The effect of complementary and alternative medicine claims on risk adjustment

Published: December 1, 2006
Category: Bibliography > Papers
Authors: Abrams C, Diehr PK, Grembowski DE, Lafferty WE, Lind BK
Countries: United States
Language: null
Types: Care Management
Settings: Academic

Med Care 44:1078-1084.

Department of Nursing, Boise State University, Boise, ID, USA

OBJECTIVE: We sought to assess how the inclusion of claims from complementary and alternative medicine (CAM) providers affects measures of morbidity burden and expectations of health care resource use for insured patients.

METHODS: Claims data from Washington State were used to create 2 versions of a case-mix index. One version included claims from all provider types; the second version omitted claims from CAM providers who are covered under commercial insurance. Expected resource use was also calculated. The distribution of expected and actual resource use was then compared for the 2 indices.

RESULTS: Inclusion of claims from CAM providers shifted 19,650 (32%) CAM users into higher morbidity categories. When morbidity categories were defined using claims from all providers, CAM users in the highest morbidity category had average (+/-SD) annual expenditures of $6661 (+/-$13,863). This was less than those in the highest morbidity category when CAM provider claims were not included in the index ($8562 +/- $16,354), and was also lower than the highest morbidity patients who did not use any CAM services ($8419 +/- $18,885).

CONCLUSIONS: Inclusion of services from CAM providers under third-party payment increases risk scores for their patients but expectations of costs for this group are lower than expected had costs been estimated based only on services from traditional providers. Risk adjustment indices may need recalibration when adding services from provider groups not included in the development of the index.

PMID: 17122711

Practice Patterns Comparison,High Risk,Medication,United States,Adolescent,Adult,Gender,Health Expenditures/statistics & numerical data,Insurance,Health,Reimbursement/economics,Insurance,Health,Reimbursement/statistics & numerical data,Middle Aged,Washington,United States

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