OBJECTIVE: To determine whether health maintenance organizations (HMOs) attract enrollees who use relatively few medical resources and whether a simple risk-adjustment system could mitigate or eliminate the inefficiency associated with risk selection. DATA SOURCES: The first and second rounds of the Community Tracking Study Household Survey (CTSHS), a national panel data set of households in 60 different markets in the United States. STUDY DESIGN: We use regression analysis to examine medical expenditures in the first round of the survey between enrollees who switched plan types (i.e., from a non-HMO plan to an HMO plan, or vice versa) between the first and second rounds of the survey versus enrollees who remained in their original plan. The dependent variable is an enrollee's medical resource use, measured in dollars, and the independent variables include gender, age, self-reported health status, and other demographic variables. DATA COLLECTION METHODS: We restrict our analysis to the 6,235 non-elderly persons who were surveyed in both rounds of the CTSHS, received health insurance from their employer or the employer of a household member in both years of the survey, and were offered a choice of an HMO and a non-HMO plan in both years. PRINCIPAL FINDINGS: We find that people who switched from a non-HMO to an HMO plan used 11 percent fewer medical services in the period prior to switching than people who remained in a non-HMO plan, and that this relatively low use persisted once they enrolled in an HMO. Furthermore, people who switched from an HMO to a non-HMO plan used 18 percent more medical services in the period prior to switching than those who remained in an HMO plan. CONCLUSIONS: HMOs are experiencing favorable risk selection and would most likely continue to do so even if employers adjusted health plan payments based on enrollees' gender and age because the selection is based on enrollee characteristics that are difficult to observe, such as preferences for medical care and health status.
OBJECTIVE: To determine whether health maintenance organizations (HMOs) attract enrollees who use relatively few medical resources and whether a simple risk-adjustment system could mitigate or eliminate the inefficiency associated with risk selection. DATA SOURCES: The first and second rounds of the Community Tracking Study Household Survey (CTSHS), a national panel data set of households in 60 different markets in the United States. STUDY DESIGN: We use regression analysis to examine medical expenditures in the first round of the survey between enrollees who switched plan types (i.e., from a non-HMO plan to an HMO plan, or vice versa) between the first and second rounds of the survey versus enrollees who remained in their original plan. The dependent variable is an enrollee's medical resource use, measured in dollars, and the independent variables include gender, age, self-reported health status, and other demographic variables. DATA COLLECTION METHODS: We restrict our analysis to the 6,235 non-elderly persons who were surveyed in both rounds of the CTSHS, received health insurance from their employer or the employer of a household member in both years of the survey, and were offered a choice of an HMO and a non-HMO plan in both years. PRINCIPAL FINDINGS: We find that people who switched from a non-HMO to an HMO plan used 11 percent fewer medical services in the period prior to switching than people who remained in a non-HMO plan, and that this relatively low use persisted once they enrolled in an HMO. Furthermore, people who switched from an HMO to a non-HMO plan used 18 percent more medical services in the period prior to switching than those who remained in an HMO plan. CONCLUSIONS: HMOs are experiencing favorable risk selection and would most likely continue to do so even if employers adjusted health plan payments based on enrollees' gender and age because the selection is based on enrollee characteristics that are difficult to observe, such as preferences for medical care and health status.
Authors: P Kemper; D Blumenthal; J M Corrigan; P J Cunningham; S M Felt; J M Grossman; L T Kohn; C E Metcalf; R F St Peter; R C Strouse; P B Ginsburg Journal: Inquiry Date: 1996 Impact factor: 1.730
Authors: Colleen L Barry; Mark R Cullen; Deron Galusha; Martin D Slade; Susan H Busch Journal: Health Aff (Millwood) Date: 2008 Nov-Dec Impact factor: 6.301
Authors: Corinne Chmiel; Oliver Reich; Andri Signorell; Stefan Neuner-Jehle; Thomas Rosemann; Oliver Senn Journal: BMJ Open Date: 2018-11-25 Impact factor: 2.692