Cohorts: Using More Meaningful Data to Drive Less Variation in Care

Healthcare systems across the country rely on data to help solve one of the largest challenges they face — how to improve the value of the care they deliver to patients by reducing cost while improving outcomes.

Data, however, is not a magic wand, and traditional ways of looking at data can result in analyses with a lot of “noise”. Successfully using data starts by making it meaningful and credible to the audience Enter the “cohort.”

A New Approach to Reducing Clinical Variation

In 2012, Utah-based Intermountain Healthcare set out to reduce unwarranted clinical variation — the largest cost driver for its surgical cases. A team of operating room nurses, analysts, and data engineers devised a revolutionary approach to grouping data in a more clinically meaningful way — the cohort.

Traditional cost and outcome analytics, which were based on basic procedure codes, were too broad and failed to give real insight into the reasons for clinical variation. Surgeons would justifiably complain that their patients were “sicker and more complicated” than the data suggested, and therefore the benchmark metrics were not appropriate.

The team at Intermountain worked collaboratively with the surgeons to dig deep into the data to create cohorts – more robust, meaningful data groupings that resulted in true “apples to apples” comparisons among surgical Cases.

Unlike previous coding driven analytics, the cohort concept accounts for a number of variables including patient type, surgical technique, equipment and supplies used, and secondary procedures performed during the case.

As a simple example, laparoscopic appendectomies — which had previously been coded identically — can be significantly different procedures, depending on if the appendix is ruptured or not. It wouldn’t be accurate to compare a ruptured case to an unruptured case, because the approach used for each is very different. By creating new categories of appendectomies that included “ruptured” and “unruptured” cohorts, the team was able to glean more meaningful insights from the data.

These unique cohort groupings make data not only more insightful — but more actionable as well. With the creation of cohorts, hospital and service line leadership are able to identify and analyze the precise clinical variation drivers within a given procedure.  With cohorts, surgical procedures can be meaningfully compared across surgeons and locations to quickly and easily find best practices and areas of opportunity.

At Intermountain, surgeons, and their clinical care teams, recognized the value of analyzing and acting upon the cohorted data.  Soon, a system-wide culture change began to take hold.. Surgeons shared best practices with each other and collaborated on ways to standardize those practices across the health system.

Automating the Process

Empiric Health built on the hard work of the Intermountain team, introducing new artificial intelligence (AI) and natural language processing (NLP) techniques to automate cohort assignment and facilitate data comparison, significantly reducing labor hours and increasing efficiency. This technology has resulted in the development of new cohorts and now serves as a key component of Empiric Health’s Approach.

A Proven Concept

As the culture change at Intermountain grew, so did the savings. Today, there are more than 300 cohorts for surgical procedures that have helped the health system save more than $90 million. As the analytics became more meaningful, physician engagement with the data grew and physician service line meetings saw a 150 percent increase in attendance.

Looking Ahead

Data is the key to improving the value equation in healthcare. More importantly, though, leaders and clinicians require clinically-relevant, accurate and actionable data that allows them to draw true insights.  Healthcare providers want to offer their patients the highest quality, and most economical care possible.

Empiric has partnered with Intermountain Healthcare to look at data in a brand new way.  By grouping information in clinically meaningful cohorts, Empiric has removed the “noise” and significantly shortened the feedback loop to allow clinicians to learn from their own data as well as that of their peers.

To learn more about Empiric Health and how our proprietary data cohorts are actually bending the cost curve in healthcare, visit