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The American Journal of Managed Care November 2012
Automated Telephone Calls to Enhance Colorectal Cancer Screening: Economic Analysis
David H. Smith, RPh, PhD; Adrianne C. Feldstein, MD, MS; Nancy Perrin, PhD; A. Gabriela Rosales, MS; David M. Mosen, PhD, MPH; Elizabeth G. Liles, MD; Jennifer L. Schneider, MPH; Jennifer E. Lafata, P
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Katrina A. B. Goddard, PhD; Erin J. Aiello Bowles, MPH; Heather Spencer Feigelson, PhD, MPH; Laurel A. Habel, PhD; Sharon Hensley Alford, PhD; Catherine A. McCarty, PhD, MPH; Larissa Nekhlyudov, MD, M
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Patient-Centered Medical Home Cost Reductions Limited to Complex Patients
Thomas J. Flottemesch, PhD; Louise H. Anderson, PhD; Leif I. Solberg, MD; Patricia Fontaine, MD, MS; and Stephen E. Asche, MA
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Patient-Centered Medical Home Cost Reductions Limited to Complex Patients

Thomas J. Flottemesch, PhD; Louise H. Anderson, PhD; Leif I. Solberg, MD; Patricia Fontaine, MD, MS; and Stephen E. Asche, MA
From 2005 to 2009, improved clinical practice systems were associated with cost reductions only for medically complex patients.
Objectives: To examine the long-term relationships between costs, utilization, and patient-centered medical home (PCMH) clinical practice systems.

Study Design: Clinical practice systems were evaluated at baseline by the Physician Practice Connections-Research Survey (PPC-RS). Annual costs and utilization of a retrospectively constructed cohort of 58,391 persons receiving primary care at 1 of 22 medical groups over a 5-year period (2005-2009) were compared.

Methods: Multivariate regressions adjusting for patient demographics, health status, and autoregressive errors compared PPC-RS scores and study outcomes for the entire cohort and 3 subcohorts defined by medical complexity (medication count 0-2 [n = 29,657], 2-6 [n = 19,505], >7 [n = 9229]). Outcomes (adjusted to 2005 dollars) were total costs, outpatient costs, inpatient costs, inpatient days, and emergency department (ED) use.

Results: For the entire cohort, a 10% increase in PPC-RS scores was associated with 3.9 (medication count: 0-2), 6 (3-6), and 11.6 (>7) fewer ED visits per 1000 in 2005; and 5.1, 7.6, and 13.6 fewer ED visits in 2009. That 10% increase was not associated with the 0-2 medication subcohort’s total (−$22/person in 2005; $184/person in 2009), outpatient (−$11/person in 2005; $42/person in 2009), or inpatient ($26/person in 2005; $29/person in 2009) costs. However, it was associated with significantly decreased total (−$446/person in 2005; −$184/person in 2009) and outpatient (−$241/person in 2005; −$54/person in 2009) costs for the most medically complex subcohort (>7 medications).

Conclusions: Association of PCMH clinical practice systems with reduced costs appears limited to the most medically complex patients.

(Am J Manag Care. 2012;18(11):677-686)
Patient-centered medical home (PCMH) clinical practice systems have been associated with quality improvement and short-term (12-24 months) reductions in patient medical costs and utilization. Using a retrospectively constructed longitudinal cohort, we examined the association between PCMH clinical practice systems and annual medical costs and utilization over a 5-year period.

  • Improved PCMH clinical practice systems were associated with reduced emergency department utilization for all patients.

  • Higher-functioning baseline PCMH clinical practice systems were associated with lower total and outpatient costs for medically complex patients (ie, 7 or more active prescriptions) over 5 years.
The patient-centered medical home (PCMH) is a topic of interest.1-15 A high-functioning medical home requires coordinated care by a consistent team.16-21 Although few clinics are PCMHs, adults reporting a usual source of primary care are 25% more likely to report positive clinician attributes22 and reduced disparities.23 Observational studies suggest the PCMH approach results in improved satisfaction24 and reduced utilization.25 Pilot studies have found reduced emergency department (ED) use26 and cost reductions.27-31 Clinical practice systems are an important component of a PCMH.32,33 They give access to relevant information, coordinate management of complex conditions, and facilitate delivery of preventive care services. The Physician Practice Connections–Patient-Centered Medical Home (PPC-PCMH), a tool endorsed by the National Committee for Quality Assurance (NCQA), measures practice systems and has been used in PCMH programs.34-38

Prior studies of utilization26,39 have looked at short time frames of 12 to 24 months.16 In theory, the PCMH would reduce medical costs over time by avoiding complications leading to ED visits and inpatient stays, especially among patients with complex illness, as suggested by the chronic care model of Bodenheimer et al.40 This study uses a 2005 measure of the PPC-PCMH and a retrospectively constructed cohort from a large Midwestern health plan to evaluate whether clinical practice systems evaluated at baseline are associated with reduced utilization and costs over a subsequent 5-year period. We present key findings in terms of predicted annual per person amounts to illustrate (1) how predicted costs and utilization change in response to clinical systems and (2) how baseline clinic systems related to different patient groups.


Data Sources, Study Population, and Primary Care Medical Groups

Utilization, billing, provider, medical group, and patient demographic data came from the administrative databases of a large, not-for-profit Midwestern health plan. Practice system measures came from a 2005 survey of medical group directors using the Physician Practice Connections-Research Survey (PPC-RS).41 This instrument is similar to the PPC-PCMH except for fewer questions about the electronic medical record, a focus on 4 chronic conditions (diabetes, cardiovascular disease, asthma, and depression), and graded response categories. A full description of the tool is available on request.

A retrospective cohort over 2005 to 2009 was constructed to compare a baseline measure of clinic systems with subsequent utilization patterns. Subjects needed to meet the following inclusion criteria: (1) have 10 or more months of continuous enrollment in each year; (2) be alive on December 31, 2009; (3) be 19 years or older as of January 1, 2005; and (4) be attributable to the same primary care medical group for 2005 to 2009. Persons were attributed to the medical group with which they had the greatest percentage of primary care visits. Primary care visits were defined as visits with providers in the following specialties: family medicine, internal medicine, general practice, geriatric medicine, and obstetrics and gynecology (Ob-Gyn). Nurse practitioner and physician assistant visits were included. Our decision to include visits with Ob-Gyn providers was made because such visits are a regular source of care for many women of childbearing age. However, such an inclusion does not strictly conform to the personfocused primary care concept of Starfield.42 The implications of this decision are discussed in the Limitations section.

Those with no primary care visits were unattributed and excluded. Those attributed to more than 1 medical group (ie, those who had an equal number of visits to 2 or more groups) were also excluded. We further limited the sample to medical groups with a minimum of 200 attributed members. Per year, of the 318,857 adults attributed in 2005 approximately 5% were excluded due to death or disenrollment, 25% due to a change in attributed medical group, and 5% due to no primary care utilization. This resulted in 58,391 persons across 22 medical groups. Most (n = 48,292) had commercial insurance, 7077 were Medicare enrollees, and 3022 were enrolled in Medicaid. For dually eligible Medicare beneficiaries aged 65 to 75 years, all claims including pharmacy were processed by the health plan in order to track benefits, deductibles, and payer liability. All claims from Medicaid beneficiaries were processed for similar reasons.

Five annualized outcomes were constructed: total cost, total outpatient cost, total inpatient cost, inpatient days, and ED visits. The health plan’s administrative databases contain information concerning insurance product, medical diagnosis, care specialty, costs, and limited demographics (age, address, and sex). These were organized using Evaluation and Management, International Classification of Diseases, Ninth Revision, and Current Procedural Terminology (CPT) codes. Total cost included all reimbursed medical costs, including copays, coinsurance, and deductibles. Outpatient cost included professional services, prescriptions, lab and x-ray tests, and outpatient surgical procedures. Inpatient cost included professional and facility fees for hospital-based services including emergency care. Inpatient days were days with an overnight hospital stay. ED visits included all visits to an ED with reimbursed service. If a subject was not enrolled for the entire year, their cost and utilization was annualized using their monthly average.

To avoid variation in outcomes due to benefit design or provider contract, total, outpatient, and inpatient costs were based on a standardized measure, the relative resource value unit. Relative resource value units are based on Centers for Medicare & Medicaid Services relative value units, inpatient diagnosis-related groups (DRGs), and Ambulatory Payment Classification weights. The logic is to apply a standardized fee schedule across all providers by developing standardized costs for each CPT code, hospital DRG, and National Drug Classification (NDC) code that is dependent upon the type of procedure/service/prescription provided but independent of the place of service, type of insurance coverage, or year. This fee schedule was developed by constructing a weighted average of billed amounts across all contracted providers for each CPT code, hospital DRG, and NDC code. Our measures of costs were developed by adjusting these averages by the ratio of billed to paid amounts across service category and scaled to the base year of 2005.

PPC-RS Survey

The PPC-RS41 asks 53 questions related to delivering preventive services, depression, diabetes, cardiovascular disease, and asthma. Of these, 43 are grouped into domains corresponding to the Chronic Care Model: Health Care Organization (n = 3), Delivery System Redesign (n = 8), Clinical Information System (n = 10), Decision Support (n = 9), and Self-Management Support (n = 23). Items are coded as present and work well (1 point), present but need improvement (1/2 point), or absent (0 points). Domain scores represent the proportion of possible items present and utilized. The PPCRS score is a summation of all items with high scores associated with higher-functioning clinical systems.

Plan of Analysis

Multiple regression models were estimated using generalized estimating equations for continuous outcomes and generalized linear models for discrete outcomes. All models were fit with a subject-level autoregressive error structure (AR1 process). Cost outcomes were log transformed43-46 and Duan’s smearing estimator was used.47,48 A majority had neither inpatient nor ED visits within a given year. Thus, 2 outcomes were considered. First, the likelihood of any utilization was modeled using a logistic regression with a subject-level AR1 process. Among those with inpatient costs, a log-transformation was used. For ED utilization and inpatient days, a zero-inflated Poisson model was used.

Our models compared a baseline measure of clinical systems with costs and utilization over a 5-year period. They adjusted for demographics (age and sex), complexity/comorbidity (number of medications and comorbidities), insurance type, and primary care visits. Outpatient prescription medications were our measure of complexity because this information was reliably available from claims data, and it is a validated, easily reproducible measure.49 Certain results categorized subjects by their baseline number of prescriptions, but models allowed that number to vary by year. All of the models were developed in the following manner. First, candidate covariance structures were considered. Second, demographic models were constructed. Covariates significant at the 10% level in univariate models were screened for confounding, multicolinearity, and consistent linear relationships. Appropriate adjustments (transformations, interactions, and polynomial terms) were made. Study year was considered as both a continuous and fixed effect with a fixed-effect specification preferable (likelihood ratio test; P = .0002). Finally, PPC-RS scores were added, and the possibility of interactions with both study year and patient demographics was considered.

To control for variation in outcomes due to differences in patient mix when presenting our results, we used a 2-part strategy. First, as discussed, we incorporated multiple patientlevel factors adjusting for patient demographics (age and sex), ability to pay (insurance type), and medical complexity (prescription drug use and comorbidities) into our final multivariate models. Second, we estimated the impact of changes in PCMH-related systems (PPC-RS scores) holding fixed patient demographics.


Table 1 contains demographic information and average per patient costs. Total costs averaged $10,347 (standard deviation [SD] $22,384) in 2005 and trended upward to $13,637 (SD $32,905) in 2009. The average 2005 age was 52.3 years with 64% being female, 13% using Medicare, and 1.8% using Medicaid. Over the study, an additional 6% enrolled in Medicare; however, Medicaid enrollment was stable.

Depression was the most prevalent chronic condition, impacting 24% in 2005 and 27% in 2009. The next most prevalent was asthma (10% in 2005, 9% in 2009), then diabetes (10% in 2005, 14% in 2009) and coronary artery disease (5% in 2005, 5.5% in 2009). Patients averaged 3.5 prescriptions in 2005 and 4.2 prescriptions in 2009. A brief discussion of demographics and specific coefficient estimates is in the eAppendix (available at

Tables 2 to 5 contain predicted amounts from our multivariate models. These predict per person costs (Tables 3 and 5) or utilization (Table 4), adjusting for patient demographics and comorbidities. They illustrate (1) how predicted costs and utilization change according to changes in baseline clinical systems (Table 3) and (2) how a hypothetical 10% improvement in baseline clinical systems potentially affects different groups differently.

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