lagging behind other industries because of a number of concerns that have to be addressed.

Naturally, one of the biggest concerns about the use of big data in healthcare is patient security.

Regulations such as HIPAA must be abided by health organizations, but they are largely left to their own devices to figure out what policies they put in place to protect the security and privacy of their patients’ data.

In addition, getting to grips with utilizing big data can be a daunting task, requiring personnel who are experts in data science, IT, or statistics, and establishing a communication policy that ensures that requested queries and reports from data administrators are up to the standard necessary for physicians to use is important.

The Outlook for Big Data in Healthcare

While there are obstacles to the use of big data in healthcare, the advantages to making use of the vast quantities of unused data within providers is helping organizations in several ways; financially, in administration, and most importantly for the provision of healthcare itself.

As the industry progresses, big data will continue to be adopted by providers as they leverage its benefits.

For organizations that are weary or unsure of how they can utilize big data, it is more likely for them to start small with systems for administrative office purposes before implementing more advanced measures.

Nonetheless, we can expect widespread adoption of analytics and automation in the healthcare industry by as early as the mid-2020s, and providers should strongly consider what their approach will be to big data in the coming years.

Bottom Line

  • The amount of big data present in healthcare organizations is vast and increasing, but many providers do not utilize it.
  • Uses and benefits of big data analytics are growing every year, and the rate of adoption suggests practitioners are beginning to take notice.
  • There are still hurdles for providers to overcome, particularly with regard to data security and compliance.
  • Big data utilization will increase over time as the healthcare and financial benefits become more apparent and cost-effective to implement.

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