There is a wealth of information in administrative claims data, but analyzing these data can be challenging.  While claims data are far from perfect, the quality of these data have improved significantly over the years.  Healthcare data warehouses typically store the diagnosis, procedure, provider, claimant and cost information for each individual service line of a claim, creating a very rich source of information, but to make these data useful, all of the disparate bits of information need to be aggregated into meaningful units of analysis.

There are several healthcare groupers and methodologies available for organizing and aggregating data to facilitate claims-based analytics.  Creating episodes of care is one grouping method.  Episode of care groupers are available from a variety of vendors and while each notes the unique features of their software, the general concept across all of them is the same.  The purpose of an episode of care grouper is to link together all of the claims that pertain to the treatment of a particular condition for a particular patient.  For example, if a patient fractures her leg, she may go to the emergency room and receive multiple treatments, receive prescriptions for pain medication, have follow-up visits with an orthopedist with additional X-rays and perhaps receive physical therapy treatments.  An episode of care grouper will combine all of the individual claims from the different providers, so the full cost of the broken leg episode can be assessed.

Episodes of care provide a unit of analysis that can be used to compare healthcare cost and utilization for similar conditions across various networks, health plans, geographic regions and more.  When differences in cost are noted at the episode level, analytic drill-downs into the detailed claim records grouped to those episodes will reveal what is driving the differences and highlight opportunities for improved efficiency.

In the following example we demonstrate how episodes can be used to analyze the differences in costs between two networks for a fairly common condition, chronic sinusitis.  For this example, we will focus on only the least severe episodes in this category (severity level 1) and remove all high and low cost outliers.

(EPISODES OF CARE IMAGE)

From these results we can see that the average allowable charge per episode is almost 13% higher in Network 2.  Network 1 is spending about 20% more on prescription drugs to treat this condition, but Network 2 is spending more for outpatient facility and professional services.

We can further analyze the cost difference using the Milliman Health Cost Guidelines (HCGs).  By drilling into the HCGs we see that the differences in outpatient facility charges are driven in large part by higher expenditures for surgery and the higher professional charges are related to greater higher expenditures on allergy testing and immunotherapy.  There is also more spending on physician office CAT Scans.

(EPISODES OF CARE IMAGE)

Given the fact that we focused only on the least severe chronic sinusitis episodes, it may be appropriate to further evaluate the treatment patterns of the providers in Network 2 to determine if utilization rates for surgery, allergy testing/treatments and CAT scans are appropriate.