National evaluation of adherence to β-blocker therapy for 1 year after acute myocardial infarction in patients with commercial health insurance

Article Outline

• Abstract
• Methods
  • Study sample
  • Outcome measures
  • Data collection
  • Statistical methods
• Results
• Discussion
  • Limitations
  • Future initiatives
• Acknowledgment
• Appendix A. Participating health plans
• Appendix B. Members of the Cardiac and/or Measurement Work Groups by organization
• References
• Copyright

Background

Quality measures of evidence-based medications post–myocardial infarction have focused on prescription at hospital discharge. Yet survival benefits of these medications are best realized with sustained therapy. We sought to examine long-term β-blocker adherence over the first year after myocardial infarction in patients with commercial health insurance and prescription drug benefits.

Methods

This multicenter analysis examined health plan records from members of 11 health plans who had myocardial infarction in 2001, survived at least 1 year, and maintained insurance coverage (N = 17035). The primary outcome measure was adherence to β-blockers (defined as prescription claims covering ≥75% of days) for 360 days post-discharge. We also examined associations with adherence—time from discharge, health plan product (commercial or Medicare + Choice [M + C]), age (35-64 or ≥65), sex, and region.

Results

For 360 days after discharge, only 45% of patients were adherent to β-blockers, with the biggest drop in adherence between 30 and 90 days. In a multivariable model, statistically significant predictors of lower adherence were participation in M + C product, residence in the Southeast, and age (driven by young participants in M + C and young females in commercial products).

Conclusions

In a population of patients with health insurance and prescription drug coverage, adherence to β-blocker therapy in the first year after myocardial infarction is poor, indicating that factors other than medication cost are important determinants of long-term adherence. Quality improvement initiatives focused on long-term adherence are needed to realize maximal benefit from medical therapy in post–myocardial infarction patients.

Approximately 7.2 million American adults have a history of myocardial infarction.¹ We have known since the mid-1980s that β-adrenergic blocking medications (β-blockers) administered long-term after myocardial infarction improve survival and reduce the risk of reinfarction. In a 1985 meta-analysis of randomized trials, Yusuf et al² estimated that the benefit of administering β-blockers long-term after myocardial infarction (1-4 years of follow-up) would confer a 23% relative improvement in survival and a 26% relative decrease in nonfatal reinfarction. These conclusions have since been corroborated in updated meta-analyses³, ⁴ by a recent randomized trial of post-infarction patients with left ventricular dysfunction in the thrombolytic/interventional era⁵ and in observational trials using large databases.⁶

Multiple studies have demonstrated that despite these convincing data, β-blockers have been underprescribed at discharge after myocardial infarction.⁷, ⁸ Recently, several national organizations have instituted quality initiatives to increase prescribing of evidence-based medications at discharge after myocardial infarction. These include the American College of Cardiology (ACC)'s Guidelines Applied in Practice (GAP), the American Heart Association (AHA)'s Get with the Guidelines, the Joint Commission on Accreditation of Health Care Organizations (JCAHO)'s quality check, and the National Committee for Quality Assurance (NCQA)'s Health Plan Employer Data and Information Set (HEDIS).⁹, ¹⁰, ¹¹, ¹² These programs currently report improved rates of β-blocker prescription at hospital discharge. The NCQA's HEDIS measure of β-blocker prescription within 7 days of discharge improved from 63% in 1996 to 93% by 2002.¹²

There has been considerably less study of long-term outpatient use of β-blockers in post-infarction patients.⁶, ¹³, ¹⁴, ¹⁵ Yet the full survival benefits of β-blockers can best be realized with sustained, chronic therapy.², ³, ⁵, ¹⁶

While poor adherence to medications is likely multifactorial, common concerns are the cost of medications and the availability of prescription benefits.¹⁷, ¹⁸ Therefore, we examined long-term medication adherence in a national sample of post–myocardial infarction patients with prescription drug coverage. Our goal was to describe long-term, post–myocardial infarction use of β-blockers, temporal trends in adherence, and factors that may be associated with lower long-term adherence.

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Methods 

Data were contributed by 11 health plans (Appendix A) belonging to the Council for Affordable Quality Healthcare (CAQH), a national, not-for-profit organization of health plans and networks organized to promote collaborative initiatives to simplify administrative processes and improve quality of care. A work group of participating health plans developed common technical specifications for data collection. Under these specifications, each plan analyzed their individual patient data and submitted aggregated, de-identified data to the CAQH. The CAQH provided aggregated data to investigators at Duke University Medical Center for analysis. The Duke University Health System's Institutional Review Board granted exemption from review for this study.

Study sample 

Patients were included in the study if they were ≥35 years old, were hospitalized for an acute myocardial infarction (ICD-9 codes 410.x1) in calendar year 2001, and had survived for at least 1 year after myocardial infarction. For patients with multiple myocardial infarction admissions, data were reported only for the first event in 2001. Patients were excluded if they were not continuously enrolled in their health plan for at least 1 year after the index event, or if they did not have prescription drug benefits. Patients were also excluded if either inpatient or outpatient records indicated the following contraindications to β-blocker therapy: a diagnosis of hypotension (ICD-9 code 458), bradycardia (ICD-9 code 427.81), or heart block greater than first degree (ICD-9 codes 426.0, 426.12, 426.13, 426.2-4, 426.51-4, and 426.7) that occurred either during the index hospitalization or during the 360 days after discharge.

Outcome measures 

The primary outcome measure in this observational study was adherence to β-blocker for the 360 days after discharge for MI. Secondary outcome measures included adherence to β-blocker over 30, 90, 180, and 270 days after discharge and the association of adherence over 360 days with the following predictor variables: age group, sex, health plan product (commercial vs Medicare + Choice [M + C]), and geographic region (Northeast, Southeast, Midwest, and West). For analysis, all commercial health plan products (health maintenance organization [HMO], preferred provider organization [PPO], point of service [POS], and indemnity) were combined due to overlap in product structures and small numbers of patients in some products. Adherence during discrete intervals (0-30, 31-90, 91-180, 181-270, and 271-360 days after infarction) was also described to assess the timing of drops in adherence. Documentation of prescription at discharge was not available in this data set.

Pharmacy claims were evaluated from 90 days before discharge for the qualifying myocardial infarction (to capture preexisting prescriptions) to 360 days after discharge. Any β-blocker on the HEDIS list of β-blockers¹⁹ was considered; changes in specific β-blocker medication and dosage were not tracked. Similar to methods used by other investigators,²⁰ “covered days” was defined as the actual number of calendar days covered by a purchased prescription of β-blocker medication within each specified time interval (ie, a 90-day supply dispensed on the 45th day had 46 days counted in the 0-90 day interval and 44 days in the 91-180 day interval). “Proportion of days covered (PDC)” was defined as the number of covered days of β-blocker divided by the number of calendar days in the measurement period. Patients were considered “adherent” if the PDC was ≥75%, a number similar to other studies of adherence in the literature.¹⁴, ²⁰, ²¹

Data collection 

Health plans aggregated data by type of health plan product (HMO, POS, PPO, indemnity plan or M + C) and by two age groups, 35 to 64 and ≥65 years. They dichotomously reported the number and percentage of patients who were 0 to 74% versus 75% to 100% adherent, based on the proportion of covered days in the relevant period. Age was determined as of December 31, 2001. Health plans also provided mean age of patients in their plan by commercial and M + C products. For descriptive purposes only, the plans used claims data to provide the number and percentage of patients with the following comorbidities: chronic obstructive pulmonary disease, diabetes, heart failure, hypertension, hyperlipidemia, or renal disease.

Statistical methods 

Because health plans provided only aggregate data, estimated mean age for the overall population and each health plan product was calculated as a weighted average of the mean age from each health plan. Other population characteristics were summarized by frequencies and percentages. The proportion of patients adherent by health plan product (commercial vs M + C) was summarized separately for patients aged ≥65 years and 35 to 64 years. Comparisons between groups were evaluated using χ² tests. Adherence over 360 days by region was described for all patients and separately for patients in commercial and M + C products. We used a multivariable logistic regression model to analyze the association of the predictor variables (age group, sex, region, and product) with 360-day adherence. Comorbidities were not included in the model because individual patient data were not available.

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Results 

Between January and December 2001, a total of 17035 patients covered by 11 health plans and prescription drug programs had myocardial infarction and subsequently survived for at least 1 year after discharge. These patients represented 48 states and the District of Columbia. Population characteristics are shown in Table I. The proportion of women among patients ≥65 years was considerably larger than in patients aged 35 to 64 years (44.7 vs 23.4%, respectively, P < .0001). In the overall population, mean age was 60.2 years (SD 11.4) with a range of 35 to 100 years. The mean age for patients in commercial products was 56.3 years (SD 8.8), and the mean age for M + C was 75.0 years (SD 7.9).

Table I. Population characteristics

	Number and percentage of patients by age group
	Total	35-64 y	65+ y
No. of patients	17035 (100)	12183 (71.5)	4852 (28.5)
Sex
Male	12013 (70.5)	9329 (76.6)	2684 (55.3)
Female	5022 (29.5)	2854 (23.4)	2168 (44.7)
Age⁎, mean (SD)	60.2 (11.4)
Region
Northeast	8876 (52.1)	5649 (46.4)	3227 (66.5)
Midwest	3828 (22.5)	3336 (27.4)	492 (10.1)
West	2229 (13.1)	1512 (12.4)	717 (14.8)
Southeast	2102 (12.3)	1686 (13.8)	416 (8.6)
Type of health plan product
Commercial	13616 (79.9)	11767 (96.6)	1849 (38.1)
HMO	4822 (28.3)	4338 (35.6)	484 (10.0)
PPO	4601 (27.0)	4039 (33.2)	562 (11.6)
POS	2714 (15.9)	2421 (19.9)	293 (6.0)
Indemnity	1479 (8.7)	969 (8.0)	510 (10.5)
M + C	3419 (20.1)	416 (3.4)	3003 (61.9)
Comorbidities† (n=13235)
Lipid disorder	8604 (65.0)
Hypertension	8088 (61.1)
Heart failure	4780 (36.1)
Diabetes	4151 (31.4)
COPD	3684 (27.8)
Renal disease	787 (5.9)

COPD, Chronic obstructive pulmonary disease.

The largest number of patients was from the Northeast, followed by the Midwest. Approximately 20% of the population had M + C; 80% had commercial health plan products (28% HMO, 27% PPO, 16% POS, 9% indemnity). For patients aged 35 to 64 years, only 3.4% had M + C; for patients ≥65 years, 61.9% had M + C.

Population comorbidities indicate considerable disease burden (Table I). Almost two thirds of patients had a lipid disorder or hypertension. Nearly one third had diabetes; 36% had a diagnosis of heart failure.

Figure 1 shows adherence over the 360 days after discharge. In the period from discharge to 30 days after infarction, only 69% of patients were adherent to β-blocker medication. This proportion of adherent patients decreased progressively over longer cumulative periods. Over the full year after infarction, only 45% of patients were adherent.

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• Figure 1. 

  National adherence by cumulative time intervals.

Data on national adherence for discrete time intervals (not shown) demonstrated that the greatest falloff in medication adherence occurs early, with a 15% absolute drop between the 0-30 day and 31-90 day periods (69%-54%, P < .0001). In both age groups and for every period, the proportion of patients adherent is considerably lower for patients in M + C products than in commercial products (Figure 2, A and B). In patients aged 35 to 64 years, only 24% of patients in M + C products were adherent to β-blockers over 360 days versus 47% of patients in commercial products (P < .0001). In patients aged ≥65 years, 39% of patients in M + C products and 49% of patients in commercial products were adherent over 360 days (P < .0001).

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• Figure 2. 

  National adherence for cumulative time intervals by health plan product for patients aged 35 to 64 (A) and ≥65 years (B).

Figure 3 shows adherence over 360 days by geographic region for the total population and separately by health plan product (commercial vs M + C). Overall and for both commercial and M + C products, adherence over 360 days was poorest in the Southeast.

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• Figure 3. 

  Adherence over 360 days by region and product. Kruskall-Wallis test: All patients: P < 0.0001; Medicare + Choice: P = 0.04.

Table II lists in order of importance (decreasing χ²) the factors determined by logistic regression analysis to be associated with 360-day adherence. Product, region, and age group were statistically significant predictors of adherence over 360 days. The impact of product and region in the model confirms the descriptive results shown in Figure 2, Figure 3.

Table II. Logistic regression results: factors associated with 360-day adherence

Main effects	Wald χ2 statistic	P
Product	67.5	<.0001
Region (3 df)	52.5	<.0001
Age Group	18.7	<.0001
Sex	0.1	.78
Interactions
Age group⁎sex	0.2	.66
Age group⁎product	8.3	.004
Sex⁎product	0.1	.78
Age group⁎sex⁎product	1.2	.28
Contrasts of interest	Odds ratio	95% CIs for odds ratio	P
Region: Southeast vs Northeast⁎	0.70	0.63-0.77	<.0001
Region: West vs Northeast⁎	0.97	0.88-1.07	.52
Region: Midwest vs Northeast⁎	0.98	0.91-1.06	.61
F vs M⁎ within M + C age 35-64	1.11	0.58-2.14	.75
F vs M⁎ within commercial age 35-64	0.87	0.80-0.95	.002
F vs M⁎ within M + C age 65+	0.99	0.86-1.15	.92
F vs M⁎ within commercial age 65+	1.15	0.95-1.38	.16
Age 65+ vs 35-64⁎ within M + C males	2.02	1.56-2.63	<.0001
Age 65+ vs 35-64⁎ within M + C females	1.80	0.97-3.34	.07
Age 65+ vs 35-64⁎ within commercial males	0.99	0.88-1.12	.91
Age 65+ vs 35-64⁎ within commercial females	1.31	1.11-1.54	.002

Model included region and 3-way interactions of age group, sex, and product. C index = 0.56.

Overall, age is predictive in the logistic regression model. As can be seen by the Contrast test results (Table II), this is driven both by lower adherence in the younger (35-64 years) M + C participants, a unique subgroup of disabled patients and those with end-stage renal disease, and by lower adherence in younger women within commercial products.

Although the results did not show an overall effect of sex, the subgroup of women aged 35 to 64 years with commercial insurance products were less likely than men in their age group (female vs male OR = 0.87, P = .002) and less likely than older women (OR = 0.76, P = .002 for younger vs older women within commercial) to adhere to β-blockers. This sex trend was reversed for the ≥65-year-old commercial participants (women vs men OR = 1.15, P = NS).

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Discussion 

This large, national sample of patients with commercial health insurance and prescription drug coverage definitively shows poor long-term outpatient adherence to β-blockers during the first year after myocardial infarction. Our study is among the first to examine long-term adherence to preventive therapy after myocardial infarction in a predominantly young, working-age population with pharmaceutical benefits.

Several studies in the elderly/retired population have shown poor long-term adherence to β-blockers after myocardial infarction.⁶, ¹³, ¹⁴ Between 1987 and 1990, Soumerai et al⁶ found that only 21% of elderly, low-income patients without contraindications to β-blockers had one or more prescription claims for β-blocker in the 90 days after discharge. Between 1994 and 1995, in a population of Medicare patients eligible for Medicaid benefits, Butler et al¹³ found that 39% of patients discharged on β-blocker after myocardial infarction were no longer taking the drug at 1 year. In a later data set (1996-1998) of elderly patients in Quebec discharged on β-blocker after myocardial infarction, 26% of patients had not filled a prescription for β-blocker within 2 months of their 1-year anniversary after discharge.¹⁴ Using an international registry of clinical trial sites (1999-2003), Eagle et al¹⁵ found among the 65% of patients with follow-up 6 months after discharge that 12% had discontinued β-blocker medication. Long-term data for another evidence-based medication indicated post-infarction, HMG-coA reductase inhibitors (statins), also show poor long-term adherence. Jackevicius et al²² found 2-year adherence rates to statins after acute coronary syndrome of only 40%.

Because all patients in this population had prescription drug coverage, cost should not have limited adherence. Still, we found disturbingly low rates of long-term adherence. These results have implications to the new Medicare prescription drug benefit. A national payment plan alone likely will not remedy poor long-term adherence to drugs that reduce morbidity and mortality after myocardial infarction.

Factors that determine long-term adherence are more complex and inherently different from those that determine initial prescription at discharge after myocardial infarction.²³, ²⁴ Upon discharge, the patient enters into the system of chronic care, which across the nation is much less organized and standardized than hospital-based care.²³ In addition to targeting community physicians to maintain prescribing, interventions to improve adherence must target patients and their family members. These individuals have the primary influence over decisions to maintain prescribed therapy. Factors that influence these decisions include their level of understanding about the benefits and risks of prescribed therapy, the complexity and duration of their medication regimen, their health beliefs, adverse effects they may attribute to their medication, cost, and inconvenience.²⁴ Additional factors are presence and accuracy of communication between hospital caregivers and community physicians who care for the patients after discharge.²⁵ Given the complexities of long-term adherence, it is not surprising that McDonald's systematic review of studies of medication adherence showed little relationship to sociodemographic factors such as age, sex, race, intelligence, and education.²⁴ Although our results did not show an overall effect of sex, it is interesting that younger women in the commercial products were less adherent than men in the same age group and less adherent than older women within commercial products. This finding is particularly intriguing given the epidemiologic data showing that younger, but not older, women who survive a hospitalization for myocardial infarction have higher 2-year mortality than men.²⁶ Younger women may perceive that they are less prone to heart disease. For exactly this reason, the AHA has mounted a “Go Red for Women” campaign, and the National Heart, Lung, and Blood Institute has started a “Heart Truth Campaign,” both designed to educate women about their significant risk of heart disease.²⁷, ²⁸

Our data show that the falloff in adherence is early. This suggests that to maintain β-blocker therapy long-term, interventions must be mounted in the first month or two after myocardial infarction.

The lower adherence in the Southeast is consistent with the regional results at hospital discharge found in the Cooperative Cardiovascular Project.⁷ Reasons for lower adherence in the Southeast need to be explored in further research to identify factors involved.

The low rates of adherence in the small number of Medicare patients aged 35 to 64 years reflect the behavior of a special population of patients eligible at that age for Medicare—those with end-stage renal disease or total disability. In the over-65 population, the lower adherence for patients with M + C products compared with commercial products could have several explanations. With few exceptions, the commercial health plan products among CAQH members do not have dollar caps on prescription drug coverage, whereas most of the M + C products have prescription caps or tiered co-pays for generic versus patented medications.²⁹ Data from 2003 also indicate that 60% of all M + C health plan products cover only generic drugs. While 74% of enrollees in plans with generic-only coverage have unlimited generic benefit, the remaining had an annual cap of $500 or less. Thus, patients in such plans taking patented medications or with annual prescription costs of generic products exceeding $500 will have out-of-pocket expenditures.

Tseng et al¹⁸ recently documented the impact of exceeding annual prescription caps on strategies used by M + C patients to lower prescription costs. In patients exceeding caps, they found a statistically significant increase in the proportion of patients who reported using less prescribed medication than did control patients who did not exceed caps. Although our findings of lower adherence in M + C beneficiaries are consistent with Tseng's results, another confounding factor could be at play. If a patient in M + C reaches a prescription cap or requires a patented medication, he may purchase the prescription out of pocket. The CAQH data cannot distinguish between medication not taken and medication purchased out-of-pocket where no claim is filed. An attempt to examine this issue in future research could provide useful information as we consider the effect of the Medicare prescription drug benefit with a gap in coverage once a beneficiary's total drug costs reach a $2250 annual cap.¹⁸

Limitations 

An important caveat is that these results are based purely on aggregated administrative data. Because adherence was reported dichotomously as ≥75% versus <75% PDC, it is not possible to examine degree of adherence as a continuous variable. Because individual patient data are not available, it is also not possible to associate individual patients' adherence early after discharge with later clinical outcomes. The administrative data were not validated using chart reviews. Thus, the number/proportion of patients actually prescribed a β-blocker at discharge is not documented. National HEDIS 2001 statistics indicate that 92.5% of commercial and 92.9% of M + C patients were prescribed β-blockers within 7 days of discharge. Assuming the HEDIS prescription estimate is correct, 93% of patients in this study were likely prescribed a β-blocker at discharge in 2001. The fact that only 69% of patients were adherent to β-blocker in the first 30 days suggests that approximately 24% of patients may not have initially filled their discharge prescription.

These data cannot account for doctors' verbal instructions to patients to change β-blocker dose. Verbal instruction to cut the dose in half would result in an underestimation of adherence. Any dose changes incorporated into new written or verbal prescriptions would be captured by our methods.

Adherence calculations were not adjusted for days spent in a hospital during the follow-up year. There would have been significant complexity in obtaining and programming these data, and it was thought likely to have minimal impact on adherence over a 360-day period of follow-up.

Although administrative data are limited in the conclusions that can be drawn, the advantages of this data set include its size and the fact that the claims being analyzed are derived under actual practice conditions. These data represent the entire unselected population of patients with MI in 2001 who had insurance with these health plans. The adherence of these patients would be more representative of patients in actual practice than adherence observed in a selective clinical trial population or in a registry requiring patient consent.

Future initiatives 

To reap the potential benefit of β-blockers after myocardial infarction, quality efforts must refocus on long-term outpatient care. Similar to inhospital quality programs, initiatives are needed to measure performance indicators. Pay-for-performance approaches that have been utilized in other quality efforts may be applicable for long-term β-blocker adherence as well.

The CAQH initiated a national education program, heartBBEAT for life, in collaboration with the AHA, ACC, American Academy of Family Physicians, and American College of Physicians to improve long-term adherence to β-blocker after myocardial infarction.³⁰ This program focuses on raising awareness and educating physicians, their patients, and public about the importance of maintaining β-blocker therapies post-MI.

The CAQH also shared its preliminary results with the NCQA, an organization that accredits health plans and develops HEDIS measures. The NCQA initiated a new HEDIS 2005 measure of 6-month adherence to β-blocker after myocardial infarction using CAQH methodology. The collaboration of health plans within CAQH and their interactions with NCQA on this matter are examples of collective leadership recently recommended as an approach to improve clinical quality across organizations.³¹ Previous quality measures for discharge prescription of β-blocker s (HEDIS, JCAHO, Get With the Guidelines, GAP) have been associated with marked improvements in rates of discharge prescriptions over the last 10 years. The new long-term HEDIS measure for β-blocker use after myocardial infarction will likely provide an incentive for health plans to promote long-term adherence.

For additional efforts to improve long-term adherence, the motivations of all parties (physicians, patients, health plans, pharmaceutical companies, pharmacists, and nurses) should be well aligned as we seek to improve the health and safety of patients through better long-term adherence to life-saving medications.

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We thank John M. Daniel (Duke Clinical Research Institute) for editorial assistance and Rhonda Arrington (PricewaterhouseCoopers) for the aggregation of de-identified health plan data. We also thank the chairs of the Cardiac Work Group, Richard Snyder, MD, and John P. Charde, MD, and the chair of the Measurement Work Group, Donald Fetterolf, MD, MBA, for their leadership in designing and conducting the study.

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Appendix A. Participating health plans 

Aetna, Inc

Anthem Blue Cross and Blue Shield

Blue Cross Blue Shield of Georgia

Blue Cross Blue Shield of Michigan

Blue Cross of California

CIGNA Corporation

Empire Blue Cross Blue Shield

First Health Group Corp

Health Net, Inc

Highmark Blue Cross Blue Shield

Independence Blue Cross

Oxford Health Plans, Inc

WellPoint Health Networks, Inc

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Appendix B. Members of the Cardiac and/or Measurement Work Groups by organization 

Aetna, Inc

Brian Hutcheson; Martin Kodish, MD; Joan Kostusiak, RN, MS, PNP; Meghan O'Brien McNamara; Claire M. Spettell, PhD; Janet L. Thomson, RPh

American Academy of Family Physicians

Janet Leiker, RN, MPH, CPHQ

America College of Cardiology

Amy Stern, MHS

American Heart Association

Patricia Beatty-Gonzalez; Dennis Milne

Anthem Blue Cross and Blue Shield

Joan Blackmon, MSPH, PhD; Aileen Broderick; Doug D'Amico; Marilyn Duffy, RN; Lynn Evans, LPN; Mary Hothem, RN, CPHQ; Laura Kauffman, MSPH; Jeanne Lehn, RN, MSN; Lisa Morris, RPh; Patricia Pool

AultCare

Greg Haban, MD; Frank Hayden

Blue Cross and Blue Shield Association

Allan M. Korn, MD, FACP; Inger Saphire-Bernstein

Blue Cross of California

Peter Lee; Alexis Neal; Mary Spitzer, RN

Blue Cross and Blue Shield of North Carolina

Don W. Bradley, MD; Robert T. Harris, MD; Keven Kunz

Blue Cross and Blue Shield of Georgia

Cheryl Harris, RN, CPHQ, MSHA, FAHM; Tracy Keefe; Robert McCormack, MD

Blue Cross Blue Shield of Michigan

Fred Fedorowicz, PA-C

Blue Cross Blue Shield of Missouri

Sharon Hoffarth, MD; Laura Ross; John Seidenfeld, MD

BlueCross BlueShield of Tennessee

Marisa Allen, MS; Jason Carter, MS; Beverly Franklin-Thompson, Pharm D.; Soyal Momin, MS, MBA

CAQH

Barbara S. Hoffman, PA-C, MBA; Jennifer Lis; Barbara Souder, RN, MPH, PhD

Empire Blue Cross Blue Shield

Daniel Checkman, MS; Ellen Silver, MD; John Whitney, MD; Lisa Biederman

First Health Group Corp

Valerie Reese, MD

Health Net, Inc

John P. Charde, MD; Ian T. Gocka; Lance Lang, MD; Eileen O'Connor, RN, FNP, CPHQ; Julee Oh

Highmark Blue Cross Blue Shield

Donald E. Fetterolf, MD, MBA, FACP; Donald Polito, RN, MBA; Sanford Reich, RPh, MD, FACC

Horizon Blue Cross Blue Shield of New Jersey

Linda Cruz, RN, CCM; Amy Holcomb, MPH; Vivian Keller, RN, BSN, MHA; Premila Kumar, MD; Mala Suri, MBA

Independence Blue Cross

William Bates; Richard Snyder, MD; Sue Ann Sperry, RN; Susan Tan-Torres, MD; Timothy C. Zeddies, PhD; Irene Spector

Oxford Health Plans, Inc

Diane Forte; Richard Lask, MD; Christy Patterson; Sara VanEtten, RN, MHA

WellPoint Health Networks, Inc

Kellie Bernell

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