David Grembowski, PhD

 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.

Nonequivalent pretest-posttest control group design.

 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.

 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; 

 <.001). PCMH patients changed their patterns of primary care utilization, as reflected by 86% more secure electronic message contacts (

 = .003), and 6% fewer in-person primary care visits (

 <.001). PCMH patients had 21% fewer ambulatory care—sensitive hospitalizations 

 <.001) and 7% fewer total inpatient admissions (

 = .002) than controls. During the 2-year redesign, we observed 17% lower inpatient costs (

 <.001) and 7% lower total healthcare costs (

 <.001) among patients at the PCMH prototype clinic.

 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.

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 practices

are implementing pilots and demonstration projects, with financial and operational support from payers

 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.

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.

 The chronic care model has been incorporated in PCMH interventions

 and PCMH interventions have disproportionately targeted chronically ill patients

 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 redesign

 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.

 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.

A comprehensive list of design principles and change components in the PCMH redesign is presented elsewhere,

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&mdash;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,

 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.

 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.

This study used a nonequivalent pretest-posttest control group design,

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.

 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.

 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.

 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 

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 utilization

 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.

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.

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.

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.

Our analysis of healthcare use investigated rates of monthly in-person contacts and virtual medicine use during the 2006 baseline, the redesign’s first year (2007), and the full observation period (in-person utilization analysis was truncated at 21 months). We used Poisson regression models to estimate  utilization rates, setting the scale parameter to the deviancedivided by the residual degrees of freedom to preclude violations of model assumptions of over- or under-dispersion.

 Regression models estimating baseline  healthcare use adjusted for age and sex. Regression models estimating use during the PCMH redesign adjusted for age, sex, Aggregated Diagnosis Groups count, and binary indicators for each of the 3 chronic illnesses.

We used an algorithm recommended by Manning and Mullahy32 to select models for cost outcomes that would provide unbiased estimates of covariate effects in right-skewed monthly cost data. This approach led us to select a generalized linear regression model with log link and gamma distribution and to report results in proportional—rather than dollar-based&mdash;cost differences. Regression models estimating baseline year costs  were adjusted for age and sex. Regression models estimating PCMH redesign period costs were adjusted for age, sex, and log-transformed baseline costs for each dependent cost variable (eg, inpatient cost models adjusted for log-transformed baseline inpatient costs).

All regression models investigating quality, utilization, and cost in the chronically ill study population were estimated using generalized estimating equations with independent working correlations and robust sandwich variance estimates.

 This approach controlled for clustering at the level of patients’  paneled primary care clinics and is robust to misspecification of within-cluster correlation. Analyses were conducted using SAS software, version 9.2 (SAS Institute Inc, Cary, North Carolina). We used an alpha threshold of 

.05 to determine the statistical significance of findings.

We conducted multiple sensitivity analyses to assess whether cost and utilization results were robust across analytic approaches. When assessing stability of 12- and 21-month cost and utilization results, 1 sensitivity analysis included patients with asthma and chronic obstructive pulmonary disease (COPD). These patients were initially targeted for inclusion but were excluded from the final study population due to low prevalence at the PCMH clinic and unavailability of diseasespecific quality outcome data. A utilization-only sensitivity analysis by using DxCG risk scores mimicked the risk adjustment approach of prior PCMH prototype analyses.

 An additional sensitivity analysis used a difference-in-difference approach, adjusting for age and sex, to assess potential variability of utilization and cost findings for the full follow-up period.

The final study population included 37,938 adults with diabetes, hypertension, and/or CHD, with 1181 patients paneled to the PCMH prototype clinic and 36,757 patients paneled to other clinics (

). On average, PCMH patients were 3.1 years older than patients at other clinics (mean age 65.0 vs 61.9 years; 

 <.001) and more likely to be female (54% vs 51%; 

= .04). Patients at the PCMH clinic were less likely to have comorbid combinations of diabetes, hypertension, and CHD (13% vs 21%; 

 <.001), but had higher counts of Aggregated Diagnosis Groups case mix variables (mean 7.2 vs 6.9;

 = .01), reflecting a greater prevalence of health conditions other than the 3 conditions targeted in this study. Among patients at both the PCMH prototype clinic and controlclinics, 93% were enrolled at Group Health for the entire 2-year follow-up period.

Quality-of-care analysis revealed differences between the PCMH clinic and control clinics (

). During the 2006 baseline year, patients with diabetes at the PCMH clinic were 4% more likely to have A1C under 9.0% (

 <.001) and their mean A1C was 0.20% lower than that of the controls (

 <.001). These differences among patients with diabetes largely persisted through the PCMH redesign. Although we observed no significant differences in LDL control among patients with CHD during baseline, in 2008 patients at the PCMH clinic were 11% more likely to have LDL below 100 mg/dL, (

 <.001), and their mean LDL cholesterol was 2.20 mg/dL lower than that of the controls (

 <.001). Patients with hypertension at the PCMH clinic were 5% more likely than controls to have blood pressure below 140/90 mm Hgduring baseline (

= .01), but we observed no differences in blood pressure control in 2008.

During the pre-PCMH baseline year, patients at the PCMH clinic had higher rates of specialty care visits (10%, 

 <.001), ambulatory care—sensitive hospitalizations (31%,

 <.001), and total inpatient admissions (22%, 

). Baseline period differences  in inpatient care were reversed during the first yearof PCMH redesign—patients at the PCMH clinic had 23% fewer ambulatory care—sensitive hospitalizations (

<.001) and 4% fewer total inpatient admissions (

 = .05) than controls—and these differences were sustained over 21 months. Patients at the PCMH clinic had fewer ED and urgent care contacts than controls during the baseline period (18%, 

 = .005); this difference widened to 29% over the first year of the PCMH redesign (

 <.001). Patients at the PCMH prototype clinic had 6% fewer in-person primary care contacts than controls (

Patients at the PCMH prototype clinic had more virtual medicine contacts than controls during the pre-PCMH baseline year (

.05). During the first 12 months of the redesign, PCMH patients had 94% more secure electronic message threads (

<.001) and 17% more telephone encounters than controls (

 <.001). These differences were attenuated slightly in 24-month results (

003). The PCMH patients had 18% and 22% fewer telephone calls to the consulting nurse service over 12 and 24 months, respectively (

 <.001 for both). This finding is consistent with the redesign’s goal of decreasing nonemergent consulting nurse calls by redirecting calls to the primary care team during clinic operating hours.

Baseline cost findings were similar to observed baseline utilization results, including higher specialty care costs (32%, 

 <.001), lower ED and urgent care costs (16%, 

 <.001),and higher total inpatient costs (33%, 

<.001) among patients at the PCMH clinic (

). Despite nonsignificant differences in specialty utilization during the PCMH redesign, specialty care costs were 9% higher among PCMH patients over both 12 months (

 = .004) and 21 months (P = .02). The PCMH patients had 6% lower ED and urgent care costs over 21 months (

= .01), 16% lower inpatient costs over 12 months (

 <.001), and 17% lower inpatient costs over 21 months (

 <.001). Total per member per month costs were 19% higher among PCMH patients during the baseline year (

In sensitivity analyses using alternative inclusion criteria and case mix variables, findings for virtual medicine use and 21-month in-person utilization and costs were robust across analytic approaches; selected 12-month findings varied by approach. When adjusting for DxCG variables or including patients with asthma and COPD (61 additional PCMH patients and 2340 additional  controls) in 12-month analyses, we observed 5% greater specialty utilization among PCMH patients (

 = .02 including asthma and COPD) and no significant differences in all-cause inpatient admissions. Including patients with asthma and COPD also led to the observation of 7% higher primary care costs among PCMH patients at 12 months (

The sensitivity analysis using a difference-in-difference approach estimated more dramatic savings among PCMH patients for inpatient costs and total healthcare costs. Appendix B presents complete difference-in-difference results and accompanying comments.

We observed numerous associations between a PCMH redesign and the quality, utilization, and costs of care among patients with diabetes, hypertension, and CHD. Patients at the PCMH clinic changed their primary care utilization patterns over the 2-year redesign, as reflected by use of more phone encounters and secure message threads, but fewer in-person primary care visits, than controls. Despite these changes, we observed no group-level differences in primary care costs, presumably due to the longer length of in-person visits at the PCMH site and costing systems’ allocation of PCMH implementation costs to the prototype clinic. The PCMH patients had 9% higher specialty care costs during the redesign, which is smaller than the 32% greater specialty costs during the baseline year, but is consistent with prior findings.

Over 21 months, we observed reduced utilization of downstream care at the PCMH site, including less ED and urgent care, fewer ambulatory care—sensitive hospitalizations, and fewer total inpatient admissions. The PCMH patients had 7% lower total healthcare  costs during the PCMH redesign,largely driven by the reversal of baseline year trends in inpatient use and costs. When applied to the $697 unadjusted total monthly healthcare costs in this study population during the 2006 baseline year, the observed 7% cost difference translates to approximately $49 savings per month.

Improved clinical outcome control among patients with CHD at the prototype clinic provides evidence of the PCMH’s ability to globally reduce costs in this population while improving quality. Although differences in LDL cholesterol were statistically significant, they were small in magnitude, which is consistent with modest quality improvements observed in other PCMH transformations.

Our 2-year results on setting-specific utilization are comparable to those in other patient populations at the PCMH prototype clinic.

 We observed slightly greater differences between PCMH patients and controls for emergency and urgent care contacts, ambulatory care—sensitive hospitalizations, and total inpatient admissions. Given similar findings across analyses (eg, 7% fewer inpatient admissions among the chronically ill patients and 6% fewer among the full clinic population), we do not believe the impacts of the PCMH redesign were confined to patients with chronic illness. However, utilization impacts in chronilogically ill patients wereconsistently larger than those in prior analyses, which probably contributed to the statistically significant lower total healthcare costs observed here, but not for all patients.

This study differed from prior medical home evaluations in important ways. Unlike several PCMH interventions in the research literature,

 Group Health’s PCMH prototype redesign did not embed care managers in a primary care practice, nor was the intervention confined to a subgroup of high-risk or chronically ill patients. It was instead implemented in the prototype clinic’s full patient population, incorporating fundamental tenets of primary care

 to support productive, longitudinal relationships between physician-led primary care teams and patients. Consistent with recommendations of a recent white paper commissioned by the Agency for Healthcare Research and Quality,

 our study investigated PCMH effects among chronically ill patients and adjusted for clinic-level clustering.

Our encouraging findings were presumably attributable to an alignment of structures and processes at the PCMH clinic that allowed for effective provision of whole-person care. The  PCMH components that may have facilitated the productivepatient-provider interactions envisioned by the chronic care model

 include enhanced care team staffing, pairing longer office visits with promotion of increased virtual medicine use, and effective outreach for patients’ chronic and acute needs.

Articles

This study investigated healthcare quality, utilization, and costs among patients with common chronic illnesses in a patient-centered medical home prototype redesign.

David Grembowski, PhD

Articles

Outcomes Among Chronically Ill Adults in a Medical Home Prototype