The Tipping Point for Desktop Analytics: A Watershed Moment in the History of Health Care

Germ theory in 1860. The Flexner Report of 1910.  Zombie immortality in 2012  There are only a few watershed moments like these in the history of U.S. health care and, after hearing the other speakers at the recent Star Ratings Congress in Las Vegas, the Disease Management Care Blog thinks it's found another one.

 It calls it "desktop analytics."

In its early health services research career, the DMCB's studies consisted of creating study protocols that included data collection and storage, very high end computing, statistical planning and a carefully contrived reporting format. The timeline typically spanned over several months, required high end computing, involved fussy Ph.D. level statisticians unaccustomed to exceeding customer expectations and ultimately having to convince a narrow, highly educated, and skeptical audience of the veracity of the DMCB's conclusions at a scientific meeting.

While that is still necessary in traditionally funded research studies, the story is now far different in mainstream health care and insurance settings.  Tapping electronic record or insurance claims data bases are now far easier. Statistical software packages are do-it-yourself and 'walk' users through the basics. Ph.D-level statisticians are unnecessary. Mainstream health workers have a working appreciation of measurement as well as trending and the folks inhabiting the C-suites use their in-house research conclusions in core business planning.  And it can all be done using desktops that cost a few hundred bucks.

At the Star Ratings Congress, the DMCB listened to speaker after speaker who presented highly polished insights about quality and cost that were developed thanks to in-house information systems and analytics resources that would have been unthinkable a decade ago. This advance in data management has enabled providers and payers to spot trends on a month-to-month basis, compare local performance to historical as well as national benchmarks and report outcomes to external agencies on a regular basis.  The research efficiency was astonishing.

It was also so taken for granted. It shouldn't be.  Compared to 10 years ago, the industry has gone from the wheel and fire to the internal combustion engine and automatic transmission.

The DMCB thinks its going to get better too.  While the electronic health record vendors have been notoriously inept at supporting data analytics, it's going to just be a matter of time until community-based providers can hit a function key on their keyboards and scan (for example) mammography rates by age, race, zip code and months since last visit.  Insurers will be able to project which enrollees with diabetes on three or more prescription drugs are least likely to take their medicines after controlling for co-pay and weather.

When we finally do figure out how to increase quality and reduce costs, it'll be because desktop analytics had finally reached the tipping point.

Coda: This has important implications for the Affordable Care Act's Coordinating Council for Comparative Effectiveness Research.  The Council may find that by the time a prospective CER study is complete that desktop analytics had already found the answer and the much of the industry had moved on.  Stay tuned.