Capitated Payments and the Delivery of Patient Education
Capitated Payments and the Delivery of Patient Education
We took the data for this study from the 2009 National Ambulatory Medical Care Survey, a nationally representative survey of office visits made to non-federally employed physicians in private offices and community health centers throughout the United States. The Centers for Disease Control and Prevention's National Center for Health Care Statistics conducts this survey annually, and it is exempt from approval by the Centers for Disease Control and Prevention's institutional review board.
The survey employs a multistage probability design that accounts for 3 stages of probability sampling—geographic primary sampling units, physician practices within the primary sampling units, and patient visits within the physician practices—to produce weighted national estimates of office visits. Office personnel use collection forms provided by the survey to obtain information about the visits, including patient demographics, medical conditions, treatments and medications ordered or provided at the time of the visit, as well as other office-related information, during a 1-week sampling period.
This study entailed a cross-sectional analysis of data collected from visits where the provider identified themselves as the patient's primary care provider. Among these visits, levels of capitation for the practice were collected via a variable within the dataset that featured 4 levels of capitation: <25%, 26% to 50%, 51% to 75%, and >75%. These levels of capitation were predetermined in the survey and a continuous measure of capitation was not available. Patient education was determined through a yes/no response to a question asking whether patient education was either provided or ordered at the time of the visit. Further information about the visit that was collected included the patient's age and sex, whether the patient had been seen before by the practice, the number of visits made by the patient to the practice within the past 12 months, the number of chronic conditions mentioned during the visit, the number of medications managed during the visit, and the expected source of payment for the visit. Ownership of the practice was a variable available within the dataset and included health maintenance organization (HMO) as an option. However, analysis of this variable found that <2% of the total number of sampled visits met this definition. More than 75% of all sampled visits were made to a practice that was owned by a physician or physician group. Therefore, it was not possible to stratify ownership type by levels of revenue capitation because of the small sample size.
The total sample size used for analyses was 9863 patient visits. Initially, we used univariate descriptive analyses to demonstrate a general characterization of the sample. We then conducted bivariate analyses using χ tests to determine significant differences in patient education across the different levels of practice capitation. Finally, we used a logistic regression model to test differences among the different levels of practice capitation while taking into account other characteristics of the visits (mentioned earlier). All analyses were conducted using SUDAAN software (RTI International, Research Triangle Park, NC; available at http://www.rti.org/sudaan/) to account for the complex sampling design of the survey and to produce national estimates.
Methods
We took the data for this study from the 2009 National Ambulatory Medical Care Survey, a nationally representative survey of office visits made to non-federally employed physicians in private offices and community health centers throughout the United States. The Centers for Disease Control and Prevention's National Center for Health Care Statistics conducts this survey annually, and it is exempt from approval by the Centers for Disease Control and Prevention's institutional review board.
The survey employs a multistage probability design that accounts for 3 stages of probability sampling—geographic primary sampling units, physician practices within the primary sampling units, and patient visits within the physician practices—to produce weighted national estimates of office visits. Office personnel use collection forms provided by the survey to obtain information about the visits, including patient demographics, medical conditions, treatments and medications ordered or provided at the time of the visit, as well as other office-related information, during a 1-week sampling period.
This study entailed a cross-sectional analysis of data collected from visits where the provider identified themselves as the patient's primary care provider. Among these visits, levels of capitation for the practice were collected via a variable within the dataset that featured 4 levels of capitation: <25%, 26% to 50%, 51% to 75%, and >75%. These levels of capitation were predetermined in the survey and a continuous measure of capitation was not available. Patient education was determined through a yes/no response to a question asking whether patient education was either provided or ordered at the time of the visit. Further information about the visit that was collected included the patient's age and sex, whether the patient had been seen before by the practice, the number of visits made by the patient to the practice within the past 12 months, the number of chronic conditions mentioned during the visit, the number of medications managed during the visit, and the expected source of payment for the visit. Ownership of the practice was a variable available within the dataset and included health maintenance organization (HMO) as an option. However, analysis of this variable found that <2% of the total number of sampled visits met this definition. More than 75% of all sampled visits were made to a practice that was owned by a physician or physician group. Therefore, it was not possible to stratify ownership type by levels of revenue capitation because of the small sample size.
The total sample size used for analyses was 9863 patient visits. Initially, we used univariate descriptive analyses to demonstrate a general characterization of the sample. We then conducted bivariate analyses using χ tests to determine significant differences in patient education across the different levels of practice capitation. Finally, we used a logistic regression model to test differences among the different levels of practice capitation while taking into account other characteristics of the visits (mentioned earlier). All analyses were conducted using SUDAAN software (RTI International, Research Triangle Park, NC; available at http://www.rti.org/sudaan/) to account for the complex sampling design of the survey and to produce national estimates.
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