Patient-Centered Medical Home Features and Expenditures by Medicare Beneficiaries
Published Online: May 20, 2014
Erica L. Stockbridge, MA; Lindsey M. Philpot, PhD, MPH; and José A. Pagán, PhD
Patient-centered medical homes (PCMHs) are showing promise as a novel way to improve healthcare quality while keeping healthcare cost growth under control.1 Through coordinated, team-based approaches to healthcare delivery that are tailored to address the needs of individual patients via enhanced communication, PCMHs shift the focus of healthcare delivery from the system level to the patient level.1 PCMH models have been implemented in single healthcare systems, and studies of these interventions and demonstrations have focused on implementation costs, patient experiences, evidence-based care processes, specific health outcomes, and healthcare utilization and costs. Published studies indicate that PCMHs are associated with small improvements in overall patient satisfaction with care and reported satisfaction with care coordination and communication,1-4 as well as moderate enhancements to clinical care delivery and processes, primarily for preventive services.1,4 There is also some evidence of potential associations of PCMHs with improved glycated hemoglobin and low-density lipoprotein values,5 as well as decreased short-term mortality rates among older adults.6
Studies to date indicate small improvements in inpatient and emergency department utilization among patients engaged with PCMHs,7-9 but none show significant cost savings associated with PCMHs.1 However, from health policy or managed care perspectives (eg, a third-party payer or an accountable care organization), it is unclear how PCMHs impact healthcare expenditures across different levels of care (eg, outpatient care, emergency department [ED] or inpatient care).
All PCMHs deliver care by combining a set of different features, components, or services that complement each other, with the goals of enhancing care delivery and communication. For example, the National Committee for Quality Assurance’s (NCQA's) Physician Practice Connections–Patient- Centered Medical Homes recognition program includes 9 standards addressing areas such as access and communication, referral tracking, and performance reporting and improvement.10,11 Another example is the Comprehensive Primary Care (CPC) initiative by the Centers for Medicare & Medicaid Services (CMS).12 Under the CPC initiative, the PCMH model is augmented by multipayer payment reform (eg, by offering bonus payments to doctors who improve care coordination), total cost accountability in the form of shared savings, and the requirement that the 500 participating primary care practices (serving 313,000 Medicare beneficiaries) use electronic health records (EHRs) to better coordinate care.13
Little is known about the role of different individual components that define a PCMH on explaining variation in future healthcare expenditures. This is important because the costs of implementing a PCMH model in a medical practice are nontrivial and, as a result, medical practices have to decide which elements should be implemented first. In the context of managed care, it is also important to know which PCMH features disrupt all the different categories of healthcare expenditures—which will impact profits, particularly under shared savings arrangements. Although CMS is in the process of testing Medicare PCMH models,14 there is currently a lack of PCMH research on the Medicare population. In this study, we use data from the Medical Expenditure Panel Survey (MEPS) to determine the impact of individual features of the PCMH model on different levels of future healthcare expenditures, including outpatient, inpatient, ED, pharmacy, and total healthcare costs among Medicare beneficiaries 65 years and older.
Data Source and Study Sample
The Household Component of the MEPS was the data source for this study. The Agency for Healthcare Research and Quality administers the MEPS, collecting in-depth information about annual healthcare utilization, medical expenditures and health conditions from a sample of households in the United States. MEPS employs an overlapping panel design; a new panel of sample households is selected each year and then tracked over the 2-year period. The 3 most recent MEPS Longitudinal Data Files were utilized, which included Panels 12, 13, and 14, interviewed over the years 2007 to 2010.
The study sample included adults who were 65 years of age or older, indicated that they were enrolled in Medicare, and reported that they had a usual source of care other than the ED. Analyses were limited to adults who were not missing data on variables of interest. As a result, 2387 individuals qualified for all analyses (54.8% of the study sample). The most common questions with missing data were those addressing PCMH features (eg, 1920 of the 1970 respondents with missing data did not include information on 1 or more of the questions used to identify PCMH features). The sample size was sufficient for determining statistical significance.
Expenditure variables describing the total of payments for care during the second year of each 2-year panel in total, and by health service category, were the outcome variables of interest. These expenditure variables were based on the sum of expenditures during the year from all payment sources, including out-of-pocket payments and payments by third-party payers. The health services expenditure categories included in the current analysis were outpatient (including office-based and hospital outpatient visits), inpatient, ED, prescription medication, and other (including dental care, home healthcare, vision aids, and other medical supplies and equipment).
Primary Independent Variables
Five variables from second-round interviews describing the features of a PCMH as described by Beal and colleagues15 were the primary independent variables for this study. These questions were worded as follows: (1) “How difficult is it to contact (a medical person at) (PROVIDER) during regular business hours over the telephone about a health problem?”; (2) “Does (PROVIDER) have office hours at night or on weekends?”; (3) “How difficult is it to contact (a medical person at) (PROVIDER) after their regular hours in case of urgent medical needs?”; (4) “Does (someone at) (PROVIDER) usually ask about prescription medications and treatments other doctors may give them?”; and (5) “If there were a choice between treatments, how often would (a medical person at) (PROVIDER) ask (you/name) to help make the decision?”
Responses of “yes,” “no,” “refused,” and “don’t know” were analyzed such that “yes” was coded as a positive response (1), “no” was coded as negative (0), and “refused” and “don’t know” were coded as missing. Questions asking about difficulties were dichotomized, with “not too difficult” and “not at all difficult” coded as positive (1), “refused” and “don’t know” coded as missing, and the remaining responses coded as negative response (0). The frequency question was also dichotomized, with “usually” and “always” coded as positive (1), “refused” and “don’t know” coded as missing, and the remaining responses coded as negative (0).
The Beal study, which defined the elements of a PCMH for the current study, also included 3 other variables that help define PCMHs.15 These 3 variables looked at whether individuals visited their regular source of care for new problems, preventive care, and ongoing health problems. They were not included in the current study because most of the responses for these 3 variables were positive (98.8%, 98.7%, and 98.0%, respectively).
Additional variables were incorporated to describe the population and adjust for potential confounders of the relationship between PCMH features and healthcare expenditures. With the exception of race/ethnicity (which is assessed during the initial round) and household income (which is based on income for the first year of the panel), these additional variables were based on the responses to questions asked during second-round interviews. These variables included age, race/ethnicity, region, marital status, poverty status, health insurance coverage, activities of daily living (ADLs) and instrumental activities of daily living (IADLs) limitations, chronic health conditions, and perceived health status). Categorizations for these variables are described in eAppendix A.
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