L. Gregory Pawlson, MD, MPH

Prior analyses of Medicare health plans have exam-ined either utilization of services or quality of care, but not both jointly. Our objective was to compare utilization and quality for Medicare Advantage (MA) enrollees with diabetes or cardiovascular disease to that for similarly defined traditional Medicare (TM) beneficiaries.

Cross-sectional matched observational study using data for 2007.

 We obtained individual-level Healthcare Effectiveness Data and Information Set (HEDIS) relative resource use (RRU) and quality data for patients enrolled in MA, and then developed comparable claims-based measures for matched samples of TM beneficiaries. Main outcome measures: utilization levels for inpatient care, evaluation and management services, and surgery; number of emergency department (ED) and inpatient visits; and quality of ambulatory care measures.

We studied approximately 680,000 MA health maintenance organization (HMO) enrollees with diabetes and 270,000 HMO enrollees with cardiovascular conditions. For both conditions and almost all major strata, the RRU was lower for those enrolled in MA than for those in TM. Spending for those with diabetes was $5223 for MA HMO enrollees compared with $6413 for those in TM (cost ratio, 0.81; 

 <.001). ED utilization rates were consistently lower in MA than TM (567 vs 719 visits/1000 enrollees; rate ratio, 0.79; 

 <.001). Health plans that are more established, nonprofit, and/or larger generally had lower resource use and better relative quality than did smaller, newer, for-profit HMOs or preferred provider organizations.

RRU for those with diabetes or cardiovascular disease is lower in MA, while quality of care is higher. Better MA plans may add value to the care of these major chronic medical conditions.

We compared price-standardized utilization and quality of care for Medicare Advan-tage (MA) enrollees with diabetes or cardiovascular disease with matched beneficiaries from traditional Medicare in geographic areas. We found that:

		For both conditions, relative resource use was lower for those enrolled in MA than for those in traditional Medicare.

		Quality of care for diabetes and cardiovascular disease measures was higher in MA for the 4 measures examined, although plans varied greatly in their performance.

		Health plans that are more established, nonprofit, and/or larger generally had lower relative resource use and better relative quality than did HMOs or PPOs that were smaller, newer, and/or for-profit.

Under managed competition, health plans are expected to compete to provide health services of high quality while also controlling the overall costs of care. A key element of a health plan’s ability to contain cost is its ability to control utilization. Although some transparency initiatives are now publicizing the costs that health plans pay for specific services,

such approaches have not assessed differences in the full range of medical services used by patients with particular health conditions. Moreover, many have concerns that health plans that are more successful at controlling utilization might also deliver care of lower quality, though existing data from commercial health plans show no clear relationship between spending and quality.

Medicare’s managed care program, Medicare Advantage (MA), currently provides care to 15.7 million Americans, representing 30% of beneficiaries.

 Relative to traditional Medicare (TM), MA plans may be able to treat patients with particular diagnoses with greater efficiency while attaining equal or superior quality through their flexibility in enrollee benefits, network contracting, and coordination of care, but whether they do so is not known.

 Since 1997, health plans participating in Medicare have been required to report annually on measures of the quality of preventive care and of the management of chronic diseases such as diabetes.

 In 2006 and 2007, CMS also required MA plans to report a set of relative resource use (RRU) measures focused on the care of patients with diabetes (2006 and 2007) and cardiovascular disease (2007 only).

 Such measures of price-adjusted utilization allow for direct comparisons of utilization among health plans as well as between MA and TM.

In this study, we evaluated both the utilization of services and the quality of ambulatory care provided by MA health plans by comparing annual standardized spending and quality of care for 2 specific medical conditions for MA health plan enrollees with corresponding measures among TM beneficiaries.

From CMS and the National Committee for Quality As-surance (NCQA), we obtained health-plan-level Healthcare Effectiveness Data and Information Set (HEDIS) measures of RRU and individual-level HEDIS data on quality of ambulatory care for patients with diabetes and cardiovascular disease enrolled in MA health plans. We focused on care delivered in 2007, the most recent year in which MA plans were required to report these measures before reporting became voluntary and much less consistent.

 We constructed similar measures within TM from Medicare claims data for a random 20% sample of beneficiaries. Given the well-known geographic variation in Medicare services, we created matched samples for each health plan based on its geographic region and enrollee demographic characteristics.

. The RRU measures use standardized pricing applied uniformly to services delivered within MA and TM, thereby accounting for variation in prices due to geography and negotiated prices in MA.

 Spending on all services for all patients with qualifying diagnoses is calculated over the entire year. The RRUs are risk-stratified by age (categorized into 5-year intervals for patients aged 65-85 years), sex, type of diabetes (type 1 or type 2) or cardiovascular disease (acute myocardial infarction, congestive heart failure, angina, or coronary artery disease) and the presence or absence of 1 or more major comorbidities (ie, cardiovascular conditions, chronic obstructive pulmonary disease [COPD], depression, hypertension, or chronic kidney disease for diabetes, and asthma, COPD, diabetes, and hypertension for cardiovascular disease). We then aggregated these strata using nationally determined weights to create a standardized measure of overall resource use. Spending categories include inpatient care, surgery and other procedures, and evaluation and management services, and we also sum spending across these categories. Rates of emergency department (ED) visits and inpatient admissions are also reported. Beneficiaries with concomitant specified dominant medical conditions including active cancer, end-stage renal disease, human immunodeficiency virus/AIDS, and organ transplants are excluded.

HEDIS quality data are collected from administrative billing or encounter records, or by using a hybrid approach in which medical records are also reviewed for services that may not be recorded in administrative data.

CMS has audited HEDIS quality measures reported by Medicare health maintenance organizations (HMOs) and found them to be highly accurate.

 In order to compare MA and TM, we focused on measures that can be constructed from Medicare claims for TM enrollees, including low-density lipoprotein (LDL) cholesterol testing in the current year for enrollees aged 65 to 75 years with cardiovascular disease, and 3 services for enrollees aged 65 to 75 years with diabetes: glycated hemoglobin (A1C) testing in the current year; LDL cholesterol testing in the current year; and a diabetic retinal exam in the current or prior year.

We defined health plans as CMS contracts, meaning a health plan unit operating in a single state, or in a few cases, up to 3 adjoining states, and we included both HMOs and preferred provider organizations (PPOs). We focused on beneficiaries 65 years or older who were enrolled for the entire calendar year. We excluded beneficiaries in legacy health plans that were reimbursed on a cost basis rather than by capitation. In addition, we excluded beneficiaries in private fee-for-service plans because these plans are not required to report HEDIS data to CMS, as well as those enrolled in special needs plans because such plans serve nonrepresentative beneficiaries. Finally, we excluded HMOs with fewer than 500 enrollees (accounting for <0.2% of enrollees).

To create a comparison sample for each health plan for both the RRU and the diabetes quality analyses, we used the TM enrollment file and Part A and Part B claims files for a random 20% of beneficiaries to identify all persons who were continuously enrolled in Medicare Part A and Part B for the entire reporting year and were 65 years or older as of January 1, 2007.

We excluded residents of long-stay nursing homes—identified using a validated algorithm&mdash;because these beneficiaries rarely enroll in MA; however, we had no similar method to exclude these beneficiaries from the MA data.

We applied NCQA specifications to identify the eligible populations for the measures, assigned a standardized priceobtained from NCQA to each delivered service identified in the claims, and aggregated spending across groups using the exact specifications from NCQA.

Medicare Beneficiary Summary files provided demographic data (age, sex, race/ethnicity, zip code, county, and state of residence), vital status, and health plan and Medicare enrollment information for each beneficiary.

We categorized health plans as large (>25,000 enrollees) versus small, and identified health plans new to MA since 2006. CMS provided the tax status.

We compared RRUs and quality of care in each MA plan with a TM sample matched by geographic distribution (RRUs and quality measures) and demographic characteristics (quality measures only) and then aggregated these results to obtain national estimates. For the RRU measures, the control TM population was weighted to match the exact distribution of health plan enrollees across all zip codes in which it operated. Matching on geography controlled for variation in practice patterns within Medicare across regions.

By matching at the zip code level where possible, we also controlled for unmeasured socioeconomic characteristics associated with residence at this level of geography.

To provide nationally representative estimates of over-all HMO and PPO performance relative to traditional Medicare, we averaged HEDIS RRUs and quality mea-sures, respectively, over MA plans and matched cohorts of traditional Medicare enrollees, weighted by MA enrollment. To assess variation in performance of health plans based on particular characteristics relative to matched traditional Medicare in local areas, we used hierarchical regression models with correlated bivariate random effects for each health plan and its matched traditional Medicare sample and fixed effects for the health plan characteristics noted above, including an indicator vari-able for PPOs, with separate coefficients for the MA and traditional Medicare samples. Because more than 80% of PPOs were small, new, and for-profit, the PPO measure pertains to just this category of PPOs (the few other PPOs were not included in these models).

Finally, for each HMO plan, we created a composite of the 3 diabetes quality measures by taking the mean across the measures, and similarly created an aggregate measure of spending by summing over the 3 spending categories: inpatient care, surgery and other procedures, and evaluation and management services. We constructed similar summary measures of quality and spending for the matched TM cohort for each health plan. We then plotted mean quality relative to the local TM comparison group against mean spending relative to the same comparison group to provide a visual representation of the marginal contribution versus TM for each health plan. We also created a similar plot for the cardiovascular cohort using the single quality measure available for this group.

Analyses were conducted with SAS version 9.2 (SAS Institute, Cary, North Carolina). Two-tailed P values are reported for statistical tests. Our study protocol was approved by the Human Studies Committee of Harvard Medical School and the CMS Privacy Board.

Characteristics of the Medicare HMOs and PPOs in our study appear in 

. We studied data from 190 HMOs and 67 PPOs that included 4,207,433, and 318,293 enrollees, respectively, in 2007. About two-thirds of enrollees were in for-profit health plans. Although 75% of the health plans were small (<25,000 members), these HMOs represented only about 25% of enrollment. Most HMOs had participated in Medicare prior to 2006, but only 11 PPOs had done so.

The MA HMOs in our study enrolled approximately 680,000 beneficiaries with diabetes (and the PPOs approximately 50,000) and approximately 268,000 enrollees with 1 of the 4 cardiovascular conditions (and approximately 12,000 PPO enrollees; sample sizes vary slightly by measure). For the diabetes cohort, just under half were male (48.8%) and more than 80% were white. The largest proportion was from the south and more than 85% had at least 1 comorbid condition (

). After weighting the TM sample to match the MA distribution, the 2 samples had identical demographic characteristics.

Utilization in MA and TM by Types of Diabetes and Cardiovascular Disease

With 1 exception, total standardized spending, as well as each of the 3 categories of spending, was markedly lower for MA HMO enrollees than for matched TM enrollees (

). For instance, total spending was 19% less for diabetics enrolled in MA than for those enrolled in TM ($5223 vs $6413; 

 <.001). The single exception was for diabetics without comorbidity (eg, inpatient spending of $1309 for MA vs $925 for TM; 

 <.001). Visits to the ED were consistently lower in MA, as were rates of hospital inpatient discharges. Similar results were observed for those enrolled in PPOs (

Spending and Utilization by Type of Health Plan 

Patterns of comparative price-standardized utilization by type of plan are presented in

 shows the detailed results). For large, established, nonprofit HMOs, all 3 categories of spending were lower than for the matched TM sample, with differences ranging from 16% (evaluation and man-agement services for diabetics) to 70% (surgery rates for those with cardiovascular disease). In contrast, results were mixed for new, for-profit, small HMOs, where aggregate spending was higher in some categories in MA (eg, inpatient spending for cardiovascular disease, 16% higher), and lower for others (eg, surgery and procedures 43% lower for diabetics in MA). Results for PPO health plans were similar to those for new, for-profit, small HMOs.

Spending and Quality of Care by Health Plan Characteristics 

The mean plan-weighted rates of A1C testing, LDL cholesterol testing, and diabetic retinal exams were 89.7%, 87.6%, and 65.2%, respectively. 

 presents scatter plots of HMO health plan spending and the composite measure of quality for the diabetes cohort by health plan age (established prior to 2006 or not), size (>25,000 members), and tax status. Several findings are apparent: first, while members of most health plans experienced higher quality than the matched TM population in their area and therefore are plotted above the solid horizontal axis, the MA-TM difference varied substantially and for some plans was negative (MA worse than TM). Similarly, MA spending was lower than that in TM for most health plans, but the magnitude of the difference varied and was sometimes reversed (plotted to right of solid vertical axis). Second, there was little association between the spending and quality effects (Pearson correlation coefficient, 0.16), as manifested by the nearly equal distribution of plans across the 4 quadrants formed by median splits (dashed lines) on the 2 variables. Finally, although HMO health plans of each type are present in all 4 quadrants of each plot, the upper left hand quadrant (higher quality/lower spending) contains the most established, large, nonprofit HMO plans and the lower right hand quadrant (lower quality/higher spending) contains a higher proportion of new, small, for-profit HMO plans. A similar pattern was seen for the cardiovascular measures (

This study provides the first compre-hensive comparison between MA and TM of price-standardized utilization and quality of care for those with diabetes and cardiovascular disease, 2 prevalent and costly chronic medical conditions. We found several notable results: first, for both cohorts, RRU—which is a measure of total utilization using a standardized set of prices&mdash;was lower in MA health plans than in TM in each of the main categories of spending examined. Moreover, MA plans achieved higher performance on measures of ambulatory quality.

 Second, marked heterogeneity was evident among MA plans. Most older, larger, nonprofit health plans were able to achieve substantial reductions in service utilization while delivering care of high quality, whereas many newer, smaller, for-profit plans had similar or greater utilization when compared with TM. Finally, utilization among PPOs—an alternative arrangement to HMOs that is generally less managed and coordinated&mdash;showed patterns that were similar to new, smaller HMOs and to TM.

Our findings have important implications for policy. With the 2010 passage of the Affordable Care Act (ACA), the attention of policy makers has now shifted to controlling the seemingly inexorable growth in healthcare costs.

Delivering high-value care requires decreasing utilization of services of low value while simultaneously maintaining or increasing the delivery of services of high value. We show that substantial numbers of larger, nonprofit HMO MA plans appear to be delivering care of high quality, while doing so with substantially fewer resources. Whether this is more due to their actions, as payers perhaps linked to their size and ability to influence provider behavior, or to use of more limited and selected networks of providers, or both, is unknown. Although there has been much focus on payment reform in TM—such as the launching of accountable care organizations (ACOs)&mdash;30% of Medicare enrollees are in MA health plans, a far larger proportion than are currently in ACOs.

MA plans are currently paid more than TM on average.

Because of Medicare regulations and competitionamong plans, many of the savings from these extra payments and the reduced utilization we documented in this study are passed through to beneficiaries in the form of lower premiums, less cost sharing, and benefits for noncovered services.

 However, providers may also profit from these excess payments, as they may be able to negotiate higher prices from MA plans. To finance its expansion of health insurance, the ACA reduced reimbursement for MA plans; how these reductions will impact plan and beneficiary participation and the future growth of the MA program remains an open question.

Articles

Compared with traditional Medicare, relative resource use for those with diabetes or cardiovascular disease is lower in Medicare Advantage, while quality of care is higher.

A Comparison of Relative Resource Use and Quality in Medicare Advantage Health Plans Versus Traditional Medicare

 To examine variation among commercial health plans in resource use and quality of care for patients with diabetes mellitus or cardiovascular disease.

 Cohort study using Healthcare Effectiveness Data and Information Set data submitted to the National Committee for Quality Assurance in 2008.

 Composite measures were estimated for diabetes and cardiovascular disease resource use and quality of care. A “value” classification approach was defined. Obtained were descriptive statistics, Pearson product moment correlations between resource use and quality of care, and 90% confidence intervals around each health plan’s composite measures of resource use and quality of care. Health plans were classified based on their results.

 For patients with diabetes, the correlation between combined medical care services resource use and composite quality of care is negative (−0.201, 

 = .008); the correlation between ambulatory pharmacy services resource use and composite quality of care is positive (0.162, 

 = .03). For patients with cardiovascular disease, no significant correlation was found between combined medical care services resource use and composite quality of care (—0.007, 

 = .94) or ambulatory pharmacy services resource use (0.170, 

Measures of resource use and quality of care provide important information about the value of a health plan. Although our analysis did not determine causality, the statistically weak or absent correlations between resource use and quality of care suggest that health plans and practices can create higher value by improving quality of care without large increases in resource use or by maintaining the same quality of care with decreased resource use.

	An examination of variation among health plans in resource use and quality of care for patients with diabetes mellitus or cardiovascular disease suggests that plans can demonstrate higher value under either of the following conditions:

		By improving quality of care without large increases in resource use

		By maintaining the same quality of care with decreased resource use.

Reducing the rate of increase in healthcare costs, while addressing gaps in quality of care, is the most challenging and pressing healthcare issue facing the United States and other developed countries.

documenting problems with quality and cost of healthcare in the United States have given rise to a growing interest in understanding the relationship between cost and quality. The science of measuring quality has advanced considerably over the past 2 decades; given the widespread use of Healthcare Effectiveness Data and Information Set (HEDIS), ORYX (an initiative by the Joint Commission on Accreditation of Healthcare Organizations), and related measures,

 there is substantial consistency around the content and scope of quality measurement. While methodological approaches to measuring differences in cost are emerging, whether through a focus on resource use or through various formulations of expenditures, there is no widely used way to directly compare costs among entities.

APPROACHES TO MEASURING RESOURCE USE AND QUALITY OF CARE

Premiums represent the cost of care to the employer and employee but have an imperfect relationship with the actual costs of medical care and reflect relative market power, benefit design, and other factors. By contrast, actual prices paid reflect resource use and local market forces but often vary among providers, making it difficult or impossible to use in cost comparative analysis among entities. Using a standardized set of prices applied to actual utilization is a common method for analysis that provides more transparency than premiums alone and avoids the challenges associated with using actual prices. Finally, resource use largely reflects the framework of interactions among patients, providers, and others within the healthcare delivery system.

The rise in chronic diseases has been directly linked to the increase in healthcare spending to treat them.

 estimates that in 2007 the total estimated cost of diabetes was $174 billion, including $116 billion in excess medical expenditures and $58 billion in reduced national productivity. Furthermore, the American Heart Association and the National Heart, Lung, and Blood Institute estimated that in 2009 the cost of cardiovascular diseases and stroke in the United States was $475.3 billion.

While HEDIS has for years included measures reporting health plan utilization of services, these measures do not directly relate to any HEDIS quality measure, are not case mix adjusted, and cannot be aggregated across categories, thus providing a limited amount of useful comparative information. In 2007, the National Committee for Quality Assurance (NCQA) introduced HEDIS measures of relative resource use (RRU) for 6 prevalent high-cost areas of care, including diabetes mellitus, cardiovascular disease, uncomplicated hypertension, chronic obstructive pulmonary disease, asthma, and low back pain, that use standardized prices and case mix adjustment to better define the cost portion of efficiency. Roski et al

 provide an overview of the NCQA HEDIS resource use measurement approach and results from a small pilot test project (<25 health plans) related to diabetes care. Turbyville et al

 examine the implications of wide national variation in RRU and quality results among commercial health maintenance organizations (HMOs) for their members with diabetes. The present study focuses on results for RRU and quality of care among commercial HMOs but goes further by examining the ability to construct a health plan classification approach based on resource use and quality-of-care performance in patients with diabetes or cardiovascular disease.

The data used in this study are part of the HEDIS 2008 health plan submission to the NCQA based on services rendered in 2007. Before submitting performance data to the NCQA, health plans must undergo an independent audit review that assesses the validity of data collection and measure calculations. Analyses reported herein are based on commercial HMO plans that submitted summarized member month, claim, and medical record data for diabetes and cardiovascular disease resource use and quality performance. Individual variables in the RRU measures are based on a patient population perspective and include all services rendered (not just those that are attributed to the disease) in the specified service categories. Health plans submit their summarized data within specified patient cohorts, which allow for risk adjustment once the data are submitted (

). The denominator inclusion criteria for HEDIS RRU measures are designed to be representative of patients included in the related quality measure denominator population, allowing for a more direct examination of the relationship between resource use and quality of care.

To estimate the quality of care, we calculated 2 combined composite quality measures based on calendar year 2007 results, 1 for diabetes and 1 for cardiovascular disease. The diabetes composite quality measure components are as follows: glycated hemoglobin testing, glycated hemoglobin poor control (>9.0% [inverted]), retinal eye examination, low-density lipoprotein cholesterol screening, low-density lipoprotein cholesterol control (<100 mg/dL [to convert cholesterol level to millimoles per liter, multiply by 0.0259]), medical attention for nephropathy, and blood pressure control (<130/80 mm Hg). The cardiovascular disease composite quality measure components are as follows: cholesterol screening, cholesterol control (<100 mg/dL), blood pressure control (<140/90 mm Hg), and persistence of B-blocker treatment after a heart attack. We further indexed each health plan’s results for diabetes or cardiovascular disease against the mean HMO commercial plan performance for each condition, allowing us to demonstrate its relative performance (eg, an index result of 1.12 implies 12% above average, whereas a result of 0.90 implies 10% below average).

To examine RRU among health plan members with diabetes or cardiovascular disease, we used 2 resource use variables for each condition, the combined medical care services variable and the ambulatory pharmacy services variable. The discrete service categories of the RRU measure for total medical care services include inpatient facility services, procedure and surgery services (inpatient and outpatient), and evaluation and management services (inpatient and outpatient). Within these service categories, the measures examine all resources expended during the 2007 calendar year. Detailed characteristics of these measures are included in the NCQA’s HEDIS technical specifications.

The category of inpatient facility services accounts for a patient’s stay and is adjusted for severity of diagnosis, occurrence of major surgery (defined in the specifications), and length of stay. The procedure and surgery services and the evaluation and management services include inpatient-based or outpatient-based care, such as that provided by an internist or a surgeon, and are adjusted for intensity. Ambulatory pharmacy services include all prescriptions rendered in ambulatory settings and are based on National Drug Code identifiers. Ambulatory pharmacy services were analyzed separately because of variation in benefit status or eligibility in pharmacy services and owing to past research on their relationship to quality. Benefit status variation results in the pharmacy component having a different per-member divisor than the medical service categories, rendering a total resource use composite computationally difficult when using summarized data. Furthermore, evidence shows that resource uses for medical and pharmacy services have different, sometimes inverse, correlations with quality.

 The RRU index is the ratio of the health plan’s observed (submitted) per-member-per-month resource use to the expected per-member-per-month resource use across the service categories. Health plans estimate their observed per-member-per-month resource use by following the RRU HEDIS specification that includes application of the NCQA standardized prices to the specified health service units. Pricing algorithms are designed to reflect service pricing levels for HMOs for the most recent period. The NCQA estimates the expected amounts based on all submitted data using indirect standardization to adjust for each health plan’s patient population distribution by age, sex, the presence of comorbid conditions, and severity of condition (eg, congestive heart failure vs angina). The ratio of each health plan’s observed results to expected results is indexed to the mean HMO commercial plan performance.

Statistical software (SAS version 9.1; SAS Institute, Cary, North Carolina) was used to calculate health plan descriptive statistics and Pearson product moment correlations for the diabetes and cardiovascular disease resource use and quality-of-care measures (combined medical care services and pharmacy services) for the ratio of observed results to expected result. We excluded health plans that did not submit complete RRU and quality-of-care data, submitted RRU data for fewer than 400 members with the given disease, or had a ratio of observed results to expected results of less than 0.33 or greater than 3.00. The NCQA based these exclusion decisions on previous research, consideration of standard errors and sample size needs for reliability purposes, and investigation of larger-than-expected observed variation among health plans, which revealed that a ratio of observed results to expected results of less than 0.33 or greater than 3.00 was caused by plan data submission errors in almost all cases. We did not report regression estimates because the direction of causality is unclear: do changes in resource use lead to changes in quality, or do changes in quality lead to changes in resource use? Furthermore, the study design did not permit us to test the direction of causality.

We estimated all results and statistical estimations after removing health plans that failed to meet the conditions for inclusion in the study. For each included health plan, we then estimated the 90% confidence intervals around each variable (combined medical care services, and ambulatory pharmacy services, and quality of care). Using STATA version 10 statistical software,

 we constructed boxplots to examine the distribution of health plan results and fabricated scatterplots to illustrate the variation in results and the plan “value” classifications.

Using the index value (1.00) for each condition, we categorized each health plan into 1 of the following 4 value groups for each condition: (1) high quality (>1.00) and low resource use (<1.00), (2) high quality (>1.00) and high resource use (>1.00), (3) low quality (<1.00) and low resource use (<1.00), and (4) low quality (<1.00) and high resource use (>1.00).

 shows the effect on the initial sample of health plans for each condition by sequential application of exclusion criteria. The final analysis included 173 health plans for diabetes and 125 health plans for cardiovascular disease. 

 gives basic information about health plans included in and excluded from the analysis. Across both conditions, two-thirds were for-profit health plans, 95% publicly reported their quality data, and 90% were accredited by the NCQA. All Health and Human Services regions of the country were represented. Health plans included in the diabetes analysis were significantly more likely to publicly report their quality data and to be accredited by the NCQA, and health plans included in the cardiovascular analysis were somewhat less likely to be for-profit health plans. In terms of regional patterns across both conditions, health plans in the East (regions in Boston, Massachusetts; New York, New York; and Philadelphia, Pennsylvania) were significantly more likely to meet the criteria for inclusion than health plans in the West (regions in Dallas, Texas; Denver, Colorado; and Seattle, Washington).

 shows the distribution of HMO results for RRU and quality of care for diabetes and cardiovascular disease. By construction, the mean for all variables is 1.00. As expected, health plans exhibited greater variation in RRU than in quality of care, as seen by the greater interquartile range (width of the boxes), the more extreme adjacent values (width of the whiskers extending from the boxes), and the presence of health plans beyond the adjacent values (dots). The variation for ambulatory pharmacy services in both disease conditions is more compressed on the upper range than that for combined medical care services. Different components of resource use, such as inpatient and outpatient services and pharmacy, may relate to measurable quality in different ways. For patients with diabetes, the correlation between combined medical care services resource use and composite quality of care is negative (−0.201, 

 <.008); the correlation between ambulatory pharmacy services resource use and composite quality of care is positive (0.162, 

). While we cannot determine a causal relationship, this finding suggests that for members with diabetes, health plans having better diabetes composite quality of care tend to use fewer combined medical care resources (ie, inpatient facility services, procedure and surgery services, and evaluation and management services) but use correspondingly more ambulatory pharmacy resources. Considering medical care services resource use elements individually, we find that inpatient facility services (—0.164, 

 = .03) and procedure and surgery services (—0.219, 

 = .006) have significant negative correlations with diabetes quality, while there is no demonstrated relationship between diabetes quality and evaluation and management services. By contrast, for patients with cardiovascular disease, no significant correlation was found between combined medical care services resource use (—0.007, 

 = .94) and ambulatory pharmacy services resource use (0.170, 

 = .06), nor for any individual medical care services element. For diabetes and cardiovascular disease, scatterplots of quality of care and combined medical care services RRU index results and ambulatory pharmacy services RRU index results show the covariation between quality of care and resource use (

). Health plans for which 90% confidence intervals did not include the mean on both dimensions are represented by red triangles; these plans were excluded from the value classification. 

 summarizes the value classification illustrated by the 4 quadrants of the scatterplots. For combined medical services only, approximately 30% of health plans can be classified as high value (high quality and low resource use) and another 15% as low value (low quality and high resource use).

The findings reported herein suggest that it is possible to categorize health plans into quadrants that indicate significantly higher or lower quality and higher or lower resource use. Analysis of correlations between quality and resource use data collected from commercial HMO plans suggests a comnplex and probably disease-specific relationship between measures of healthcare service delivery and quality.

Our findings demonstrate that we are able to examine the relationship between RRU and quality (as measured by process and intermediate outcome variables) and can classify a substantial number of health plans based on their RRU and quality performance. This represents a modest first step toward determining the relative value of health plans. While some researchers have related quality to cost, we are aware of no previous studies that use large-scale observations for 2 major chronic conditions to classify the relative value of health plans. Furthermore, this study includes quality measures that reflect control of key physiological variables, such as blood pressure and cholesterol and glycated hemoglobin levels. The importance of including ambulatory pharmacy services is obvious: pharmacy may account for more than half of the mean total costs for many chronic illnesses.

 Likewise, the use of intermediate outcome (control) measures is a major advance over the exclusive use of process measures, such as test ordering or screening, and more closely reflects the benefit portion of the value equation.

Our study illustrates the ability to create a useful value classification approach that places health plans into quadrants of high and low quality and high and low resource use and may be applicable to other care delivery systems (eg, accountable care organizations). Furthermore, improvement in risk-adjustment methods may allow reliable reporting of a larger proportion of health plans or entities (eg, those with smaller populations). The finding of a negative relationship between combined medical care services resource use and related inpatient subcomponents— composite quality of care in diabetes and no clear correlation in cardiovascular disease&mdash;indicates a complex relationship between cost components and quality that needs to be explored on a disease-by-disease basis. That each of the 2 major components of the combined medical care services (inpatient facility services and procedure and surgery services) is negatively related to composite quality of care in diabetes suggests the need for a more detailed examination of specific procedures and services that might indicate opportunities for reducing resource use without negatively affecting quality or for improving quality and reducing specific resource use.

A critical question going forward is whether there are definable and replicable practices and interventions that relate to how some health plans seem to achieve higher value, as denoted by higher quality for the same or lower resource use. While additional factors must be considered, the present results imply that overall resource use and quality are independent characteristics of care, at least as now measured, but that certain subcomponents of resource use or cost may be negatively or positively associated with quality for specific diseases. This suggests that purchasers and health plans should use information from both quality and resource use measures to benchmark their performance; furthermore, specific interventions may be required related to controlling and rationalizing resource utilization, as well as efforts that focus on quality improvement. Information on resource use along with data on premiums or expenditures can promote better understanding of the forces driving medical costs.

This analysis is limited to commercial HMO plans that submitted HEDIS data to the NCQA and may not apply to other types of health plans or in other settings. However, the health plans included represent a substantial proportion of the commercial HMO plans and are an important market segment. Moreover, the results tend to support the conclusions by Wennberg et al

 related to expenditures in the Medicare program, suggesting that the findings are robust. The definition of value used in his study is limited to the HEDIS measures, which included clinical process and intermediate outcome measures but did not include measures of patient experience of care or of hospital-related quality. Data are limited to patients with diabetes or cardiovascular disease; while these are highly prevalent and costly diseases, it is unclear if our findings apply to other diseases. Similar analyses are needed for preferred provider organizations and for Medicaid and Medicare health plans.

The HEDIS RRU measures reflect the use of all services received by patients with diabetes or cardiovascular disease. While this inclusiveness of services avoids the issue of excesnsive partitioning of cost found in episode-of-care cost measures, it also leads to inclusion of costs that may not be directly related to the measured condition. The resource use measures are adjusted for age, sex, major chronic condition, and the presence of chronic comorbid conditions. These represent the most important predictors of cost identified in the literature. Indirect standardization ensures that the health plans are treated equally with respect to these attributes, but it is possible that health plans differ on an unobserved variable. However, because these data are cross-sectional, we do not believe that there was an opportunity for health plans to game the measure by selecting on unobservables with an intention to bias these measures. There may be residual bias in the unobservables for other purposes (eg, selection of more compliant patients), and this represents a possible limitation. In addition, these data reflect only a cross-sectional analysis of the relationship between quality and resource use and do not directly prove whether improving quality would lead to changes in resource use or vice versa.

In conclusion, measures of resource use along with measures of quality of care provide objective information that can help define the value of a health plan or delivery system. Although this study did not determine causality, the findings suggest that health plans and practices can create higher value by improving quality of care without large increases in resource use or by maintaining the same quality of care with decreased resource use.

 From National Quality Forum (SET), Washington, DC; National Committee for Quality Assurance (RCS, MAT, SHS, LGP), Washington, DC.

This study was funded by grant 20070496 from The Commonwealth Fund.

This study examines variation among health plans in resource use and quality of care for patients with diabetes mellitus or cardiovascular disease.

Classification of Health Plans Based on Relative Resource Use and Quality of Care

The article by Hayward and his colleagues1 is of interest to those trying to improve clinical guidelines and measures as we move into the rich array of electronic healthcare information that will be readily available in the near future. Currently, both clinical guidelines and their related performance measures are limited by the fact that guidelines must be simple to be remembered and measures can use only claims data, augmented where necessary by basic, yet costly, reviews of paper charts. This situation leads to oversimplification of guidelines and measures as well as potential misuse if they are overinterpreted and pushed beyond their limits. Indeed any general guideline related to control of blood pressure, glycosylated hemoglobin, or any other biologic parameter could cause harm if every patient was pushed therapeutically to comply with that target. It is important to note that the National Committee for Quality Assurance (NCQA) includes a strong advisory that 100% performance is not the goal and that clinical judgment should be used in applying a measure.

Indeed, if 100% compliance were the goal for a measure, it would mean there was absolutely no controversy about the measure or variability in whether patients should be at the threshold goals. There aren’t many issues like that in medicine, but for the ones that come close (eg, aspirin after acute myocardial infarction), compliance is close to perfect, and quality measurement is unlikely to drive improvement. Current guidelines and measures are designed to provide a starting point for the evaluation and improvement of quality of care in a population—not to dictate the care of any individual patient. They are only tools for management.

Although physicians should be aware of guidelines and measures, they need also to apply more nuanced approaches when seeing individual patients. To imply that clinicians would knowingly put patients in harm so they could perform marginally better than other physicians on a clinical performance measure provides a rather dim view of medical practice. The average levels of blood pressure control in persons with diabetes indicate that control is far from optimal overall.2 Thus, it is likely that improved decision making by clinicians and enhanced care of patients will result from providing data to benchmark widely used guidelines related to blood pressure control. Indeed, a reasonable interpretation of the data presented by Hayward et al is that even a highly regarded academic medical center with a sophisticated electronic medical record system may not have reached optimal performance levels for more than half of their patients with diabetes.

Many, but not all, of the widely used evidence-based guidelines (The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure, American Diabetes Association, American Association of Clinical Endocrinologists) recommend 130/80 mm Hg as the most desirable level of control of blood pressure in most patients with diabetes, specifying diabetes as an equivalent to established cardiovascular disease. At the time the measure was developed, the recommendation of the technical subcommittee of the National Diabetes Quality Improvement Alliance, which was only 1 of the groups providing input to NCQA, to use “adequate doses of 3 drugs” as an alternative to reaching the threshold level was simply not feasible using the relatively simple chart reviews and limited claims data available. This is in contrast to the full electronic medical record data available to Hayward et al. That said, the suggestion by the authors of the current article to exclude patients whose systolic blood pressure is not at threshold but whose diastolic blood pressure is less than 70 mm Hg should first be reviewed by the scientific groups overseeing the evidence-based guidelines on which the Healthcare Effectiveness Data and Information Set (HEDIS) measures are based, and if changes are made in the guidelines, considered in turn by the measurement advisory committees and the Committee on Performance Measurement that oversee HEDIS measures.

At a more fundamental level, a previous article in this journal by one of us (LGP) pointed out the inherent problems in reference standards, guidelines, and measures that use a single threshold and suggested a much more robust set of approaches that could be used in settings with full electronic health record data systems.3 Approaches currently under study by NCQA include assessment of adequate dosing of medications using expanded order entry and pharmacy data, or using blood pressure levels integrated over time and adjusted for baseline patient risk with clinical data available in the electronic medical record. Equally interesting are efforts to link in electronic data systems much more highly evolved clinical guidelines and decision support tools with measurement and feedback in the “real time” of the clinician encounter. An example of an even more forward-looking approach is that of David Eddy and his group at Archimedes4: to create and apply at the time of the patient encounter patient-specific guidelines and clinical decision support using sophisticated mathematical modeling of both the individual patient’s own clinical parameters and the results of clinical trials. Measures then could be linked to this process to determine whether and how physicians use (or don’t use) such information to shape clinical treatment and the results of that treatment in reducing a patient’s overall cardiovascular risk.

However, all of these advances require fully developed and interoperable electronic clinical data systems.5 Until these systems are in widespread use, we will be limited to making rather small and stepwise improvements in current guidelines and their related measures.

From the National Committee for Quality Assurance (LGP), Washington, DC; and Partners Healthcare (THL), Boston, MA.

Dr Pawlson is the Executive Vice President for the National Committee for Quality Asurance, the organization that develops performance measures. Dr Lee reports serving on the advisory board to Bridges to Excellence. The authors (LGP, THL) report no other relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (LGP); drafting of the manuscript (LGP, THL); critical revision of the manuscript for important intellectual content (LGP, THL); and supervision (LGP).

L. Gregory Pawlson, MD, MPH, Executive Vice President, National Committee for Quality Assurance, 100 13th St, Washington, DC 20005. E-mail: pawlson@ncqa.org.

1. Choe HM, Bernstein SJ, Standiford C, Hayward RA.

 New diabetes HEDIS blood pressure quality measure: potential for overtreatment. 

2. National Committee for Quality Assurance.

2009 State of Health Care Quality Report.

 Washington, DC: NCQA; October 2009. http://www.ncqa.org/tabid/203/Default.aspx. Accessed December 14, 2009.

The past as prologue: future directions in clinical performance measurement in ambulatory care. 

4. Heikes KE, Eddy DM, Arondekar B, Schlessinger L. 

Diabetes Risk Calculator: a simple tool for detecting undiagnosed diabetes and prediabetes. 

Improving CRC screening requires innovative approaches: can electronic medical records help? 

Although physicians should be aware of guidelines and measures, they need to apply more nuanced approaches when seeing individual patients.

Clinical Guidelines and Performance Measures

 To test the relationship between the presence of recommended chronic care model systems and the degree of integration among large medical groups.

 Cross-sectional survey in 2007 completed by medical directors of medical groups nationally with at least 100 physicians and a range of medical services and who had also participated in the National Survey of Physician Organizations.

 We recruited 111 medical directors among 123 who were eligible. The survey asked about the medical group’s structural, financial, and functional aspects of integrated care, as well as the presence and use of practice systems for chronic disease care as measured by the Physician Practice Connections—Readiness Survey (PPC-RS). The analysis tested the association between integration measures and the presence of practice systems, controlling for medical group attributes.

 Ninety-seven completed surveys were returned (89.0% of 109 medical directors eligible). Measures of integration and practice systems varied widely among the medical groups. The total PPC-RS score correlated with each measure of integration but most highly with functional integration (r = 0.53, 

 <.01). The strongest PPC-RS component score correlations were for delivery systems redesign (r = 0.27-0.52, 

 <.01) and for decision support (r = 0.21-0.46, 

 <.05). Adjusting for organizational characteristics had little effect on these relationships.

 As measured by these scales, integration seems to be related to the presence of practice systems components of the chronic care model, although simply having the potential for integration (structure and finance) is much less strongly related than evidence of functional integration.

Large (≥100 physicians) medical groups that have integrated patient care across separate parts of their care systems have more practice systems for quality care. We found the following:

Considerable variation in the degree to which these groups exhibit structural, financial, or functional integration;

Similarly large variation in the presence of practice systems that implement the chronic care model domains;

Significant correlation between each domain of integration and the extent of practice systems, but greatest (r = 0.53, 

Strongest correlations for the chronic care model domains of delivery systems redesign, decision support, and self-management support.

We are in a period of great ferment about what kinds of insurance coverage, payment approaches, and care delivery systems might best address concerns about the cost and quality of US healthcare.

 A recurring theme is that fragmentation in the care systems is largely to blame.

 Some policy leaders have looked to large care systems as the answer in the belief that coordination of services across multiple units of care should lower costs and improve quality.

 The quality transformation of the Veterans Health Administration and the apparent higher quality produced by large staff-model or group-model systems like Kaiser Permanente are often used as examples of what can be done in systems large enough to organize care across the continuum.

This ability to coordinate functions and activities across separate operating units is called integration, and it can occur vertically (owning or contracting for physician services, hospital services, urgent care, rehabilitation, and long-term care centers) or horizontally (creating multihospital systems).

 demonstrated that horizontal integration added little value in quality or costs. This insight led to a greater interest in vertical integration that truly connects functions across organizations, and various models have been described.

The most practical model came from the Health Systems Integration Study (HSIS), which defined an integrated delivery system as “a network of organizations that provides or arranges to provide a coordinated continuum of services to a defined population and is willing to be held clinically and fiscally accountable for the outcomes and health status of the population served.”

 The HSIS studied 3 major types of integration, namely, functional, physician/systems, and clinical. Except for this model from the HSIS, integration has not been measured, and the relationship between integration and quality or cost of care has not been tested, to our knowledge.

Because many care delivery organizations want to know how to improve care quality and cost for longitudinal episodes of care and because it seems logical that a multicomponent integrated organization would have greater opportunities to do that, we studied this relationship among large medical groups nationally. We adopted the definition by the HSIS

 of integrated delivery systems (given in the preceding paragraph) and developed an operational set of measures of integration based on that definition.

In the absence of a standardized set of quality performance measures across the nation for comparison, we had to rely on an upstream process measure, namely, the presence of organized processes of care or practice systems. Casalino et al

 called these care management processes in their National Survey of Physician Organizations. For the present study, we used the Physician Practice Connections—Readiness Survey (PPC-RS) from the National Committee for Quality Assurance to measure these systems.

The PPCRS scores have been shown to be associated with process and outcome measures of diabetes care quality in 40 Minnesota medical groups, and a version of this instrument has become the main assessment device for medical homes.

 Our hypothesis was that the presence of practice systems would be directly proportional to the degree to which a large medical group was integrated.

To be eligible for this study, medical groups had to have at least 100 physicians, including primary care, and have completed the National Survey of Physician Organizations (described in the preceding paragraph). Although the size choice was arbitrary, large groups generally have greater resources and capability to integrate care. There were 132 medical groups that seemed to meet these criteria. However, during the recruitment process, 8 groups were found to be ineligible, and 2 groups in the pool had merged. Of the remaining 123 medical groups, 111 medical directors agreed to complete and return the surveys, for a participation rate of 90.2%.

The PPC-RS was developed by the National Committee for Quality Assurance to assess the presence of practice systems that implement the chronic care model (CCM) by Bodenheimer et al.

 Using the CCM and a Six Sigma analysis of office practice as the basis for creating structural and process measures of systems in office practice, the PPC was developed with an expert panel and a literature review. Its survey questions were subsequently tested for reliability, best respondent, and validity against on-site audits and standardized quality measures.

 In addition to documenting the presence of individual practice systems, the PPC-RS (research version) produces an overall score and a score for 5 of 6 domains of the CCM (health systems, delivery systems redesign, clinical information systems, decision support, and self-management support). A somewhat modified version, the PPC—Patient-Centered Medical Home Survey Tool, has been adopted by all 4 primary care professional associations and by the Patient-Centered Primary Care Collaborative as the main measure of qualifying practices as a medical home.

To create integration measures for this study, we relied on the aforementioned definition of integrated delivery systems to develop items measuring structural, functional, and financial aspects of integration. Details about these measures and their psychometric properties are available in a Technical Appendix from the corresponding author. Briefly, the development of integration composite scores was based on an assessment of patterns of bivariate association among items, assessment of internal consistency reliability, and examination of the factor structure of the items in exploratory dichotomous factor analysis of the tetrachoric correlation matrix. The final approach was also tested qualitatively among an advisory committee of medical directors, who agreed that it was understandable and had good face validity.

The final survey also included a few questions to describe the organization (eg, ownership, number of practice sites, and number of physicians) and was conducted as a mailed questionnaire in late 2007 to the medical directors who had agreed to participate in this study. Follow-up consisted of 2 e-mail reminders and subsequent telephone calls by those of us who had performed the original recruiting (LIS and NT). A few medical groups were contacted to obtain information that was missing or conflicting on the questionnaire.

The degree of integration was summarized in 3 separate scales for each domain of structure, function, and finance. The 9 items in the structure domain ask whether the organization provides (directly or indirectly through contractual arrangements) subspecialty, hospital inpatient, emergency department, rehabilitation center, skilled nursing facility, home health, pharmacy, and palliative or hospice care in addition to primary care. The 5 finance items ask whether the organization assumed financial risk for most patients for professional services, hospital services, ancillary services, durable medical equipment, and injectables. The 9 new survey items comprising the functional integration index measured coordination of care across different service units in the areas of appointment scheduling, clinical information systems, service notification, care protocols, service lines, performance measurement and reporting, and quality improvement. The information technology interoperability score from the National Survey of Physician Organizations constitutes the 10th item in the unit-weighted functional integration score.

Each domain was represented by a score that reflected the proportion of component items reported to be present in an individual medical group, where 100 reflected the presence of all items and 0 reflected none. The domains seemed to represent conceptually different features, and that was verified by our analysis of the component questions through factor analysis. Therefore, it would have been inappropriate to combine them in an overall score. Internal consistency reliability was as follows: α = .82 for the structural index, α = .92 for financial risk, and α = .80 for the functional index. The PPC-RS was scored in a similar way, with a score for each of 5 domains representing the proportion of component items reported to be present and with 100 as the highest score possible. However, in this case there is conceptual similarity among domains, so a total score is calculated as the mean of 5 domain scores.

The analysis first described the mean and variance of medical group scores in each domain of integration and the PPC-RS, as well as the total PPC-RS score. Pearson product moment correlations were calculated between these integrations and the PPC-RS scores. Finally, a linear multiple regression analysis was performed predicting the total PPC-RS score from each integration domain and then with adjustment for covariates of total number of physicians, percentage of primary care physicians, whether insurance for most patients was provided through a group’s own plan, American Medical Association (AMA) region, percentage of Medicaid patients, and ownership. This study was reviewed, approved, and monitored by the appropriate institutional review board.

Among 111 medical directors who agreed to complete the survey, 99 surveys were returned, but 1 survey was removed from the analytic sample because the respondent did not offer primary care, and another survey was removed because the organization did not deliver medical services. This resulted in an analytic data file of 97 medical groups, for an adjusted response rate of 89.0% (97 of 109) or 80.2% (97 of 121) of the eligible groups contacted. Their locations were widely distributed across the United States, with 5 to 8 medical groups each in 7 of 9 AMA regions but with 22 in the northeast central region and 27 in the Pacific region. The organizational characteristics of these medical groups are listed in Table 1. Most were owned by the physicians or by a health system, and only 14.4% had most of their patients covered by a single insurance plan. There was considerable variation in the number of physicians and care sites. There was also great variation in the proportion of physicians in primary care and the proportion of patients covered by various types of insurance. Although almost all medical groups had some patient record data in an electronic form, only 36.1% had complete paperless electronic medical records. There was a high degree of variation among these groups in the extent to which they reported having the practice systems constituting the domains of the CCM. Only the healthcare organization domain was consistently present to a high degree.

Responses to the integration domains are given in Table 2, demonstrating a substantial degree of variation among these large medical groups. Most of the groups provided at least 6 of 9 service types in the structure domain, but only 40.2% assumed any financial risk (eg, for professional services). Groups providing fewer than half of 9 service types had lower financial risk scores than those providing 5 or more service types (financial risk means of 11.2% vs 36.8%; 

 = .002). Groups providing fewer than half of the service types also had slightly lower functional integration scores than those providing 5 or more services (functional integration means of 42.3 vs 52.9; 

Associations between integration composites and the PPCRS systems composites are given in Table 3. Each measure of integration was significantly associated with the total PPC-RS score and with at least 3 domain scores, but the strongest  relationships for the PPC-RS domains were with the functional index of integration. The healthcare organization and clinical information systems domains were the most poorly related to integration.

Table 4 gives the results of linear regression analyses predicting the PPC-RS total score from each integration domain separately, while controlling for several covariates. The magnitude of the regression coefficient and 

 is substantially reduced in the adjusted model for the financial score but less so for the structural and functional integration scores. These adjusted coefficients remain significant (

 <.05) for structural and functional integration but not for the financial domain (

The strong relationship between practice systems and functional integration is most easily seen in the Figure. A least squares regression line drawn through this scatterplot demonstrates that medical groups with more practice systems clearly tend to be at the higher end of the functional integration composite. Comparing medical groups in the lowest and highest quartiles on each of the integration scores showed a mean difference in the PPC-RS total scores of 12 for structure, 8 for finance, and 22 for function.

This approach to measuring structural, financial risk, and functional aspects of integration produces scores with substantial variation among these large medical groups. Moreover, these scores correlate well with the presence of CCM systems. Although previous findings demonstrated that the PPC-RS scores correlate well with process and outcome performance measures for diabetes care quality,

 further work is needed to be more confident that these systems scores are a valid measure of performance for various chronic conditions.

A study of association between integration and quality was conducted by Mehrotra et al.

 They demonstrated that large integrated medical groups were more likely than individual practice associations to have an electronic medical record, quality improvement programs, and higher preventive services rates on 4 measures. However, these groups did not have higher scores on asthma control medications or β-blocker use after heart attacks. Unfortunately, the authors’ definition of integrated medical groups was limited to “centralized organizations in which physicians are employees or participants in a partnership arrangement.”

 noted that these findings may simply reflect large size rather than any relationship between integration and quality. Gillies et al

 also found an association between health plans that contracted with staff- and group-delivery systems and their scores on Health Employer Data and Information Set measures. To date, the only other published study to test associations between structure and quality performance measures is that by Keating et al,

 who found only small and marginally significant differences in diabetes care quality among 135 Minnesota practices based on financial arrangements or involvement in health plan programs.

Shortell and Hull wrote that “[e]arly evidence suggests that organized delivery systems that are more integrated have the potential to provide more accessible coordinated care across the continuum…than less integrated delivery forms.”

One approach to better coordination of care is to organize care delivery around conditions or service lines rather than by medical specialty, as has traditionally been the case. Parker et al

 described this development and identified challenges in this approach, including decision-making blocks and difficulties in providing service line managers with sufficient authority. Another approach is to implement the practice systems that are identified and measured in the PPC-RS used for this study. The PPC was developed primarily to evaluate the implementation of the CCM by Bodenheimer et al,

 a model that is also relevant for preventive services and for care of complex patients.

 However, if it can be shown that fragmentation between sites of care produces lower quality and higher cost, it will be necessary to implement the CCM and systems integration across health systems operating units and not just within them.

This approach has important limitations. The main limitation is that, in the absence of a common set of performance measures, we used the reported presence of practice systems as a surrogate measure of quality. When a standardized set of performance metrics becomes available at the level of medical groups, it will be important to substantiate the demonstrated relationships. Across medical groups with a standardized measure of performance, our surrogate measure of practice systems is correlated with process and outcome measures of quality of care for diabetes,19 but neither the correlations nor the systems survey is perfect.

 However, the PPC-RS has been validated, and it is the primary tool for verifying whether a medical practice can be considered a medical home for various demonstration projects. Some investigators might be concerned that the functional integration composite also measures systems aspects of organization. Although the latter systems measures (Table 2) address agreements or protocols across organizational units rather than processes within a single unit, it is possible that a medical group with practice systems internally might also be more likely to enter into such agreements across units. The findings would have been stronger if the PPC-RS measured not only the presence of systems but also the frequency and intensity of use, but it does not, and we believed that the respondent burden of adding such questions would have been too great. Finally, the findings might apply only to groups as large as those in this study. Further research is needed to investigate the frequency and effects of virtual or contractual integration by the much smaller groups that provide most US healthcare.

This demonstration of a relationship between integration and quality provides support for encouragement of integration. Further studies and reactions to these measures of integration are needed, as are other studies of the relationship using various measures of the 6 dimensions of quality identified by the Institute of Medicine, namely, safety, timeliness, effectiveness, efficiency, equity, and patient-centeredness.

 The fact that structural integration has a much weaker relationship to systems than does functional integration reaffirms the importance of going beyond assembling units providing various types of care. One must also work at making that assemblage function in a coordinated way. This is an area where selected case studies of medical groups with high and low levels of functional integration might be instructive. While our results suggest that functional integration (in particular) seems to be associated with higher process management scores, further work is needed on how different types of physician organizations might be able to achieve functional integration and how functional integration in turn might be associated with better outcomes of care in terms of quality and cost.

 From HealthPartners Research Foundation (LIS, SEA), Minneapolis, MN; School of Public Health (SMS, RRG), University of California—Berkeley, Berkeley, CA; Council of Accountable Physician Practices (NT, MRY), Oakland, CA; and National Committee for Quality Assurance (LGP, SHS), Washington, DC.

 This study was made possible by a grant from Novartis to the Council of Accountable Physician Practices.

Integrated care systems allegedly provide better care. This study of 97 large medical groups nationally provides suggestive evidence that this may be true.


L. Gregory Pawlson, MD, MPH

Articles

A Comparison of Relative Resource Use and Quality in Medicare Advantage Health Plans Versus Traditional Medicare

Classification of Health Plans Based on Relative Resource Use and Quality of Care

Clinical Guidelines and Performance Measures

Is Integration in Large Medical Groups Associated With Quality?