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The American Journal of Managed Care October 2013
Dispensing Channel and Medication Adherence: Evidence Across 3 Therapy Classes
Reethi Iyengar, PhD, MBA, MHM; Rochelle Henderson, PhD, MPA; Jay Visaria, PhD, MPH; and Sharon Glave Frazee, PhD, MPH
Utilization of Lymph Node Dissection, Race/Ethnicity, and Breast Cancer Outcomes
Zhannat Z. Nurgalieva, MD, PhD; Luisa Franzini, PhD; Robert O. Morgan, PhD; Sally W. Vernon, PhD; and Xianglin L. Du, MD, PhD
The Mis-Measure of Physician Performance
Seth W. Glickman, MD, MBA; and Kevin A. Schulman, MD
Inefficiencies in Osteoarthritis and Chronic Low Back Pain Management
Margaret K. Pasquale, PhD; Robert Dufour, PhD; Ashish V. Joshi, PhD; Andrew T. Reiners, MD; David Schaaf, MD; Jack Mardekian, PhD; George A. Andrews, MD, MBA, CPE; Nick C. Patel, PharmD, PhD, BCPP; and James Harnett, PharmD, MS
Empirical Analysis of Domestic Medical Travel for Elective Cardiovascular Procedures
Jacob D. Langley, MS-HSM; Tricia J. Johnson, PhD; Samuel F. Hohmann, PhD, MS-HSM; Steve J. Meurer, PhD, MBA, MHS; and Andy N. Garman, PsyD
Physician Capability to Electronically Exchange Clinical Information, 2011
Vaishali Patel, PhD, MPH; Matthew J. Swain, MPH; Jennifer King, PhD; and Michael F. Furukawa, PhD
Physician Assistants in American Medicine: The Half-Century Mark
James F. Cawley, MPH, PA-C; and Roderick S. Hooker, PhD, PA
How Do Providers Prioritize Prevention? A Qualitative Study
Jeffrey L. Solomon, PhD; Allen L. Gifford, MD; Steven M. Asch, MD; Nora Mueller, MAA; Colin M. Thomas, MD; John M. Stevens, MD; and Barbara G. Bokhour, PhD
Currently Reading
Outcomes Among Chronically Ill Adults in a Medical Home Prototype
David T. Liss, PhD; Paul A. Fishman, PhD; Carolyn M. Rutter, PhD; David Grembowski, PhD; Tyler R. Ross, MA; Eric A. Johnson, MS; and Robert J. Reid, MD, PhD

Outcomes Among Chronically Ill Adults in a Medical Home Prototype

David T. Liss, PhD; Paul A. Fishman, PhD; Carolyn M. Rutter, PhD; David Grembowski, PhD; Tyler R. Ross, MA; Eric A. Johnson, MS; and Robert J. Reid, MD, PhD
This study investigated healthcare quality, utilization, and costs among patients with common chronic illnesses in a patient-centered medical home prototype redesign.
Objectives: To compare quality, utilization, and cost outcomes for patients with selected chronic illnesses at a patient-centered medical home (PCMH) prototype site with outcomes for patients with the same chronic illnesses at 19 nonintervention control sites.

Study Design: Nonequivalent pretest-posttest control group design.

Methods: PCMH redesign results were investigated for patients with preexisting diabetes, hypertension, and/or coronary heart disease. Data from automated databases were collected for eligible enrollees in an integrated healthcare delivery system. Multivariable regression models tested for adjusted differences between PCMH patients and controls during the baseline and follow-up periods. Dependent measures under study included clinical processes and, outcomes, monthly healthcare utilization, and costs.

Results: Compared with controls over 2 years, patients at the PCMH prototype clinic had slightly better clinical outcome control in coronary heart disease (2.20 mg/dL lower mean low-density lipoprotein cholesterol; P <.001). PCMH patients changed their patterns of primary care utilization, as reflected by 86% more secure electronic message contacts (P <.001), 10% more telephone  contacts (P = .003), and 6% fewer in-person primary care visits (P <.001). PCMH patients had 21% fewer ambulatory care–sensitive hospitalizations (P <.001) and 7% fewer total inpatient admissions (P = .002) than controls. During the 2-year redesign, we observed 17% lower inpatient costs (P <.001) and 7% lower total healthcare costs (P <.001) among patients at the PCMH prototype clinic.

Conclusions: A clinic-level population-based PCMH redesign can decrease downstream utilization and reduce total healthcare costs in a subpopulation of patients with common chronic illnesses.

Am J Manag Care. 2013;19(10):e348-e358
This study investigated healthcare quality, utilization, and costs among patients with common chronic illnesses in a clinic-level, population-based patient-centered medical home (PCMH) prototype redesign durin 2007 and 2008. Multivariable regression models tested for adjusted differences between PCMH patients and controls during baseline and follow-up periods.
  • PCMH patients had 7% lower total healthcare costs during the 2-year redesign, largely driven by lower inpatient utilization and costs.
  • PCMH patients with coronary heart disease had slightly better low-density lipoprotein cholesterol control.
  • Findings demonstrate the capacity of a population-based PCMH redesign to achieve desirable outcomes in a chronically ill patient subpopulation.
Many stakeholders in American healthcare have embraced the patient-centered medical home (PCMH) in recent years. A variety of small and large practices1 and delivery systems2,3 are implementing pilots and demonstration projects, with financial and operational support from payers4-6 and multistakeholder collaboratives.7 Although each medical home initiative reflects a unique blend of clinicians, patients, practice infrastructures, and payment mechanisms, all PCMH interventions have the goal of providing patients with a continuous source of whole-person primary care.8-10

Most PCMH interventions emphasize mechanisms to improve care delivery for persons with chronic illness. Chronically ill patients have long been hypothesized to benefit from PCMH elements such as teambased care, productive patient-provider relationships, clinical information  technology use, and delivery system design.11 The chronic care model has been incorporated in PCMH interventions12 and assessment tools,13 and PCMH interventions have disproportionately targeted chronically ill patients14 or elderly patients with high chronic illness burdens.

Despite these links between the medical home and chronic illnes care, the evidence base contains few, if any, rigorous evaluations of PCMH effects on the quality, utilization, and costs of care in patients with chronic illnesses. We address this gap by reporting findings of a 2007 to 2008 prototype PCMH redesign2 among patients with at least 1 of 3 common chronic illnesses in which the majority of care is typically delivered in the primary care setting: diabetes, hypertension, and coronary heart disease (CHD). Our objective in conducting this study was to investigate differences in quality, utilization, and costs of care between chronically ill patients at the PCMH site and comparable patients at 19 nonintervention control sites in the same healthcare system.


We assessed the impact of a PCMH redesign implemented at 1 clinic within Group Health, an integrated health plan and care delivery system in Washington State. The PCMH prototype clinic is located in metropolitan Seattle and is one of 20 clinics Group Health owns and operates in Washington’s Puget Sound region. The clinic was chosen as the PCMH prototype because of the stability of its leadership and its history of successfully implementing change. Group Health pursued the PCMH redesign after a series of reforms in financing and primary care operations yielded mixed results.15 Although the earlier reforms achieved their primary objectives of increasing patient access and satisfaction with care and reducing total costs, discouraging trends (eg, increased emergency department [ED] costs, decreased job satisfaction among primary care physicians) were also observed.16

A comprehensive list of design principles and change components in the PCMH redesign is presented elsewhere,12 but we describe selected key elements here. In the prototype clinic, increased primary care staffing supported reductions in physicians’ patient panels from an average of 2327 patients to 1800 patients, physicians were paired in dyads with medical assistants, and standard in-person primary care office visits were lengthened from 20 to 30 minutes. “Virtual medicine” contacts—secure electronic messaging and telephone encounters—were emphasized by encouraging patients to register for a secure online patient portal and by rerouting patients’ calls to an organizational consulting nurse service to primary care teams during normal clinic operating hours. Some PCMH components explicitly targeted chronically ill patients,2 such as creation of collaborative care plans and provider outreach (by phone or secure message) to manage monitoring tests.

Prior analyses compared 2-year outcomes for patients at the PCMH prototype clinic with those for patients at other Group Health clinics in western Washington State.2,17 In both the full practice and the practice’s elderly subpopulation, PCMH patients had fewer ambulatory care–sensitive hospital admissions (13% full practice, 18% elderly) and fewer combined ED and urgent care visits (29% full practice, 21% elderly). Six percent fewer all-cause hospitalizations and accompanying lower inpatient costs ($14 per month) were also observed in the full practice.2


Study Design and Population

This study used a nonequivalent pretest-posttest control group design,18 including baseline data from 2006 and followup data from 2007 and 2008. We used automated Group Health databases to identify adults with diabetes mellitus (types 1 and 2), hypertension, or CHD. These data sources contain diagnoses, procedures, and pharmacy data for care obtained at Group Health and at sites where providers deliver care to Group Health patients on a contracted basis; laboratory results and clinical encounter data are only available for care provided at Group Health. The accuracy and completeness of these data sources have been extensively validated.19-22 Group Health’s institutional review board approved all study protocols.

Patients in the final study population were aged 18 to 85 years, received care at 1 of 20 Group Health clinics in western Washington State, had at least 6 months of enrollment during 2006, and had 3 or more months of enrollment in both 2007 and 2008. We also required enrollment during December 2006, which facilitated collection of baseline case mix variables.23 To account for clinic-level factors and ensure comparability across study groups, we excluded patients who switched enrollment between clinics on a year-to-year basis. We excluded patients with dementia at baseline and women who gave birth during the study, as much of their healthcare use was presumably attributable to factors external to the PCMH redesign.

Patients at both the PCMH clinic and other clinics were only included in the final study population if they had 1 or more of the 3 included chronic illnesses. We identified patients with preexisting diabetes, hypertension, and CHD using case definitions designed to achieve high specificity and high positive predictive value.24,25 This approach utilized patterns of diagnoses, procedures, laboratory values, and pharmacy fills to minimize erroneous inclusion of “false positive” patients with unconfirmed chronic illness. Case definitions are listed in Appendix A.

Data Collection and Measures

We collected data on disease-specific quality of care in the years 2006 and 2008. Laboratory results provided glycated hemoglobin (A1C) levels for patients with diabetes and low-density lipoprotein (LDL) cholesterol levels for patients with CHD. Systolic and diastolic blood pressure readings for patients with hypertension were acquired from electronic encounter data. If patients had clinical outcome data collected more than once in an individual year, we only used the last recorded value from that year.

Laboratory and blood pressure data were converted into disease-specific dependent variables for 3 types of quality measures: clinical processes, clinical outcome benchmarks, and mean clinical outcomes. We created binary measures of clinical process performance based on whether laboratory data on A1C and LDL were collected annually. Outcome benchmarks were assessed by binary measures of A1C below 9.0% among patients with diabetes, blood pressure below 140/90 mm Hg among patients with hypertension, and LDL below 100 mg/dL among patients with CHD. Continuous A1C, systolic blood pressure, and LDL cholesterol results provided mean clinical outcome measures for each chronic illness.

Utilization and cost data were collected for the 2006 baseline year and the 2-year PCMH redesign. Group Health’s automated systems assigned patient costs on a monthly basis, reporting actual costs from the general ledger. Overhead costs (eg, additional staffing costs during the PCMH redesign) were fully allocated to patient care departments. Cost and in-person utilization data were collected for primary care, specialty care, total inpatient admissions, and combined ED and urgent care. We also collected data on  ambulatory care–sensitive inpatient utilization26 and total healthcare costs.  Clinical databases provided data on patients’ use of secure message threads, telephone encounters, and calls to the consulting nurse service. We accounted for changes in internal cost accounting at Group Health during fall 2008 by truncating collection of cost and in-person utilization data at 21 months, which ensured consistency in these variables over time. Period-specific utilization and costs were converted to monthly rates based on  patients’ number of days of enrollment at Group Health during the baseline and PCMH redesign.

We collected data on patients’ age, sex, and case mix variables from Johns Hopkins Adjusted Clinical Groups (ACG) System software.23 Aggregated Diagnosis Groups variables from December 2006 provided a measure of patients’ morbidity burden during the 2006 baseline year. The 32 Aggregated Diagnosis Groups variables classify International Classification of Diseases, Ninth Revision diagnoses into clinically cogent morbidity clusters based on duration, severity, diagnostic certainty, etiology, and expected need for specialty care, and have been extensively used for case mix ascertainment and adjustment in primary care populations.27-29


Multivariable regression models assessed differences between PCMH patients and controls during the 2006 baseline year and the 2007 to 2008 follow-up periods. Each model tested for the effect of the PCMH by including a patient-level indicator of empanelment at the PCMH prototype clinic as the independent variable.

Quality-of-care analyses used data from the 2006 baseline year and 2008 to investigate differences between PCMH patients and controls with respect to condition-specific processes and outcomes. All patients with diabetes and CHD were included in the clinical process analysis; the clinical outcomes analysis for each chronic illness was restricted to patients who had disease-specific outcome data collected in both 2006 and 2008. We used Poisson regression models (Poisson distribution, log link) to investigate clinical processes and outcome benchmarks in the 2006 baseline year and the 2008 follow-up year, incorporating robust variance estimates to obtain relative risks.30 We used linear regression models to investigate differences in mean clinical outcomes in 2006 and 2008. Each condition-specific regression model was restricted to patients with the targeted chronic illness, and adjusted for age and sex. Regression models estimating 2008 results additionally adjusted for 2006 baseline results.

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