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The American Journal of Managed Care October 2014
Quality of Care at Retail Clinics for 3 Common Conditions
William H. Shrank, MD, MSHS; Alexis A. Krumme, MS; Angela Y. Tong, MS; Claire M. Spettell, PhD; Olga S. Matlin, PhD; Andrew Sussman, MD; Troyen A. Brennan, MD, JD; and Niteesh K. Choudhry, MD, PhD
A Comprehensive Hospital-Based Intervention to Reduce Readmissions for Chronically Ill Patients: A Randomized Controlled Trial
Ariel Linden, DrPH; and Susan W. Butterworth, PhD
Physician Compensation Strategies and Quality of Care for Medicare Beneficiaries
Bruce E. Landon, MD, MBA; A. James O'Malley, PhD; M. Richard McKellar, BA; James D. Reschovsky, PhD; and Jack Hadley, PhD
Increasing Access to Specialty Care: Patient Discharges From a Gastroenterology Clinic
Delphine S. Tuot, MDCM, MAS; Justin L. Sewell, MD, MPH; Lukejohn Day, MD; Kiren Leeds, BA; and Alice Hm Chen, MD, MPH
Increasing Preventive Health Services via Tailored Health Communications
Kathleen T. Durant, PhD; Jack Newsom, ScD; Elizabeth Rubin, MPA; Jan Berger, MD, MJ; and Glenn Pomerantz, MD
The Duration of Office Visits in the United States, 1993 to 2010
Meredith K. Shaw; Scott A. Davis, MA; Alan B. Fleischer, Jr, MD; and Steven R. Feldman, MD, PhD
Evaluation of Collaborative Therapy Review to Improve Care of Heart Failure Patients
Harleen Singh, PharmD; Jessina C. McGregor, PhD; Sarah J. Nigro, PharmD; Amy Higginson, BS; and Greg C. Larsen, MD
Extending the 5Cs: The Health Plan Tobacco Cessation Index
Victor Olaolu Kolade, MD
Caregiver Presence and Patient Completion of a Transitional Care Intervention
Gary Epstein-Lubow, MD; Rosa R. Baier, MPH; Kristen Butterfield, MPH; Rebekah Gardner, MD; Elizabeth Babalola, BA; Eric A. Coleman, MD, MPH; and Stefan Gravenstein, MD, MPH
Ninety-Day Readmission Risks, Rates, and Costs After Common Vascular Surgeries
Eleftherios S. Xenos, MD, PhD; Jessica A. Lyden, BSc; Ryan L. Korosec, MBA, CPA; and Daniel L. Davenport, PhD
Using Electronic Health Record Clinical Decision Support Is Associated With Improved Quality of Care
Rebecca G. Mishuris, MD, MS; Jeffrey A. Linder, MD, MPH; David W. Bates, MD, MSc; and Asaf Bitton, MD, MPH
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The Impact of Pay-for-Performance on Quality of Care for Minority Patients
Arnold M. Epstein, MD, MA; Ashish K. Jha, MD, MPH; and E. John Orav, PhD
Predictors of High-Risk Prescribing Among Elderly Medicare Advantage Beneficiaries
Alicia L. Cooper, MPH, PhD; David D. Dore, PharmD, PhD; Lewis E. Kazis, ScD; Vincent Mor, PhD; and Amal N. Trivedi, MD, MPH

The Impact of Pay-for-Performance on Quality of Care for Minority Patients

Arnold M. Epstein, MD, MA; Ashish K. Jha, MD, MPH; and E. John Orav, PhD
This study finds no evidence of a deleterious impact of pay-for-performance on minority patients in the Premier Hospital Quality Incentive Demonstration.
To determine whether racial disparities in process quality and outcomes of care change under hospital pay-for-performance.

Study Design
Retrospective cohort study comparing the change in racial disparities in process quality and outcomes of care between 2004 and 2008 in hospitals participating in the Premier Hospital Quality Incentive Demonstration versus control hospitals.

Using patient-level Hospital Quality Alliance (HQA) data, we identified 226,096 patients in Premier hospitals, which were subject to pay-for-performance (P4P) contracts and 1,607,575 patients at control hospitals who had process of care measured during hospitalization for acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia. We additionally identified 123,241 Medicare patients in Premier hospitals and 995,107 in controls who were hospitalized for AMI, CHF, pneumonia, or coronary artery bypass graft (CABG) surgery. We then compared HQA process quality indicators for AMI, CHF, and pneumonia between P4P and control hospitals, as well as risk-adjusted mortality rates for AMI, CHF, pneumonia, and CABG.

Black patients initially had lower performance on process quality indicators in both Premier and non-Premier hospitals. The racial gap decreased over time in both groups; the reduction in the gap in Premier hospitals was greater than the gap reduction in non-Premier hospitals for AMI patients. During the study period, mortality generally decreased for blacks relative to whites for AMI, CHF, and pneumonia in both Premier and non-Premier hospitals, with the relative reduction for blacks greatest in Premier hospitals for CHF.

Our results show no evidence of a deleterious impact of P4P in the Premier HQID on racial disparities in process quality or outcomes.

Am J Manag Care. 2014;20(10):e479-e486
Our results, from the largest pay-for-performance (P4P) demonstration performed to date, suggest disparities in process and outcomes for hospitalized minority patients are not aggravated by P4P, and may in fact be improved
  • Racial disparities in process quality measures improved in P4P hospitals and controls, but more so in P4P hospitals for acute myocardial infarction patients.
  • Difference in mortality decreased for black patients relative to white patients in both P4P and control hospitals, although the relative reduction for black patients was greater in P4P hospitals for congestive heart failure.
  • Concerns that P4P will exacerbate existing disparities for minority patients may be unwarranted.
Provision of financial incentives for high-quality care, commonly known as pay-for-performance (P4P), has become a common strategy to improve quality of care. By 2006 more than 80% of privately insured persons were covered by health plans using P4P.1 In October 2012, CMS adopted P4P for the Medicare program nationwide in most hospitals, except for Critical Access Hospitals.

Prior to adopting P4P, CMS conducted the largest demonstration program of the strategy to date among hospitals. Between the fourth quarter of 2003 and the first quarter of 2009, the Premier Hospital Quality Incentive Demonstration (HQID) rewarded high quality of care delivered by participating hospitals for 6 conditions: acute myocardial infarction (AMI), congestive heart failure (CHF), pneumonia, coronary artery bypass graft surgery (CABG), total hip replacement, and total knee replacement. Premier is a national organization of not-for-profit hospitals, which partnered with the federal government in the Premier HQID. While studies of the Premier HQID2-5 have shown it to have only modest benefits in improving quality of care, it is nonetheless the model for the new federal P4P program.

Programs using financial incentives to improve quality of care have enormous face validity. However, there are persistent concerns that rather than reduce disparities, this approach may exacerbate racial and ethnic disparities because of between-hospital differences in where minorities and nonminorities receive their care and/or within-hospital differences in how care is administered at a given hospital.6,7 Critics worry that racial minorities may receive care in institutions that are undercapitalized and less able to promote high-quality care. At the same time, minorities may have more disability or lower health literacy that result in poorer health outcomes and greater challenges in delivering high-quality care. Faced with financial incentives, hospitals might engage in quality improvement efforts that could widen disparities if the cultural, linguistic, and educational needs of minority patients prove difficult to address.

Despite the risk of unintended consequences, there is surprisingly little information on quality of care for racial minorities under P4P programs. To our knowledge there have been no studies to date in the United States documenting the impact of P4P on disparities in quality of care for minority patients. Therefore, in this study, we sought to answer 3 questions: First, how did black patients initially fare compared with white patients on receipt of evidence-based processes of care under Premier P4P? Second, how did these patients fare in terms of initial outcomes under the Premier P4P program? Finally, how did any racial disparities in the processes and outcomes of care change under P4P compared with patterns for patients treated in a group of hospitals that participated in public reporting but did not receive financial incentives? In carrying out these analyses, we were concerned with disparities combined from both between and within hospitals sources.


Premier HQID Hospital Participants and Controls

In 2003, CMS invited 421 hospitals that were part of the Premier Healthcare Informatics Program to participate in the HQID pay-for-performance program, and more than 260 hospitals agreed to do so. To participate, hospitals were required to provide data on 33 quality indicators for 3 medical conditions (AMI, CHF, and pneumonia) and 3 surgical procedures (CABG, total hip replacement, and total knee replacement). The 33 indicators included process measures for all 6 conditions and risk-adjusted mortality for AMI and CABG surgery. Hospitals performing in the top decile for any of the conditions would receive a bonus payment of 2% of Medicare payments for that condition. Hospitals scoring in the second decile received a 1% bonus. Starting in the third year of the demonstration, hospitals in the second-lowest decile were liable for a 1% financial penalty, while hospitals in the lowest decile received a 2% financial penalty. However, penalties were not ultimately initiated until the fourth year. After program initiation, the Premier HQID made changes to its incentive structures to reward improvement as well as performance.

We identified the national sample of non-Premier hospitals participating in public reporting through the Hospital Quality Alliance (HQA) as a control group, and adjusted for differences in hospital characteristics and the patient population.

Process Quality Indicators

In examining process indicators, we were able to obtain, from CMS, patient-based data on patients from hospitals participating in the HQA program. To ensure confidentiality, these data included a hospital-encrypted identifier and information on the hospitals’ bed size, teaching status, and participation in Premier, but no other hospital-based information.

The patient-level, all-payer data on HQA process indicators that we obtained from CMS included information on all patients discharged with AMI, CHF, or pneumonia submitted by hospitals participating in the HQA program between the fourth quarter of 2003 and the fourth quarter of 2008. Because many hospitals did not start reporting until the first quarter of 2004, we excluded data from the fourth quarter of 2003. The de-identified data we received included performance information on 18 process indicators for the 3 conditions (see eAppendix Table 1 available at In addition to performance on the relevant quality indicators, hospitals reported patient gender, age, race/ethnicity, and primary payer. For each medical condition, we identified the relevant process measures and then tabulated the number of patients in the denominator for that condition and the percentage of them who received the specified service. We compared performance at 250 Premier hospitals and 3507 control hospitals in the patient-level data set we received from CMS.

Outcome Quality Indicators

To examine outcome indicators, we identified all hospitals providing hospital-based HQA data between 2004 and 2008 to the Medicare Compare database publicly available on the Internet, and linked them to Medicare cost reports and the American Hospital Association (AHA) annual survey. Using data from Medicare cost reports and the AHA survey, we were able to characterize these hospitals according to bed size, regional location, profit status, teaching status, eligibility for large bonuses (based on the proportion of the hospital’s patients with Medicare coverage), margin, and location in a competitive market (as measured by the Herfindahl-Hirschman Index). The latter 3 characteristics have been shown to be associated with a greater response to P4P.3

To examine clinical outcomes, we used Part A Medicare data in 2004 and 2008 on all patients discharged with a principal diagnosis of AMI, CHF, or pneumonia, or a procedure code indicating that they had received a CABG procedure. We included 251 Premier hospitals and 3257 control hospitals with linked data. We employed separate logistic regression models examining 30-day mortality for each of the conditions, accounting for clustering of patients within hospital in each model.

The study was determined to be exempt from human subjects review by the Harvard Office of Human Research Administration.


We first compared characteristics of Premier and non-Premier hospitals and the characteristics of white and black patients who received care at those hospitals. We initially examined process indicators separately for AMI, CHF, and pneumonia, displaying the data by quarter. Then we limited the database to process indicators collected during 2004 and 2008 and examined whether disparities changed between 2004 and 2008, and whether these changes were of similar magnitude in Premier and non-Premier hospitals. The process measure analysis used a repeated measures linear regression, with the patient as the unit of analysis so that each individual patient’s health quality score was used as the dependent variable in the model. Each patient had a score between 0 and 100, indicating the proportion of quality indicators that were relevant and whose standards were met.

By analyzing at the level of the patient, it was necessary for the model to assess and adjust for correlation between patients seen within the same hospital. A marginal generalized estimated equation model, as implemented through the GENMOD procedure in the SAS language, was used to control for this clustering of patients within hospitals.8 An initial independent correlation structure was used to provide an estimate of the overall effect of race (both within and between hospitals) on disparities, although empirical standard errors were used to derive correlation-adjusted test statistics and confidence intervals. The primary predictors were: time (2008 vs 2004); race; Premier status (Premier vs non-Premier); 2-way interactions between race and 2008, race and Premier, and 2008 and Premier; and a 3-way interaction between race, 2008, and Premier.

The 2-way interactions allowed us to compare racial disparities between Premier and non-Premier hospitals, as well as to compare racial disparities between the baseline and terminal time periods. The 3-way interaction allowed us to compare the change over time in racial disparities in Premier hospitals to the change over time in non-Premier hospitals. The main effects of race, 2008, and Premier, and the 2- and 3-way interactions were included in all models, regardless of significance. In this way, our models compared actual observed differences in HQA scores rather than assuming that certain differences were zero simply because their P values were greater than .05 (which could have happened because of limited power). Hospital characteristics such as size and teaching status were intentionally excluded from the model in order to preserve the effect of between-hospital differences. Results are displayed as adjusted means with P values for black versus white differences over time and Premier versus non-Premier differences determined from the appropriate interaction term. To assess patient mortality outcomes and determine whether disparities changed over time, we similarly used a marginal repeated measures logistic regression for Premier and non-Premier hospitals. Again, each patient was analyzed individually so that their binary mortality status was the dependent variable in the regression model. By using a patient-based model we were able to include patient-based variables (age, sex, and 28 comorbidities), as well as time (2008 vs 2004); race (white vs black); Premier status; 2-way interactions between race and 2008, race and Premier, and 2008 and Premier; and a 3-way interaction between race, 2008, and Premier.

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