- Healthcare big data analytics isn’t just a “use it or lose it” proposition for the provider community – it’s quickly becoming a “use it if you want to hold on to anything at all” situation for organizations that must invest in population health management, clinical analytics, and risk stratification if they are to succeed in a value-based reimbursement world.
Maintaining market share during this shift away from the simpler cash transactions of a fee-for-service environment requires organizations to take a proactive dive into their financial and clinical data, yet developing the technological and organizational competencies to take advantage of big data tools is just as complex as it sounds.
Despite the vital importance of using big data to describe, predict, and prevent costly events in large patient populations, providers of all types and sizes are struggling to collect, categorize, store, retrieve, and analyze their data assets.
In a recent industry poll, Stoltenberg Consulting found that big data confuses half of providers, and six percent of participants were too intimidated by the process to even consider starting a healthcare big data analytics program.
Why is big data analytics such a difficult topic to tackle for healthcare organizations? How do successful providers begin the process? In this article, healthcare stakeholders weigh in on the three most important foundational steps for beginning a big data analytics and population health management program.
Define a direction and outline specific goals
Big data may be nearly infinite in scope, but having data for the sake of having data will not help achieve measurable organizational objectives. Healthcare providers must start off their big data journey by defining clear, bite-sized problems that need solving. Often, these problems are the “low hanging fruit” of healthcare operations: preventable readmissions, emergency department overuse, chronic disease management, patient engagement, and primary care screening rates.
“I would highly encourage people to start around specific high-value use cases,” suggested Marc Perlman, Global Vice President of Healthcare and Life Sciences at Oracle during a 2014 interview. “I think the average hospital or health system has over 400 different interfaces and integration points, but it may take seven of them working together to give you some value out of your data. Hospitals and health systems need to think about what they’re trying to fix, and then based upon that, what data sources they need.”
“We think it makes a lot of sense to think about what they are going to try to accomplish, what the methods are that they’re trying to drive, and how they are going to focus on sustainability,” he continued. “I would say the most important thing is have a vision of your solution, what you’re trying to fix, and know where you’re going. And then the technology will follow.”
Successful healthcare organizations often flag financial pain points as the gateway into big data analytics. A March 2015 survey found that 59 percent of hospitals identified higher-than-necessary costs of care as one of their top motivating factors for implementing big data analytics. Organizations are also seeking the clinical and financial insights necessary to engage in pay-for-performance reimbursement structures and combat the pressures of accountable care.
To do this, organizations implemented clinical analytics technologies that integrate EHR data and patient outcomes into a rich portrait of a patient’s journey through the care continuum. More than 60 percent of organizations that took this approach have been able to improve their 30-day preventable readmissions rates and cut their mortality rates.
“Start out slow and have realistic goals,” says Dr. Robert M. Fishman, DO, FACP, who has helped to lead Valley Health Partners to the highest level of patient-centered medical home (PCMH) recognition thanks to investments in health IT and a fundamental attitude shift within the physician hospital organization (PHO). “And when you achieve those goals, set out new goals.”
“We set up a very modest program where we would pick a couple of diagnoses in internal medicine and a couple of diagnoses in pediatrics and we would begin to set up policies and think in a more patient-centric way to reach certain goals,” he said.
“We concentrated on congestive heart failure and COPD, because those two conditions produce a lot of readmissions and emergency department visits. There’s a lot of expense. If we could really get a handle on those conditions, we could improve care, improve outcomes and decrease costs. Three things that we're all very interested in.”
By focusing on solving specific use cases for big data analytics, these organizations have proven that small big data investments are worth larger ones in the future.
Measure twice, cut once to plan a health IT infrastructure
After outlining a strategy, healthcare organizations can start making investments in advanced health IT products to support their efforts. While the vast majority of providers already have a basic EHR infrastructure in place – and EHRs are increasingly coming packaged with population health management tools that meet many big data needs – sometimes more sophisticated products are required to get the ball rolling.
But crafting an interoperable health IT infrastructure with a high degree of flexibility and usability is a difficult ask. Stakeholders are only focusing on the desperate need for interoperability between data systems because it is so rare to find an ecosystem of well-integrated vendor products that communicate freely with one another.
Many organizations continue to be challenged by their convoluted legacy systems, and most providers cannot afford to rip out and replace an entire infrastructure. For those lucky few starting from scratch, 2015 is a good time to get into the big data analytics game.
As vendors focus on developing plug-and-play technologies and health information exchanges take on more mature roles in the local provider community, crafting an interoperable health IT system is easier than ever – if the right rules are on the table from the beginning.
“When it comes to doing big data analytics, you have to have two things right off the bat,” stated Richard C. Howe, PhD, FHIMSS, Executive Director of the North Texas Regional Extension Center, which also functions as the Dallas-Fort Worth region’s primary HIE.
“The first is strong governance. You have to get all the participants in the same room so they can determine what data they are going to contribute. Get the absolute governance structure outlined at the beginning so you know what direction you’re trying to go.”
“The second thing is to start simple. If you start off with thousands of different data elements, you’re just going to drown in the data before you see any results. We started with claims data, and we found that there is a lot of really good information there that has been valuable to our hospital members even before we started adding more clinical information. I would say good governance has to start simple.”
An organization’s data governance plan can make or break a big data analytics project: the old “garbage in, garbage out” rule will always apply. Even the most robust tools require their users to understand their potential and their limitations, both of which rely on the quality of data moving through the system.
Understanding the scope of health IT tools, the data standards they are built upon, and the data integrity requirements of leveraging health IT for actionable insights will help organizations pick the best products for their needs in the long term, not just in the next six months to a year.
“When it comes to tools, providers should consider products that can give them the functionality that they need to answer the questions they’re asking today, but can also grow with them to continue answering those questions three years and five years down the road,” said Shane Pilcher, Vice President at Stoltenberg Consulting.
“They have to be thinking three, five and maybe even ten years down the road in terms of what they anticipate their questions are going to be, so that they know they’re on the right track with collecting the data that’s going to answer them.
“Even from the EHR perspective, this is where that long-term plan comes into play. They know the type of questions that they’re looking for today,” he added. “They need to anticipate the type of questions they’re going to asking in the future. But in most cases, you don’t know what you don’t know, so you’ve got to be as creative, as imaginative as you can today when you’re setting up your roadmap. That’s going to give you the information that you need to start defining what used to be collected today in the EHR and what you need to grow.”
A far-sighted approach to big data analytics may help organizations avoid or mitigate some of the interoperability problems that have plagued the industry for so long. Investing in products that encourage health information exchange through standardized data elements will make analytics easier and ensure that organizations are set up for meeting ongoing mandates such as meaningful use.
Ensure support from executives and buy-in from clinical staff
No healthcare big data analytics plan can succeed without enthusiasm and support from all levels of the organization. The board room must provide the funding and the direction; the clinical end-users must understand and embrace new technologies and new workflows. Big data analytics isn’t just an IT project, but an organizational transformation from top to bottom.
Starting small, measuring results, and demonstrating improvement is often the key to securing executive buy-in, Pilcher says. “Once you start picking up traction, you can start to identify that low-hanging fruit that can lead to cost savings and improve patient care. These are cost savings that go directly to the bottom line of the organization and also show return on investment. By being able to show ROI, the administration may be more inclined to invest more time, more labor, and more capital to further enhance the program and go after bigger and greater fruits.”
And executive leaders may not take too much convincing these days. Despite the fact that more than a third of providers feel that a lack of leadership is a major barrier to big data analytics success, executives largely recognize the critical role that data competency will play in the immediate future.
Eighty-nine percent of hospital executives participating in a recent PwC poll are taking action to become more innovative and nimble through big data analytics adoption, and 95 percent are seeking to harness the potential of analytics technologies to extract actionable insights from their big data.
During the HIMSS15 Leadership Survey, three-quarters of organizations agreed with the notion that health IT is vital for achieving strategic goals and improvements in patient care. Over half believe that health IT has helped them improve their population health management programs. More than four in ten respondents think their executive leaders have a “fairly sophisticated understanding” of big data analytics technologies and the need to leverage them.
C-suite leaders are among the most likely to express an intention to purchase data analytics tools, with Chief Information Officers and Chief Medical Information Officers being the most eager to invest in new health IT products. Even Chief Financial Officers are recognizing the fundamental need for analytics infrastructure to cut costs, raise revenues, and utilize resources more appropriately.
Getting clinicians to understand why their workflows are suddenly changing can be even more complicated than securing funds to purchase new tools, however. It is important for providers to develop a multi-disciplinary team for big data analytics: one that includes representatives from all areas of the organization.
Clinical champions can help to explain to their peers why certain tasks are changing, why new metrics may be pointing out flaws in the patient care process, and why it is important to adapt to an evolving health IT landscape. Above all, both executive leaders and staff-level super users must be able to point to clear and immediate benefits when introducing a new tool, or risk rebellion among dissatisfied clinicians.
“When we are talking about big data, I think there needs to be a clear purpose,” says Tina Esposito, Vice President of the Center for Health Information Services at Advocate Health Care. “There has to be a core need or a well-defined problem that you are trying to solve.”
“Big data is a means to an end for solving problems. So you got to be very clear that you are not pulling this data together just to do put it together. There has got to be a focused effort from the right people to leverage that information so that ultimately you are supporting the business and your population health goals.”
“You need to be sure that what you are creating is usable in the most efficient and easiest way, and that it makes a positive impact on clinicians,” she said. “Is the clinician leveraging that intelligence that you are providing as part of their workflow in the EHR? Are they seeing a benefit from it? That’s going to be the most important piece of any big data project.”