What Is Health Analytics and Why Do Enterprises Need It?

The amount of patient data available to healthcare organizations – estimated to reach roughly 25,000 petabytes by the year 2020 – is beyond comprehension. And it’s only growing in volume and velocity. A CIO article notes that the increasing digitization of healthcare data means that organizations often add terabytes’ worth of patient records to data centers – annually. Why Health Analytics Important

The most shocking part?

The majority of that data goes unused – and, with it, the opportunity to produce actionable clinical and financial insights, improving both the quality and efficiency in healthcare.

A KPMG survey of 270 healthcare professionals shows that only 10 percent of respondents are using advanced tools for data collection with analytics and predictive capabilities. Less than one fifth of survey participants rely on data warehouses to track key performance indicators, and just 16 percent are using data in strategic decision making.

“We are only skimming the surface about using the full potential of data and analytics to improve healthcare,” says John Weis, director of data & analytics at KPMG, in a Managed Healthcare Executive article on the research findings. “The executives surveyed see the full potential being reached in three to four years.”

It’s clear that harnessing the power of healthcare big data requires synthesizing and analyzing information to influence patient outcomes, create differentiation, and drive revenue growth. But, with the wealth of patient data available (not to mention the breadth of analytics vendor solutions), it can be daunting trying to figure out where to begin.

So let’s start at the beginning:

What is Health Analytics?

Health analytics is the process of deriving insights from patterns and correlations in data that can be used to make better healthcare decisions. Health analytics extends beyond data management to finding meaning in real-time or historical data, and making predictions about the future to improve the probability of success.

That is how we define health analytics at Evariant. But, with so many organizations claiming to be doing health analytics, the fact of the matter is that one vendor’s definition of analytics can look vastly different from another’s. Add to that complexity the myriad categories of health analytics (i.e., population health, risk, revenue, etc.) and the definition gets even blurrier.

The same KPMG survey referenced above shows that the perception of the value of analytics also varies significantly for companies in the healthcare industry. Fifty-six percent of survey participants say business intelligence is the greatest benefit of health analytics, while 35 percent cite lowered costs, and 32 percent note improved health outcomes.

Talk about confusing!

Benefits of Health Analytics

Let’s take a look at the primary benefits of health analytics, as well as examples of how it can be used to achieve greater customer engagement:

Better Data = Better Insight = Improved Outcomes

While healthcare data management and analysis exist as two different stages of the health analytics process, the truth is that analytics can’t exist without data. Before analytics can begin, health systems need to acquire, capture, cleanse, and integrate data from multiple sources with the help of a centralized healthcare analytics platform.

In that sense, there is an overlap between where data preparation ends and analytics begins. With valid and accurate data in place, healthcare enterprises can begin the process of interpreting the data to inform future interactions with patients, prospects, and populations.

Let’s say, for example, healthcare marketers have the goal of creating tailored, relevant messaging reminding certain consumers to get their annual flu shot. Analytics allows organizations to determine which patients are at highest risk for the flu, who has or has not received a flu shot, analyze which messages are more or less effective for different groups of people (based on historical data), and make predictions about which communication channels will result in the best response rate from each group (i.e., predictive modeling).

Analytics can also help to uncover the root cause of response or, conversely, lack of response (i.e., distance to healthcare provider, availability of flu shot, or even bad data (for example, a patient who did get a flu shot from a provider in a different network)).

The overall goal is to use analytics to engage large numbers of patients in ways that improve every patient’s quality of life.

Performance Improvements Across the Organization

According to a new poll by GE and Accenture, big data analytics is a priority for roughly 90 percent of healthcare organizations seeking to improve clinical quality and gain market share. A Health IT Analytics article covering the research findings notes that 80 percent of healthcare executives believe providers who adopt an analytics strategy in the coming years will outpace their peers when it comes to clinical quality and operational bottlenecks.

“There is much more data available that requires additional expertise,” says Christopher C. Colenda, MD, MPH, President and CEO of West Virginia United Health System, in the article. “Having the right people with the right skills to interpret the data—biostatistics, epidemiology, health informaticists, other health professional clinicians [is critical].”

The research shows 40 percent of executives surveyed will invest in an analytics platform to improve patient data and staff workflow in the next three years; a separate study from Damo Consulting shows upwards of 60 percent of healthcare providers plan to invest in analytics to improve operational workflow and efficiency in 2015 alone.

With a healthcare CRM platform in place, organizations can not only integrate multiple sources of information into a single pane, but transform the data into actionable information used to drive future campaigns. Specifically, the CRM allows enterprises to build, design, and execute campaigns, analyze performance with key stakeholders, and report on campaign statistics.

The End Result of Health Analytics?

More strategic marketing campaigns that result in improved outcomes – as well as an attributable return on investment.

Final Thoughts

In today’s competitive healthcare landscape, health analytics competency is more important than ever. Organizations that fail to effectively harness their data to monitor business processes and produce actionable clinical and financial insights risk falling behind more agile competitors in the next few years.

Regardless of where an organization falls in the maturity curve of health analytics, the imperative for marketers is to effectively analyze data to inform campaign planning, increase responses, and enhance the patient’s overall experience.

Data is Meaningless, Health Analytics are Everything

Avery Earwood

Avery Earwood

Avery Earwood is the VP of Analytics and Data Science at Evariant. His passion is helping Evariant’s clients use their information assets to improve the cost and quality of health care, engage patients, and maximize overall performance. Avery specializes in analytics strategy, population health, consumer engagement, and Lean process improvement. Prior to joining Evariant, Avery served in a variety of executive-level strategic planning and leadership roles as Principal Strategist at SAS, VP Strategy at Blue Cross Blue Shield of TN, and Executive Director at Healthways to name a few. Avery has an MBA in Healthcare Management and a Bachelors of Science in Business Leadership and Management from Capella University. His certifications include Agile SCRUM Master and Product Owner, Strategic Leader, and LEAN. In his spare time, he raises Alpacas with his wife and three sons on a small farm in Rougemont, North Carolina.
Avery Earwood