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The American Journal of Managed Care January 2011
Hypertension Treatment and Control Within an Independent Nurse Practitioner Setting
Wendy L. Wright, MS; Joan E. Romboli, MSN; Margaret A. DiTulio, MS, MBA; Jenifer Wogen, MS; and Daniel A. Belletti, MA
Relationship Between Short-Acting -Adrenergic Agonist Use and Healthcare Costs
Harris S. Silver, MD; Christopher M. Blanchette, PhD; Shital Kamble, PhD; Hans Petersen, MS; Matthew A. Letter, BS; David Meddis, PhD; and Benjamin Gutierrez, PhD
Healthcare Costs and Nonadherence Among Chronic Opioid Users
Harry L. Leider, MD, MBA; Jatinder Dhaliwal, MBA; Elizabeth J. Davis, PhD; Mahesh Kulakodlu, MS; and Ami R. Buikema, MPH
A System-Based Intervention to Improve Colorectal Cancer Screening Uptake
Richard M. Hoffman, MD, MPH; Susan R. Steel, RN, MSN; Ellen F. T. Yee, MD; Larry Massie, MD; Ronald M. Schrader, PhD; Maurice L. Moffett, PhD; and Glen H. Murata, MD
Relationship Between Patient Satisfaction With Inpatient Care and Hospital Readmission Within 30 Days
William Boulding, PhD; Seth W. Glickman, MD, MBA; Matthew P. Manary, MSE; Kevin A. Schulman, MD; and Richard Staelin, PhD
Effects of Health Savings Account Eligible Plans on Utilization and Expenditures
Mary E. Charlton, PhD; Barcey T. Levy, PhD, MD; Robin R. High, MBA, MA; John E. Schneider, PhD; and John M. Brooks, PhD
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Health Plan Resource Use Bringing Us Closer to Value-Based Decisions
Sally Elizabeth Turbyville, MA, MS; Meredith B. Rosenthal, PhD; L. Gregory Pawlson, MD; and Sarah Hudson Scholle, DrPH
Economic Model for Emergency Use Authorization of Intravenous Peramivir
Bruce Y. Lee, MD, MBA; Julie H. Y. Tai, MD; Rachel R. Bailey, MPH; Sarah M. McGlone, MPH; Ann E. Wiringa, MPH; Shanta M. Zimmer, MD; Kenneth J. Smith, MD, MS; and Richard K. Zimmerman, MD, MPH
High-Deductible Health Plans and Costs and Utilization of Maternity Care
Katy Backes Kozhimannil, PhD, MPA; Haiden A. Huskamp, PhD; Amy Johnson Graves, MPH; Stephen B. Soumerai, ScD; Dennis Ross-Degnan, ScD; and J. Frank Wharam, MB, BCh, MPH
High-Deductible Health Plans and Costs and Utilization of Maternity Care
Katy Backes Kozhimannil, PhD, MPA; Haiden A. Huskamp, PhD; Amy Johnson Graves, MPH; Stephen B. Soumerai, ScD; Dennis Ross-Degnan, ScD; and J. Frank Wharam, MB, BCh, MPH
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Brenda R. Motheral, PhD
Economic Model for Emergency Use Authorization of Intravenous Peramivir
Bruce Y. Lee, MD, MBA; Julie H. Y. Tai, MD; Rachel R. Bailey, MPH; Sarah M. McGlone, MPH; Ann E. Wiringa, MPH; Shanta M. Zimmer, MD; Kenneth J. Smith, MD, MS; and Richard K. Zimmerman, MD, MPH

Health Plan Resource Use Bringing Us Closer to Value-Based Decisions

Sally Elizabeth Turbyville, MA, MS; Meredith B. Rosenthal, PhD; L. Gregory Pawlson, MD; and Sarah Hudson Scholle, DrPH

There is a critical need for comparative information about plan resource use to support value-based purchasing efforts.

Objective: To examine commercial health plan variation in resource use for members with diabetes and its relationship to the quality of care for these members.

 

Study Design: Cohort study using National Committee for Quality Assurance (NCQA) Healthcare Effectiveness Data and Information Set data submitted to the NCQA in 2007, reflecting 2006 health plan performance. Data are submitted to the NCQA by plans based on claim and administrative data; medical record data may be used to supplement missing claim data.

 

Methods: Composite measures for diabetes quality and resource use (total medical care observed-to-expected [O/E] and pharmacy O/E variables) were estimated. Descriptive statistics, Pearson correlations between quality and resource use, and 90% confidence intervals around each health plan's composite quality and resource use results were estimated.

 

Results: Vast variation was found for both quality and resource use. Medical care resource use has no relationship to quality for diabetes. Pharmacy resource use has a moderate positive relationship to quality.

 

Conclusions: Measures of resource use, along with measures of quality, can be produced and provide important information about the value of a health plan. Although this analysis did not determine causality, the lack of relationship between quality and resource use suggests that plans could improve quality of care without large increases in resource use or could achieve the same level of quality with less resource use.

 

(Am J Manag Care. 2011;17(1):68-74)

Commercial health plan variation in resource use for members with diabetes was examined using National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (HEDIS) data.

 

  • Medical resource use had no relationship to quality of care for diabetes; pharmacy resource use has a moderate positive relationship to quality of care.

 

  • The lack of relationship between quality and resource use suggests that plans could improve quality of care without large increases in resource use or could achieve the same level of quality with less resource use.

 

  • There is a critical need for comparative information about plan resource use to support value-based purchasing efforts.
Those who want to improve healthcare affordability and healthcare quality in the United States often look to value-based purchasing principles for guidance. Because they involve specific consideration of the design and operation of health benefit exchanges, these principles will certainly be critical elements of many healthcare market reforms. Employers and other payers need comparative information on both cost and quality of care to select and manage health plans and health plan providers. For nearly 2 decades, purchasers have used the quality-of-care reports of the Healthcare Effectiveness Data and Information Set (HEDIS) to inform plan choice, to encourage quality improvement by plans, and more recently, to offer performance incentives. On the cost side of the health plan value equation, however, purchasers observe only the proffered premiums or, in the case of self-insured products, total claims paid plus the administrative fees for their employees. Although some purchasers access and analyze more detailed data (eg, at the practice level), they may be limited in their ability to compare and contrast their resource use in a manner that informs value-based decision making.

Premiums are clearly salient to employers and purchasers in short-run decision making, but they also mask different forces that drive benefit costs. It is our contention that some of these forces could be addressed by purchasers and policy makers if they had better information. In particular, premiums commingle differences in utilization of healthcare resources, as well as differences in benefit design, underwriting cycles, and provider fee schedules. These masked differences have substantial impact on how providers and patients access and use the healthcare system, thus increasing or decreasing utilization of health services.1,2 Further, the offered premiums or actual costs preclude benchmarking and, given the wide variation in benefits offered, create additional barriers to value-based purchasing. To assess and compare the value added by a plan or self-insured product, we are in critical need of an approach that allows for resource use benchmarking and comparison among plans. This need is not limited to HMO plans  precisely because differences in less transparent plan activities (including benefit structure) contribute to utilization; thus, their influence on utilization and quality should be examined and assessed, and plans should be compared with a relevant group of peers.

Health system efficiency is often defined as the level of real-ized attainment (eg, an actual attained level of quality) compared with the maximum that could be achieved by a given level of resource use.3 Thus, effective purchasing and oversight of health benefits requires that purchasers be able to disentangle resource use from other factors to identify drivers of spending growth and make meaningful assessments of efficiency or value. Both private purchasers and public policy makers have an interest in understanding not only the overall price charged or paid for health benefits, but also how the price and volume of services interact.

The National Committee for Quality Assurance (NCQA) has worked with stakeholders to develop a set of HEDIS relative resource use (RRU) measures that complement existing HEDIS quality measures. These RRU measures capture resources (inpatient and outpatient services) used by a plan in the care of populations with certain chronic conditions (eg, diabetes) and estimate adjusted benchmarks to support comparative reporting. The measures are defined in a way that corresponds to existing related HEDIS quality measures, allowing simultaneous assessment. In this vein, it is important to note that 6 disease states, including diabetes, cardiovascular disease, asthma, chronic obstructive pulmonary disease, lower back pain, and hypertension, account for approximately 60% of all medical care expenditures by commercial plans.4 The rise in chronic diseases has been directly linked to the increase in healthcare spending directed toward treating them.5 The American Diabetes Association 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.6

The NCQA began collecting 3 of the 6 RRU measures in 2007 and all 6 measures in 2008. The NCQA began public reporting of plan-level results, along with related quality results, in late 2010. Coupled with premium or expenditure data, linked quality and resource use data allow payers (1) to gain a better understanding of cost-quality trade-offs, (2) to examine and compare types of resource use (eg, inpatient vs pharmacy), (3) to help plans identify areas where they may be able to improve quality while reducing total expenditures or holding them level, and (4) to select more efficient plans. We focused on the HEDIS RRU and quality results for patients with diabetes in advance of any public reporting at the plan level and explored implications for public reporting and policy decision making.

DATA AND METHODS

This analysis used HEDIS data on diabetes care submitted by commercial HMO plans to the NCQA in 2007, reflecting health plan performance in 2006. (Prior to submitting  performance data to the NCQA, health plans submit to an independent audit review, which assesses the validity of the data.) In 2007, 205 commercial HMO plans submitted performance data to the NCQA.

To estimate the overall quality of diabetes care for commercial HMO plans, we calculated the average performance across 7 HEDIS diabetes-process or intermediate-outcome measures and indexed each plan’s quality composite rate against the mean HMO commercial plan performance. There are 7 diabetes quality composite components: glycosylated hemoglobin (A1C) testing; poor A1C control (>9.0%) (this rate is inverted such that a higher rate indicates better performance); retinal eye exam; low-density lipoprotein cholesterol screening; low-density lipoprotein cholesterol control (<100 mg/dL); medical attention for nephropathy; and blood pressure control (<130/80 mm Hg).

The HEDIS RRU measures include the application of standardized prices to account for observed variance in the intensity of resource use (achieved by weighing the services) and allows for plan-to-plan comparison. (This approach supports consistent comparisons across all data—including patients, health plans, and geographic areas—as if the data were derived from a single source using a single approach for classifying and pricing services. The pricing algorithms are designed to reflect service pricing levels for managed care organizations for the most recent time period.) The episode length for the Relative Resource Use for People with Diabetes measure is 12 months, and resource use is calculated for all services, whether or not they relate to the diabetes condition.

The HEDIS RRU measurement risk adjustment approach adjusts each plan’s submitted resource use based on the distribution of its diabetes cohorts; the cohorts are based on a patient’s major clinical condition, clinical category, presence of comorbidity, and age and sex (Table 1). (A detailed specification for each of the HEDIS quality and resource use  component measures is available from the NCQA.) An example of a member cohort would be members with type 1 diabetes mediwith no comorbid conditions who are female and between the ages of 18 and 44 years.

For each diabetes cohort stratum, plans report to the NCQA the number of members and their respective monetized resource use during the measurement period. The NCQA estimates the average resource use amount for each cohort within a plan peer group (eg, commercial HMO plans). Peer groups can be national or regional, are always restricted to products (Medicare, commercial, and Medicaid), and compare HMOs with HMOs and preferred provider organizations (PPOs) with PPOs. An adjusted peer amount represents the resource use amounts expected from a plan based on the mix and size of their reported member cohorts using an indirect standardization technique.

In more detail, within the major clinical condition (diabetes), a member is assigned to 1 clinical category based on the specified hierarchies. For example, for diabetes, a member is assigned to 1 of 2 clinical categories using this hierarchy: diabetes type 1 or diabetes type 2. Members are then assigned to a hierarchy category based on the presence of a relevant and specified comorbid condition (eg, cardiovascular condition, chronic kidney disease). For the diabetes RRU measure, members are assigned into 1 of 4 hierarchical groupings based on their clinical and comorbid condition status and further stratified into 1 of 32 member cohort groups based on their sex and age. Across all the RRU measures, members with specified dominant medical conditions (active cancer, end-stage renal disease, HIV/AIDS, or organ transplant) are excluded. To avoid skewed results stemming from rare and costly events, the specifications include a member cap amount within each of the service categories (eg, inpatient facility), and members who exceed the monetized resource use are assigned the specified capped amount.

To examine the RRU among members with diabetes, we used a composite medical care observed-to-expected (O/E) variable and an ambulatory pharmacy O/E variable that include all services within specific categories for patients with a diagnosis of diabetes. The detailed characteristics of these measures were reported in a prior article and in a report  released by the Centers for Medicare & Medicaid Services.4,7 The service categories of the RRU measure include the following total medical care composite: (1) inpatient facility services, (2) procedure and surgery services (inpatient and outpatient), (3) evaluation and management services (inpatient and outpatient), and (4) pharmacy (ambulatory).  Research indicates that these 4 service categories account for approximately 75% of the total expenditures for patients with diabetes.4

The total medical care RRU index is the ratio of the plan’s observed per member per month (PMPM) resource use, summarized across the service categories, compared with the expected PMPM resource use across the service categories for the specified condition. The pharmacy variable is each plan’s observed pharmacy PMPM divided by its respective expected pharmacy PMPM resource use. The ratio of each plan’s O/E result is indexed to the mean commercial HMO plan performance. (We use the term HMO to describe the insurance products for which the health plans [ie, the corporate entities] submitted the data we analyzed here. Some health plans separately submitted RRU data for their PPO products, but these data were not within the scope of this article.) Resource use by inpatient facilities includes services provided by the facility during a patient stay. Professional services, such as those provided by an internist or a surgeon, are included in the evaluation-and-management and procedure-and-surgery service categories. Pharmacy services include all prescriptions rendered in an ambulatory setting and are based on National Drug Codes, thus differentiating between name-brand and generic drugs and taking into account the days of supply and the amount of medication (ie, dosage and pill size).

 
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