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The American Journal of Managed Care May 2014
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Patient-Centered Medical Home Features and Expenditures by Medicare Beneficiaries

Erica L. Stockbridge, MA; Lindsey M. Philpot, PhD, MPH; and José A. Pagán, PhD
Analysis of the impact of individual features of the patient-centered medical home care model on future healthcare expenditures among Medicare beneficiaries.
Objectives

To determine the impact of individual features of the patientcentered medical home (PCMH) care model on next-year healthcare expenditures including outpatient, inpatient, emergency department, pharmacy, and total healthcare expenditures among Medicare beneficiaries 65 years and older.

Study Design

Analysis of retrospective longitudinal survey data. Methods Longitudinal files from the Medical Expenditure Panel Survey were analyzed. Differences in expenditures for individuals whose usual sources of care did or did not have different PCMH features were estimated using recycled predictions from generalized linear regression models.

Results

Having little to no difficulty contacting the regular source of care over the telephone during regular business hours was associated with significantly lower total and inpatient expenditures over the next year (differences of $2867 and $3736, respectively). Having a regular source of care with office hours at night or on weekends was associated with significantly lower outpatient, emergency department, and other expenditures (differences of $535, $103, and $328, respectively). Pharmacy expenditures were significantly higher for individuals whose usual source of care inquired about medications and treatments prescribed by other doctors (difference of $362).

Conclusions

This study points out the need to identify how individual PCMH features impact healthcare expenditures across different policy-relevant categories. Practices that have not fully adopted a PCMH model can still make progress in improving quality and controlling costs by adopting even some modest features of the PCMH model.

Am J Manag Care. 2014;20(5):379-385
  • Features of the patient-centered medical home (PCMH) care model individually influence future healthcare expenditures among Medicare beneficiaries.
  • Total, inpatient, outpatient, emergency department, pharmacy, and other expenditures were differentially affected by 1 or more individual features of PCMH care.
  • This study points out the need to identify how individual PCMH features impact healthcare expenditures across different policy-relevant categories.
  • Results revealed that practices that have not fully adopted a PCMH model can still make progress in improving healthcare quality while reducing or controlling costs by adopting even some modest features of the PCMH model.
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.

METHODS

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.

Outcome Variables

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).

Other Measures

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.

Statistical Analyses

We first explored the characteristics of the population and investigated how the potential confounding variables differed across the different features of a PCMH. χ² tests were used to assess the significance of unadjusted differences between the categories in each of the PCMH features. Next, we conducted analyses of second-year expenditures associated with each of the PCMH features. As the second-year expenditures for Panels 12, 13, and 14 represented expenditures in 2008, 2009, and 2010, respectively, the expenditures for Panels 12 and 13 were adjusted for inflation to 2010 levels of expenditures using the Consumer Price Index for Medical Care Services.16 Then, for each of the expenditure categories, we calculated the unadjusted and adjusted predicted expenditures associated with each of the PCMH features using generalized linear regression models with a gamma distribution and a loglink function. This statistical approach accounted for the skewed nature of cost data.16 Unadjusted models included only a single PCMH feature as a predictor, while adjusted models included all PCMH features and all potential confounders described previously.

The average differences in costs for individuals who had usual sources of care with and without each of the PCMH features were determined by predicting healthcare expenditures using the estimated coefficients from the generalized linear regression equations. This yielded predictions for each individual based on the typical expenditures for individuals with similar characteristics, which were estimated using the method of recycled predictions.16 We then calculated the differences in these average expenditures. All statistical analyses took into account the complex survey design of MEPS and were conducted in Stata MP version 12.1 (StataCorp LP, College Station, Texas).17

RESULTS

Table 1
describes the percentage of the study population (N = 2387) by the reported PCMH features. The most common PCMH feature was always or usually asking patients to help decide between treatments, with 85.4% of respondents reporting this feature (95% confidence interval [CI], 83.1-87.6). The least common PCMH feature was having office hours on nights or weekends, with 29.6% of respondents reporting this feature (95% CI, 26.4-32.7). The reported frequency of the other features are provided in Table 1, and information about how the PCMH features vary by sociodemographic and health status variables is provided in eAppendix A.

 
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