Few industries generate more frustration for everyone involved – customers, companies and employees – than healthcare.
"On average, healthcare is a $220-a-month product for which we're paying $570," Athenahealth CEO Jonathan Bush writes in the December issue of Harvard Business Review. "It's simply infuriating." Athenahealth cloud services and mobile apps help healthcare providers streamline processes that divert resources from patient care. Their mantra? "Let doctors be doctors."
The operational inefficiencies of healthcare stem from a range of sources, including antiquated record-keeping, data silos and onerous regulatory requirements. But there are promising signs that healthcare payers and providers are making the right investments in analytics to stop the leaks and right the ship. They also are exploring analytics opportunities to improve diagnosis accuracy and prescribe better-researched, more personalized treatment plans for a variety of patient conditions.
Two recent analyst reports provide seeds of optimism. First is Ralph Finos of Wikibon, whose survey of eight U.S. verticals noted that "Healthcare had a very significant gain in Big Data usage between spring 2014 and fall 2015."
In fact, 78 percent of healthcare respondents – more than any other industry – reported they have near-real time operations or transactions that derive from analytics-generated insights. Fully 68 percent have successful Big Data production projects, second only to financial services at 69 percent. Both healthcare and financial services had 69 percent of respondents stating that Big Data is a "new source of competitive advantage" that is or will be "fundamental to our business." Finos notes that healthcare providers and payers are using analytics for patient analytics and risk management in particular.
This is a surprising digital achievement for an industry that has relied on doctors' handwritten scribbles well into the 21st Century. Challenges, of course, remain. While healthcare is more serious about Hadoop than most other verticals, healthcare respondents reported a lower than average satisfaction with this new open source platform for analytics. Finos surmises that healthcare companies are still in early days with new platforms like Hadoop, and in many cases are short on the necessary skill sets to capitalize on them.
So what does success look like for payers and providers? Michael Lock with Aberdeen sheds some intriguing light on this question in a recent report that compares healthcare leaders with followers. Lock distinguishes healthcare leaders based on (1) the rate at which they are increasing their "searchable" or "discoverable" data (56 percent year over year on average for leaders, versus 5 percent for followers); (2) respondent satisfaction with the relevance of analytics to their jobs; and (3) the portion of respondents that increasingly trust the data underlying their decisions (77 percent leaders vs. 41 percent followers).
In short, healthcare leaders can base their decisions on more data, and more trustworthy data, using more relevant analytics. They rely on operational dashboards and real-time reporting and analysis tools, as well as data management and data discovery tools.
Aberdeen reports these leaders increase their revenue an average 20 percent annually, more than double the followers, and have reduced operating costs by 15 percent year over year. While the report doesn't find that analytics usage drives top and bottom line performance, the implication is that data analytics at least contribute to profitability.
So what can we learn from healthcare leaders? Most leaders have an exec-level sponsor or champion for business analytics, established data governance and user access policies, and defined policies for defining and communicating business needs for analytics. Most followers do not have these things.
At Attunity we work with a number of healthcare payers and providers that seek to make better operational decisions with analytics. They are working to more rapidly integrate data across platforms - ranging from EPIC Clarity to other major DB, EDW or Hadoop platforms, on-premises or in the cloud - to lay the foundation for analytics.
But the story is more than just improving operations. Cognitive computing platforms such as IBM Watson can more accurately diagnose the care of rare patient systems by first reviewing millions of anonymized patient records. IBM Watson then can prescribe an individual treatment plan based on their well-founded diagnosis and calculations of the patient's genetic profile.
In all likelihood, the healthcare industry will continue to stretch our patience, wallets and sanity at times. But evidence is mounting that conditions are improving. Private sector firms are applying the powerful medicine of analytics to some of our thorniest problems and most promising breakthroughs.
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