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A comparative analysis of claims-based methods of health risk assessment for commercial populations

Published: June 6, 2002
Category: Reports
Authors: Cameron BA, Cumming RB, Derrick B, Knutson D
Country: United States
Language: null
Type: Care Management
Settings: Academic, Health Plan

Minneapolis, MN, USA: Milliman USA and Park Nicollet Institute Health Research Center.

Milliman USA and Park Nicollet Institute Health Research Center, Minneapolis, MN, USA

The use of health risk assessment methods based on medical diagnosis codes from  administrative claim data continues to grow. The federal government has implemented  a process that uses medical diagnosis codes to adjust payments to Medicare+Choice contractors. Numerous states have implemented methods that use medical diagnosis codes to adjust payments to managed care plans for Medicaid enrollees. Diagnosis-based  methods of risk assessment have also been used by employers in analyzing how  employee contributions should vary by choice of provider or health plan. Health insurers are increasingly using, or are considering using, diagnosis or pharmacy-based methods  of risk assessment for provider profiling, case management, provider payment, and rating/underwriting.
There has also been a significant increase in the activity and interest in risk assessment methods that rely on pharmacy information from administrative claim data. A number of researchers have recently developed pharmacy-based risk assessment methods, and a number of others are planning to develop such methods. This is a reflection of the advantages of pharmacy data over medical diagnosis data. In general, the advantages are that pharmacy data is timelier, more complete, and less costly to collect and validate. At the same time, concerns have been raised regarding pharmacy-based risk assessment methods, including the ability to keep pace with rapid changes in drug technology and the ability to manipulate risk assessment scores if the methods are not sufficiently sensitive to gaming.
The strong interest and potential growth in “consumer-driven” health plans (e.g., defined contribution plans) may also increase the need for more accurate health risk assessment. Many of these health plans involve giving the employee more responsibility and more choice in benefit plan offerings. With increased choice comes the possibility of significant differences in health status among the pools of employees that select a given benefit plan option. Accordingly, it will be even more important to understand and quantify differences in health status when analyzing the cost efficiency of different health plans for the purpose of establishing employee contribution requirements. Adjusting for health status selection is also important in analyzing the impact of these new plans on the employer’s overall cost for health benefits.

Predictive Risk Modeling,Cost Burden Evaluation,Payment,United States

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