How Healthcare Analytics Has Changed in 10 Years

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How Healthcare Analytics Has Changed in 10 Years

By Steve West
Solutions Director, Streamline Health

Healthcare technology has evolved drastically, but it’s easy to forget just how far the industry has come recently. Enterprise and consumer technologies especially have changed a lot over the decade, getting faster and more complicated with each passing year.

Think back to 2007: Most phones still had physical keyboards, BluRay and HD DVD formats battled for dominance in the marketplace, and Microsoft’s Zune struggled to compete with other MP3 players.

In the healthcare world, industry professionals had just been introduced to new technologies like reporting software, data warehouses and advanced analytics. At the same time, regulatory changes and shifting social movements were influencing how doctors practice medicine.

Healthcare analytics has been at the center of many of the industry’s greatest achievements of the past decade. Here are a few solutions that advanced data analysis capabilities have brought to the world of healthcare:

Legacy technology needs to be left behind in order to move forward.Legacy technology needs to be left behind in order to move forward.

Replacing legacy technology

The forward progress of healthcare technology has left legacy systems in the dust. Switching to new systems is never easy, and big data analytics solutions have proven to be very complicated.

Leonard D’Avolio, an assistant professor at Harvard University, had this to say about the struggles of identifying and implementing big data solutions within a legacy system:

“What we learned from that, after the necessary growing pains of wasting billions of dollars trying to jam what is in effect new ways of doing business into existing companies without a very exact focus on the problem you are trying to solve and careful consideration of the existing constraints and workflows, is that you are very likely to end up basically running around with a hammer assuming every problem is a nail.”

Growing pains like these have been common over the last 10 years and continue to cause trouble. However, as systems become more sophisticated and individuals more educated, the learning curve grows gentler.

With time, the transition away from legacy technology will get easier. Millennials are now the largest generation in the workforce, according to Pew Research. And in a few more years, Generation Z won’t be far behind. Since members of these generations have grown up with technology, new advancements will be more intuitive.

Getting everyone up to speed on analytics

At the turn of the century, when big data was first hitting the scene, it was often defined by the “three V’s” of volume, velocity and variety, reported Health Catalyst. In other words, big data consists of massive amounts of data in widely varying file formats – and yet, in order to be useful, organizations must be able to utilize it quickly.

Innovators within the industry have pushed for more education around the introduction of healthcare analytics. In part, clinical documentation improvement programs, which encourage meaningful outcomes, have played a vital role in the acceptance of new technology. As documentation improves, more analytic possibilities emerge.

Patients expect their health records to follow them everywhere.Patients expect their health records to follow them everywhere.

Pushing for interoperability and integration

One goal of the Affordable Care Act, which became law in 2010, was to push for more integration and interoperability between EHR systems. The purpose of these stipulations was to not only make it easier for patients to move between care givers, but also expand the digital infrastructure of the industry as a whole.

When patients move between healthcare organizations, they expect their medical records to follow. In 2007, that wasn’t always the case. Today, 80 percent of organizations can electronically query patient health information from external sources; and more than half of all hospitals automatically notify primary care doctors when their patients are admitted to the emergency room, according to The Office of the National Coordinator for Health Information Technology.

Connections between organizations allow for improved analytics functions, which include reducing instances of duplicate care. With improved access to data, organizations can better engage patients and create more meaningful outcomes.

Introducing MACRA

In 2015, former President Barack Obama signed into law the Medicare Access and Chip Reauthorization Act, which ties reimbursements to quality of care and affordability. Today, many organizations are still struggling to make the necessary changes to avoid negative payment adjustments.

“Healthcare analytics can improve outcomes.”

Organizations that are able to report evidence of accomplishing pre-defined improvement measures stand to gain positive payment adjustments. Doing so hasn’t been easy, however. Healthcare analytics may make the way forward easier by automating certain value-based improvements. But the solutions aren’t cheap.

There’s still hope: Big data and healthcare analytics may be a significant investment, but when implemented correctly, they could save an organization money. Big data company MAPR​ noted that, since the Affordable Care Act and MACRA place more importance on value-based care – and analytics can vastly improve outcomes – it stands to reason that implementation of big data analytics could help protect an organization’s revenue cycle.

Internal code audits have also helped in this regard. Empowered assistance can help lower the number of erroneous charts, reducing the likelihood of claims denials.

Implementing new technologies the smart way

Not only can healthcare analytics protect an organization’s revenue cycle, it can also lead to some stunning predictive uses. Some organizations use years of data to predict admission rates for any given day, for instance.

Consider another example: According to the Harvard Business review, fewer than 0.05 percent of newborns have blood-culture confirmed infections, yet 11 percent of all newborns receive antibiotics. To stem this potentially dangerous overuse of antibiotics, Kaiser Permanente of Northern California used predictive analytics to accurately predict neonatal infections based on the mother’s clinical history.

These are just two of many useful applications of big data. Looking to the future, healthcare organizations will be able to develop better predictive systems to optimize patient care and resource allocation.

The industry has come a long way in the past decade, and it’s exciting to think about where it will go next. From big data analytics to robots and beyond, it’s an amazing time to be in the business of healthcare.

By | 2017-05-10T20:18:13+00:00 May 10th, 2017|Categories: Healthcare Industry, Meaningful Use|0 Comments

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