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Concurrent Risk Adjustment
Concurrent risk adjusters are distinct from prospective risk adjusters, which use one period’s
claim data to predict a future period’s claim costs. Concurrent risk adjusters, on the other hand,
use characteristics of one period’s claim data, such as diagnosis codes, to predict claim costs
for that same period.
Concurrent Risk Adjustment
A concurrent risk adjuster is not a true predictive model,
but rather an explanatory or benchmarking model, since
the actual costs at the end of any given time period are
already known. Concurrent risk adjusters have a
number of applications at present, and may have an
expanded role in the future under healthcare reform.
Milliman Advanced Risk Adjuster (MARA) concurrent
models deliver strong predictive power and offer features
not typically found in other risk adjustment models.
Uses of concurrent risk adjusters
Concurrent risk adjusters’ applications include
reimbursement arrangements, provider profiling, group
and employer profiling, as well as uses in disease and
care management outcome studies. For instance,
concurrent models can be used to quantify the health
status of patient panels when implementing provider
reimbursement arrangements.
Health care organizations often rank and profile
providers, and in order to adjust for the differing mix of
patients in the provider panel, concurrent risk adjusters
are used to create risk-neutral provider profiles (i.e., to
remove the effect of underlying patient panel health
status when evaluating physician financial performance).
January 2012
A similar approach is used in profiling employer groups
within a health plan.
In outcome studies (e.g., analysis of readmission rates)
the relative health status of the individuals in the study
needs to be accounted for to avoid reaching misleading
conclusions. Concurrent models are often used for this
purpose.
What makes MARA’s concurrent model different?
While the concurrent MARA model does produce a
single risk score that can be used for all of the purposes
mentioned above, it also outputs four component service
category risk scores - inpatient facility, outpatient facility,
professional and pharmaceutical services. The R2 at the
individual member level (the most commonly used
measure of model predictive accuracy) of the four
component risk scores and the overall MARA concurrent
model are presented in Table 1 for the comprehensive
model.1 These results indicate that the model is able to
explain the greatest amount of variation in prescription
drug costs, and the least amount of variation in the
outpatient facility costs. Overall, the model explains
more than 60% of the variation in member total claim
costs for a given time period (which is higher than other
leading risk adjusters).
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Table 1 - Concurrent Model R2
R2
Service Category
Inpatient Facility
41.7%
Outpatient Facility
35.4%
Professional Services
42.6%
Prescription Rx Services
71.7%
Total
62.5%
Service category risk scores: So what?
It is not surprising that different medical conditions often
give rise to different types of costs. For example, acne is
unlikely to result in an inpatient admission, but it could
result in significant physician and prescription drug
costs.
When used for provider profiling, MARA’s component
risk scores can add another layer of precision to the
analysis. The component risk scores allow the user to
reflect variations in the categories of services
contributing to the total costs (or overall risk score).
Often, effective care management will shift the
distribution of healthcare costs among service
categories, which should be considered when using
concurrent risk adjuster scores.
Concurrent risk scores can also be used to develop
provider efficiency metrics by comparing members’
actual service category-level claim costs to the cost
levels predicted by the risk score. This approach helps
illustrate (and better communicate) utilization
performance, and can be a valuable addition to
performance management reporting.
1
The comprehensive model uses information from both medical and pharmacy claims to predict costs, including diagnosis codes,
National Drug Codes (NDCs), and other information. Of course, it does not use the cost information on the claims in making its
predictions—otherwise, there would be no point in comparing actual claims to the benchmark.
This monthly newsletter is written by Ksenia Draaghtel to provide information on timely
topics pertaining to the development of risk selection and underwriting tools. Ksenia has
been with Milliman’s Denver Health practice since 2005. She manages the technical
development of the Milliman Advanced Risk Technologies. Her other areas of expertise
include predictive modeling and lifestyle based analytics. She has also worked
extensively with Long-Term Care products and healthcare reform modeling.
January 2012
Ksenia Draaghtel, A.S.A., M.A.A.A.
Associate Actuary
Direct: 303 299-9400
[email protected]
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