Healthcare Analytics, Population Health Management, Healthcare Big Data

Tools & Strategies News

What Will 2017 Bring for Healthcare Analytics, Interoperability?

2017 is likely to be a red letter year for healthcare analytics as interoperability, machine learning, and population health management bring major changes to the industry.

By Jennifer Bresnick

- After a tumultuous end to 2016, healthcare providers may be looking for a chance to catch their breath before diving back into solving some of the industry’s most intractable problems.  But the growing pressures of interoperability, big data analytics, and value-based care aren’t likely to give anyone a break.

2017 is already gearing up to be a challenging year for hospitals, physicians, and their expanding web of partners, with the start of MACRA, a potential political upheaval of the Affordable Care Act, and a slew of cutting-edge technologies slated to change the game for the entire care continuum.

Interoperability will remain the watchword of the year for healthcare organizations looking to turn their big data into actionable insights, experts predict, while the tectonic forces of value-based reimbursement and financial risk continue to reshape the revenue cycle and clinical landscape.

Provider consolidation will build the business case for interoperability

Many healthcare stakeholders have already decided that there is safety in numbers, and have used 2016 to invest in mergers, acquisitions, and consolidation projects intended to bring financial stability to entities that are loath to face the prospect of risk-sharing on their own.

READ MORE: Healthcare Artificial Intelligence, Cognitive Tools Bring Big Investments

As physicians continue to accept employment in larger health systems, and those health systems shift from hospital-centric acute care centers to wellness networks with a focus on prevention, organizations are finding that their data sharing strategies must also change.

“In order to understand as much as they can about their attributed patients, organizations need to share and collect as much data as they can,” said Sanket Shah, Professor of Health Informatics at the University of Illinois at Chicago, to  “The industry is going to have to break down the data siloes that are still causing information sharing problems.”

“That takes collaboration, and it takes partnerships.  I’m hoping to see much more of that in 2017.  As health systems get bigger and bigger, we are hopefully going to see some more seamless data exchange within them and between them.”

But sharing data for data’s sake will not help providers accrue the population health management competencies and actionable insights required to meet new quality metrics, report on progress, and improve the patient-provider relationship.

“Interoperability will need to be embedded deeply into a broader strategy centered on quality and performance” in order to effect real change, asserted Arien Malec, Vice President of Data Platform Solutions at RelayHealth and Co-Chair of the ONC Health IT Standards Committee.

READ MORE: Top 4 Machine Learning Use Cases for Healthcare Providers

“In 2017, we will see increased merger and acquisition activity, along with reduced regulatory oversight of those agreements, which will cause the healthcare landscape to reshuffle around a smaller set of provider organizations and payer organizations,” he predicted.

As mergers create more tightly-knit administrative relationships, they will also produce a new business case for data interoperability, he added.  Patients moving between multiple providers that share the same brand name will expect their data to follow them, regardless of whether or not the same electronic health record is in use at every single location.

“Most of the individual organizations coming together into these larger groups will not have the financial means to replace all of their information technology to start using the same systems, but they will still need to share data seamlessly within the new organization,” Malec points out.  “That will create a strong business imperative for interoperability.”

While Malec anticipates a strong shift towards improved data sharing by the end of 2017, respondents to a recent Black Book Research poll aren’t as convinced that they will have the resources to support improvements to their data sharing infrastructure.

Eighty-five percent of hospitals that acknowledge their data siloes are not planning to spend any money on enhanced interoperability tools in 2017, Black Book found, while 88 percent of smaller, financially unstable hospitals are also putting data sharing developments on the back burner.

READ MORE: Navigating the Hype of Healthcare Artificial Intelligence Companies

In general, technology spending could see a downward slide of about 13 percent, the survey participants said, with staffing levels remaining stagnant due to high costs and a sharply competitive marketplace for qualified big data professionals.

Budgets may be shrinking, but big data is only getting bigger

Providers may be hedging their budgeting bets, but Shah doesn’t believe that analytics development is going to come to a halt during the next twelve months.  In fact, despite the reticence to commit to a dollar figure, healthcare organizations are likely to find that investments in real-time analytics and predictive analytics will contribute to their profitability sooner rather than later.

We are going to see more collaboration between hospital systems, vendors, and the payer community to start really building out some predictive analytics,” Shah said.  “We will also see more emphasis on real-time analytics.  I believe that in 2017, we’re going to see a big jump in our velocity.”

Population health management and value-based care will drive providers away from slower claims-based analytics and towards a more immediate approach to information sharing.

“Right now, we’re still in a claims-based world for the most part,” he explained.  “‘Real-time’ is still ‘near-real-time,’ or maybe even a couple of months old.  So if there’s that much lag in your predictive modeling, how accurate are those predictions going to be?”

“When a patient goes into the emergency room, we’ll be able to see it immediately and leverage that information now, instead of in four months from now after that claim gets adjudicated and then finalized.”

In order to reach this next level of big data analytics, organizations may have to spring for a new generation of tools, whether they really want to open up their wallets or not. 

Existing strategies and software packages are simply not up to the task, believes Jake Freivald of Information Builders, and providers are already “hitting the wall” with “modern” business intelligence tools.

“We consistently find that [providers] are running into the same things with their existing BI tools that they used to see with Excel: untrusted data, different results from different people, and answers that open up more questions,” he said in emailed commentary.

“Don't get me wrong, I don't expect them to go away – just like I don't expect Excel to go away, and for many of the same reasons.  But their current position has the feel of peak pendulum position to me.”

Organizations that do not invest early in broad, comprehensive data governance programs are likely to find themselves without the trusted analytics firepower required to succeed with population health and other reform initiatives, Freivald warned.

“Most of these problems are related, at least in part, to a lack of governance,” he said. “Sometimes they lose trust because people source their own data, and it's hard to govern data that can come from anywhere. Sometimes the analytical processes themselves are too open-ended, unpredictable, and unreproducible.”

Entities that find themselves in this position may either need to rip-and-replace their big data analytics infrastructure or start undertaking a massive governance overhaul to salvage the tools and data assets they have.

2017 will be a good year to dive into these difficult processes, said leaders from AHIMA at the 2016 Convention in October, since health information management professionals have been recalibrating their expertise to align with value-based care and the data governance challenges of big data analytics technologies.

“Our future is not only to help interpret that clinical story, but to move the industry forward as we change how we get paid and how we treat patients,” said AHIMA CEO Lynne Thomas Gordon, MBA, RHIA, CAE, FACHE, FAHIMA.  “The HIM role is expanding into informatics, data analytics, and information governance to make sure all this information can be trusted.”

Without trusted data, providers will be unable to make the informed decisions required to boost performance, bring in revenue, and improve outcomes, added Ann Chenoweth, MBA, RHIA, FAHIMA, President and Chair of the 2017 Board of Directors.

“Trusted data must be reliable, accurate, accessible, where and when it’s needed,” she said.

“Having that integrity and governance around the data is key, and HIM will do that for you. HIM has a unique skill around understanding the flow of information, the source of information, and the nuances of data. They’re a critical part of a team of data scientists, IT experts, and clinical leaders, to really put meaning to the bits and bytes in this huge big data pool we’re creating.”

Machine learning and artificial intelligence will bring new players into healthcare

Developing a trusted, accurate, and reliable source of big data will help to create a strong foundation for the next major wave of health information technology: analytics tools driven by machine learning.

Machine learning, the basis for what may eventually become true artificial intelligence, has already captured the imagination of the healthcare industry.

Providers have been employing natural language processing techniques for years to extract meaning from free text or static images like PDFs and ease the burdens of clinical documentation.

Now, vendors from all over the tech industry are trying to capitalize on these early successes by creating sophisticated algorithms that don’t just extract data, but also learn from it.

IBM, Google, Microsoft, Apple, Amazon, and even Facebook are all dabbling in machine learning and AI projects that may find a home in healthcare, bringing name recognition and broad expertise to a complex and exciting use case for the big data providers have been dutifully collecting.

“I believe that there will be more collaboration with companies that aren’t traditionally known in the healthcare space right now,” Shah predicted.  “Some of these vendors, like Apple and Google, are starting to emerge as key players. 

“Working with those names and their brands is probably going to be very appealing to many healthcare organizations,” he added.  “Healthcare is a very difficult industry to jump into, but if they have the means and the foresight to hire experts with experience in the field, they are going to have an edge over their competitors.”

2017 may be the year that machine learning truly starts to strut its stuff, a Silicon Valley Bank survey from September intimates, due to the large number of potential applications for artificially intelligent systems.

From chronic disease management and customer service to clinical decision support, AI offerings driven by machine learning may be able to reduce human workloads while fostering tailored, personalized patient engagement.

Thirty-five percent of respondents to the poll said that artificial intelligence was going to be 2017’s biggest breakthrough – more than twice the number that pinned their primary interest to the Internet of Things.

As new markets, such as “machine learning as a service,” start to drive increased business opportunities for big data experts with a healthcare focus, the coming months may see providers start to transition away from just hoarding data and towards the ability to leverage it for measurable improvements.

Doing so would allow early adopters to surge ahead in the race to deliver the highest possible quality care at the lowest costs, bringing shared savings for those participating in value-based care programs and better outcomes to patients across the care continuum.

How Healthcare Can Prep for Artificial Intelligence, Machine Learning

The Difference Between Big Data and Smart Data in Healthcare

Continue to site...