Currently Viewing:
The American Journal of Managed Care September 2012
Asthma Expenditures in the United States Comparing 2004 to 2006 and 1996 to 1998
Matthew A. Rank, MD; Juliette T. Liesinger, BA; Jeanette Y. Ziegenfuss, PhD; Megan E. Branda, MS; Kaiser G. Lim, MD; Barbara P. Yawn, MD, MSc; James T. Li, MD, PhD; and Nilay D. Shah, PhD
Impact of Clinical Complexity on the Quality of Diabetes Care
LeChauncy D. Woodard, MD, MPH; Cassie R. Landrum, MPH; Tracy H. Urech, MPH; Degang Wang, PhD; Salim S. Virani, MD; and Laura A. Petersen, MD, MPH
Impact of a Managed Controlled-Opioid Prescription Monitoring Program on Care Coordination
Arsenio M. Gonzalez III, PharmD, RPh; and Andrew Kolbasovsky, PsyD, MBA
State-Level Projections of Cancer-Related Medical Care Costs: 2010 to 2020
Justin G. Trogdon, PhD; Florence K. L. Tangka, PhD; Donatus U. Ekwueme, PhD; Gery P. Guy Jr, PhD; Isaac Nwaise, PhD; and Diane Orenstein, PhD
Outpatient-Shopping Behavior and Survival Rates in Newly Diagnosed Cancer Patients
Shang-Jyh Chiou, DrPH; Shiow-Ing Wang, PhD; Chien-Hsiang Liu, PhD; and Chih-Liang Yaung, PhD
Currently Reading
Impact of Medical Homes on Quality, Healthcare Utilization, and Costs
Andrea DeVries, PhD; Chia-Hsuan Winnie Li, MS; Gayathri Sridhar, PhD; Jill Rubin Hummel, JD; Scott Breidbart, MD; and John J. Barron, PharmD
Medication Adherence and Medicare Expenditure Among Beneficiaries With Heart Failure
Ruth Lopert, MD, FAFPHM; J. Samantha Shoemaker, PhD; Amy Davidoff, PhD; Thomas Shaffer, MHS; Abdulla M. Abdulhalim, BSPharm; Jennifer Lloyd, MA; and Bruce Stuart, PhD
Medicare Part D and Potentially Inappropriate Medication Use in the Elderly
Julie M. Donohue, PhD; Zachary A. Marcum, PharmD, MS; Walid F. Gellad, MD, MPH; Judith R. Lave, PhD; Aiju Men, MS; and Joseph T. Hanlon, PharmD, MS
Frequency of and Harm Associated With Primary Care Safety Incidents
Katrin Gehring, PhD; David L.B. Schwappach, PhD, MPH; Markus Battaglia, MD, MPH; Roman Buff, MD; Felix Huber, MD; Peter Sauter, MBA; and Markus Wieser, MD
Outcomes Associated With Timing of Maintenance Treatment for COPD Exacerbation
Anand A. Dalal, PhD, MBA; Manan B. Shah, PharmD, PhD; Anna O. D'Souza, BPharm, PhD; Amol D. Dhamane, BPharm, MS; and Glenn D. Crater, MD
Antidepressant Medication Adherence via Interactive Voice Response Telephone Calls
Terri Castle, RN, MS; Michael A. Cunningham, MS; and Gary M. Marsh, PhD
Measuring Value for Low-Acuity Care Across Settings
Sofie Rahman Morgan, MD, MBA; Meaghan A. Smith, BS; Stephen R. Pitts, MD, MPH; Robert Shesser, MD, MPH; Lori Uscher-Pines, PhD, MSc; Michael J. Ward, MD, MBA; and Jesse M. Pines, MD, MBA, MSCE

Impact of Medical Homes on Quality, Healthcare Utilization, and Costs

Andrea DeVries, PhD; Chia-Hsuan Winnie Li, MS; Gayathri Sridhar, PhD; Jill Rubin Hummel, JD; Scott Breidbart, MD; and John J. Barron, PharmD
Patient-centered medical home practices provided better preventive care and disease management with less resource utilization than practices not pursuing PCMH status.
Objectives: To assess baseline quality metrics, healthcare utilization, and costs of commercially insured patients treated at practices participating in a patient-centered medical home (PCMH) pilot.

Study Design: Observational cohort study utilizing claims data for patients treated at PCMH and non-PCMH practices.

Methods: Data from Empire Blue Cross and Blue Shield, 1 of 14 plans in the HealthCore Integrated Research Database, were queried for patients identified based on visits to PCMH and non-PCMH practices during 2007-2008; outcome metrics were formulated from the baseline calendar year, 2009. Differences in healthcare utilization were determined with x2 and 2-sample t tests. Regression models were used to test differences in adjusted emergency department (ED) use, inpatient services, and costs.

Results: The study included 31,032 PCMH and 350,015 non-PCMH patients. Among PCMH-treated patients, diabetics had higher rates of glycated hemoglobin testing; cardiovascular disease patients had higher rates of testing and better low-density lipoprotein cholesterol control; imaging rates for low back pain were lower; among pediatric patients, inappropriate antibiotic use for nonspecific or viral respiratory infections was lower. PCMH-treated adults and children had 12% and 23% lower odds of hospitalization, and required 11% and 17% fewer ED services, respectively, than non-PCMH patients. Risk-adjusted total per member per month costs were 8.6% and 14.5% lower for PCMH-treated pediatric and adult patients, respectively (P <.01).

Conclusions: PCMH practices in this pilot were associated with better preventive health, higher levels of disease management, and lower resource utilization and costs in 2009 compared with practices not pursuing PCMH status.

(Am J Manag Care. 2012;18(9):534-544)
Baseline quality metrics, healthcare utilization, and costs for commercially insured patients treated at practices participating in a patient-centered medical home (PCMH) pilot were assessed.

  • Compared with patients treated in practices not pursuing PCMH status, patients treated within PCMH practices had equal or better care management; lower rates of high-cost diagnostic imaging procedures, emergency department visits, and hospitalizations; and lower resource utilization and costs.

  • Providers using the PCMH model may have an opportunity to offer enhanced patient care even during early transformation.
Amidst dwindling economic resources and shrinking healthcare budgets, patient-centered medical homes (PCMHs) are gaining traction as an innovative approach for improving healthcare quality while reducing costs.1-4 With solid emphasis on preventive care2 and primary care,5-7 the PCMH model seeks to improve the continuum of patient care8 and drive efficiency by reconfiguring the primary care system.2,4,9 First introduced for pediatric patients in the 1960s,10 the PCMH concept now enjoys more support because of its broader potential in chronic disease management11-14 and for the enhancement of the vital triple outcomes: quality, cost, and the experiences of patients, their families, and providers.5,15

The PCMH approach seeks to replace volume-based financial incentives for providers (eg, reimbursements tied to numbers of visits, laboratory tests, and procedures) with more coordinated care that targets better patient outcomes.11 Practices typically seek recognition from accredited entities such as the National Committee for Quality Assurance (NCQA),16 which requires the satisfaction of a number of well-defined “must-pass” requirements (Table 1).17,18 Two prominent practice models have emerged: consultative, in which practices engage external consultants to help them navigate the transformation period; and enhanced chronic care models, which pursue PCMH recognition by enhancing their core capabilities in caring for chronic illnesses.19

In a study of 7 PCMH demonstrations, Fields et al showed annual reductions in hospitalizations (6% to 40%) and emergency department (ED) visits (7.3% to 29%), suggesting improvements in quality, and total annual savings ranging from $71 to $640 per patient.20 The practices did not focus on chronic care exclusively, and one, the Colorado Medical Homes for Children, reported total mean annual savings of $169 for all patients versus $530 for patients with chronic conditions.20 Transformation to PCMH status, however, could be a long and complicated process, as Nutting et al observed in a study of the first national PCMH demonstration (36 practices) that spanned from June 2006 to May 2008.21 Apart from payment reforms, PCMH transformation entails numerous adjustments such as learning new or redesigned service delivery models, changed perceptions about patient-provider relationships, and the need for extended periods of external guidance, among others.21

Increasing evidence suggests that the PCMH model offers greater advantages to stakeholders than non-PCMH practices in treatment delivery, especially for chronic care. To date, however, much of the available data have been derived from pre-PCMH versus post-PCMH comparisons, which provide a limited picture of changes from baseline to full-fledged PCMH status.14,21-25 Only a few more than a third of the demonstration projects nationwide used non-PCMH controls in their comparisons,19 and in 7 studies evaluated by Fields et al, only 1 study used a control.20 A control population enhances the robustness and reliability of the results in comparison studies.26

The main objective of this study was to compare PCMH practices during their prerecognition phase with non-PCMH practices to assess important quality differences in healthcare delivery and costs that may already be evident during the transformative baseline period. This study, the first of its kind in a large commercially insured population, compared key elements of the vital triple outcomes for PCMH practices, with particular emphasis on quality, including appropriate prevention, screening, and care of common chronic conditions; laboratory evaluations; and cost improvements.


This study compared patients treated within primary care practices classified as PCMH and non-PCMH, and located within the same geographic region. All practices (both PCMH [10] and non-PCMH [202]) were located within the 5 boroughs of New York City and its suburbs in Nassau County, Suffolk County, and Westchester County, were part of the Empire Blue Cross and Blue Shield (BCBS) network, and received payment according to existing negotiated fee schedules. The 10 practices in the PCMH category achieved NCQA recognition in 2010. The PCMH practices employed 247 physicians at 86 different locations. The 202 non-PCMH practices operated from 898 different sites and employed a total of 4048 physicians; non- PCMH practice sizes ranged from 1 to 250 providers.

Data Source

Data on the patients treated by the practices were drawn from the HealthCore Integrated Research Database, which contains medical and pharmacy claims from 14 commercial healthcare plans in the northeastern, southeastern, mid-Atlantic, Midwestern, and western regions of the United States. Data for 2007 through 2009 were selected for 1 northeastern managed care plan, Empire BCBS, relative to its PCMH initiative that started in August 2010. Data handling in this observational retrospective study complied with the Health Insurance Portability and Accountability Act of 1996.

Study Design

This study included administrative claims data from 2 periods: (1) the identification period of 2007 to 2008 and (2) the measurement period of calendar year 2009. Administrative claims for calendar years 2007 and 2008 were used for patient-provider attribution; a patient was attributed to the single provider with whom the patient had the highest number of office visits based on the patient’s medical records in 2007 and 2008. Patients attributable to multiple providers were assigned to the provider visited most recently. Patients were then classified as PCMH or non-PCMH according to the PCMH recognition status as of 2010 for the provider to whom they were attributed. Members who had at least 2 visits to a practice that had not achieved PCMH recognition in 2010 were assigned to the non-PCMH control group. Data in the second period, calendar year 2009, were used to examine patients’ outcome metrics.

Inclusion/Exclusion Criteria

The study’s inclusion criteria required that the patients be treated at primary care practices located within specific zip codes and that the practices participate in the BCBS network. There were no age restrictions on patients, but members who were older than 65 years at baseline (January 1, 2009) were excluded from the analysis to avoid any service complications or potential missing expenditures associated with Medicare eligibility. Members without continuous eligibility during calendar year 2009 were also excluded from the analysis to avoid any incompleteness in the data set.

Outcome Measures

The processes and clinical metrics in this analysis focused on appropriate prevention of and care for chronic conditions, derived from a selection of measures in the Healthcare Effectiveness Data and Information Set (HEDIS).27-29 We measured appropriate markers for disease control, where applicable (eg, laboratory tests for glycated hemoglobin [A1C] and low-denlipoprotein cholesterol [LDL-C]), which were adapted from the HEDIS measures (Table 2). Also compared were rates of eye examinations (retinal) and medical attention for nephropathy for diabetic patients, imaging procedures for low back pain that were not supported by appropriate diagnoses, and appropriate testing of children with pharyngitis. Appropriate medication usage was assessed, including antibiotic use among children with nonspecific or viral upper respiratory tract infections and among adults with acute bronchitis, as well as the use of long-term controller medications among patients with persistent asthma. This study also evaluated the rates of inpatient hospitalization and use of ED services, and costs for PCMH and non-PCMH patients.

This analysis assessed 2 types of costs: medical costs only and total costs on a per member per month (PMPM) basis. Allowed medical costs included plan-paid and patient out-of-pocket costs (deductibles, copayments, and coinsurance) associated with medical claims only, which allowed for the inclusion of members who did not receive their pharmacy benefits through BCBS. Total costs represented the aggregate of allowed costs associated with both medical and pharmacy claims, and applied to members who received both their medical and pharmacy benefits from BCBS only. Members with zero PMPM costs were excluded from the cost analyses as these cases could potentially distort the analysis.

Statistical Analysis

Chi-square and 2-sample t tests were used to assess the statistical significance of any differences in preventive services and care management between the PCMH and non-PCMH cohorts. Statistical models were fitted individually to the pediatric and adult groups. Logistic regression models were used to test differences in adjusted rates of inpatient hospitalizatioin and use of ED services, with covariates including age, sex, health plan type, and Deyo-Charlson comorbidity index (DCI) scores that were calculated from claims between July 1, 2008, and December 31, 2008, depending on their clinical merit and potential importance.30 Differences in costs were analyzed with multivariate generalized linear regression modeling, using gamma distribution and log link function because of its applicability to continuous variables with highly skewed distribution. To risk-adjust costs, multivariate generalized linear regression models were fitted for costs based on non-PCMH cohorts’ data. The cost models included age, sex, health plan type, and DCI score as covariates because of their clinical importance to healthcare utilization.31 Female patients, older patients, and patients with greater comorbidity burdens were likelier to utilize healthcare services. We also adjusted for health plan types (health maintenance organizations, preferred provider organizations, point-of-service plans) to account for potential differences due to benefit design or variations in insurance products. Cost models were used to predict the costs for each patient in the PCMH cohort given an individual’s age, sex, health plan type, and DCI score, and predicted costs were presented as risk-adjusted costs.32 To address outlier issues in the costs data, PMPM caps were applied in accordance with actuarial standards ($8333.33 for pediatrics and $20,833.33 for adults). Statistical analyses were conducted with SAS 9.1 software (SAS Institute Inc, Cary, North Carolina). The alpha level was set at .05 for each test.


Patients’ Clinical and Demographic Characteristics at Baseline

This study included 31,032 patients in the PCMH cohort and 350,015 patients in the non-PCMH cohort, each of which had a greater proportion of females (P <.01).

Pediatric Population

Copyright AJMC 2006-2019 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up