What Is Big Data in Healthcare, and How Is It Being Used?

In this article, we’ll define big data in Healthcare and discuss how (and by whom) it’s currently being used to improve patient care.

Big data in healthcare is a major driver of the new MACRA EHR requirements and the legislative push for interoperability.

The term “Big Data” is a popular one these days.

For some time, the topic has been making waves in other industries, but many of its applications in healthcare are still in their early stages. As evidenced by some interesting use cases, the use of big data shows exciting promise for improving health outcomes and controlling costs, but the practice appears to be defined somewhat differently by each expert we ask.

I wanted to know what big data will mean for healthcare, so I spoke with Dr. Russell Richmond, a big data analytics and healthcare informatics expert, about what the future holds.

Dr. Richmond is a leading healthcare technology expert who has built large data analytics companies, consulted with health system executives, and served on the boards of big data organizations.

The biggest big data in Healthcare benefit: more precise treatments

One of the most exciting implications of big data in healthcare, according to Dr. Richmond, is that providers will be able to provide much more precise and personalized care.

They will be able to determine how a specific patient will respond to a specific treatment, or even identify at-risk patients before a health issue arises, with a more complete, detailed picture of patients and populations.

“More information yields more granular diagnosis, which creates the opportunity for more precise treatment,” as Dr. Richmond put it.

Big data is already being used in healthcare—here’s how

Understanding the big picture of big data in medicine is important, but so is recognizing real-world data analytics applications as they are used today.

To that end, here are a few notable examples of big data analytics currently being used in the healthcare community.

More accurate diagnoses

Data analytics tools that provide better clinical support, at-risk patient population management, and cost of care measurement are already available. Many of these systems have built massive databases, some with billions of data points, to which they can then apply sorting and filtering algorithms to quickly analyze all of that information.

One amazing feature that users can use is to determine how differences in patients and treatments affect health outcomes. Providers can develop more precise treatment plans for individual patients or patient populations based on these insights.

For example, in a world of big data, “general asthma” may no longer be a sufficient diagnosis. Because of the granularity of big data, we may be able to detect and diagnose multiple variants of asthma, with different treatment pathways for each. Data mining could direct physicians to the specific treatment plan required by each patient’s individual case.

Predictive medicine

As Dr. Richmond mentioned during our conversation, genomics is the next frontier of medicine.

The cost of genome sequencing is decreasing; you can now sequence your entire genome for a couple of thousand dollars, compared to around $100 million a decade ago. As a result, the volume of genomics data is rapidly increasing, as is our ability to exploit that data.

Using genomic data, we can already predict how diseases like cancer will progress more accurately.

Population health management

While the primary goal of big data in medicine is to improve patient outcomes, another significant benefit of data analytics is cost savings.

Some systems can collect information from revenue cycle software and billing systems to aggregate cost-related data and identify cost-cutting opportunities.

For example, the state of Rhode Island has partnered with InterSystems to collect and analyze patient data on a statewide scale using its HealthShare Active Analytics tool. The state’s Quality Institute then discovered that approximately 10% of major lab tests performed in over 25% of the state’s population were medically unnecessary—a discovery that has since helped Rhode Island reign in spending while also improving care quality.

Big data for the small practice

So, while this is great for large health organizations that can afford big data analytics tools today, what does it mean for independent practitioners?

“We’re spending an awful lot of time putting information in [to digital systems like EHRs], but we haven’t yet harnessed the insight that comes from using that information once it’s in,” says Dr. Richmond.

However, some progress is being made. Interoperability was a recurring theme in a recent survey of medical providers on the impact of the HITECH Act. Patients, too, are eager to reap the benefits of more widely disseminated health data.

Legislators have long talked about empowering medical providers to become more connected, but interoperability has only recently become truly necessary for Medicare reimbursement qualification. MACRA is now incentivizing and mandating the use of EHRs that support interoperable functionality.

Aside from federal regulations, investors see big data as a huge moneymaker—and more investment means more solutions.

Dr. Richmond anticipates that big data and personalized medicine will help eliminate “one-size-fits-all” treatment approaches in a few years. And, more importantly, he believes that the ability to better manage care will result in lower healthcare costs.

Related Article:
https://www.softwareadvice.com/resources/what-is-big-data-in-healthcare-and-whos-already-doing-it/