Association Between Physician Quality Improvement Incentives and Ambulatory Quality Measures

Objectives:

these incentives.

To determine the prevalence of physician incentives for quality and to test the hypothesis that the quality of ambulatory medical care is better when provided by physicians with

Study Design:

Cross-sectional study using data from the National Ambulatory Medical Care Survey.

Methods:

We examined the association between 12 measures of high-quality ambulatory care and physician compensation based on quality, physician compensation based on satisfaction, and public reporting of quality measures.

Results:

Overall, 20.8% of visits were to physicians whose compensation was partially based on quality, 17.7% of visits were to physicians whose compensation was partially based on patient satisfaction, and 10.0% of visits were to physicians who publicly reported quality measures. Quality of ambulatory care varied: weight reduction counseling occurred in 12.0% of preventive care visits by obese patients, whereas no urinalysis in patients with no indication was achieved in 93.0% of preventive care visits. In multivariable analyses, there were no statistically significant associations between compensation for quality and delivery of any of the 12 measures, nor between compensation for satisfaction and 11 of the 12 measures; the exception was body mass index screening in preventive visits (47.8% vs 56.2%, adjusted P = .004). There was also no statistically significant association between public reporting and delivery of 11 of 12 measures; the exception was weight reduction counseling for overweight patients (10.0% vs 25.5%, adjusted P = .01).

Conclusions: We found no consistent association between incentives for quality and 12 measures of high-quality ambulatory care.

(Am J Manag Care. 2012;18(4):e126-e134)Using data from the National Ambulatory Medical Care Survey, we examined the association between 3 incentives—financial compensation for quality and patient satisfaction, and public reporting of quality measures—and 12 ambulatory care quality measures.

• We found fewer than one-fifth of ambulatory visits were to physicians receiving these incentives.

• There were no consistent associations between financial incentives or public reporting and 12 measures of ambulatory quality.

Variation in quality is a problem of the US healthcare system.^1,2 Pay for performance and public reporting of quality measures are 2 incentives that health insurers and payers use to promote high-quality medical care.^3-7 Almost half of commercial health maintenance organizaions (HMOs) use some form of pay for performance, according to a 2006 national survey.⁶ In addition, the Deficit Reduction Act of 2005 and the Tax Relief and Health Care Act of 2006 required the Centers for Medicare & Medicaid Services (CMS) to establish plans for value-based payments and physician quality reporting mechanisms.^8,9 In 2007, CMS instituted a voluntary physician reporting program—the Physician Quality Reporting System—as part of its valuebased purchasing program.¹⁰ CMS also has an ongoing demonstration project, the Physician Group Practice project, to study the impact of pay for performance and other payment models on quality of care.¹¹ The

Patient Protection and Affordable Care Act of 2010 has allocated funding for models such as accountable care organizations and the Patient- Centered Medical Home, which tie compensation to quality.¹²

Despite increasing interest, the impact of pay for performance and public reporting on the quality of ambulatory medical care is unclear.^13-17 A 2006 systematic review of the literature found that very few studies had assessed the effect of pay for performance on quality.¹⁸ Of these studies, the impact of pay for performance varied: several programs showed improvements in quality and others showed little or none. Even less is known about the effect of physician public reporting of quality measures on ambulatory quality of care.^19,20 Furthermore, evaluations of physician-level incentives to improve quality have focused on a small number of health plans or systems, or programs outside the United States.^18,21-34 To our knowledge, no studies have examined the association between physician incentives and quality of ambulatory care on a national level.

Using a nationally representative survey of ambulatory visits in the United States, we sought to determine the prevalence of physician incentives for quality and to test the hypothesis that quality of ambulatory medical care is better in the context of these physician incentives. We looked specifically at financial incentives that reward higher quality care and financial incentives that reward better patient satisfaction with the hypothesis that these methods of compensation will be associated with higher quality care. We also examined the association between quality and public reporting of quality measures with the assumption that physicians are motivated to improve performance on publicly reported quality measures to maintain or improve their professionalreputation and patient volume.

METHODSStudy Design

We performed a cross-sectional analysis using data from the 2006 and 2007 National Ambulatory Medical Care Survey (NAMCS). NAMCS is a nationally representative survey administered by the Centers for Disease Control’s National Center for Health Statistics. NAMCS contains information about patient visits to non—federally funded, non–hospitalbased offices throughout the United States. Physicians in the fields of anesthesiology, radiology, and pathology are excluded from the survey. Physicians who participate in the survey cannot participate again for at least 3 years.^35,36

NAMCS uses a 3-stage sampling design. The first stage is based on geographic location, the second stage identifies offices in each geographic location, and the third stage samples visits within each office. The visits sampled take place during a 1-week period that is randomly assigned for each practice. Between 20% and 100% of the visits that week are sampled, depending on the size of the practice. The National Center for Health Statistics weighs each visit so that the data can be used for national estimates. Each visit weight accounts for selection probability, adjusts for nonresponse, and accounts for other factors so that the national estimates properly reflect the scope of ambulatory visits in the United States.

The survey collects patient and office demographics, and visit-specific clinical information. The information from each visit is recorded on a standardized survey form by the physician, office staff, or a US Census Bureau representative. Clinical characteristics include up to 3 reasons for the visit coded using a reason-for-visit classification, up to 3 diagnoses coded using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), and up to 8 drugs coded using the Lexicon Plus classification, a proprietary database of Cerner Multum, Inc. Because this study used publicly available NAMCS data without respondent identifiers, the Mount Sinai Institutional Review Board exempted it from review.

Study Sample

We limited our sample to visits by nonpregnant patients who were >18 years of age. Because this study focused on rates of delivery of high-quality ambulatory care, we further limited our sample to visits to primary care physicians (ie, general internists, family practitioners, gynecologists) and internal medicine subspecialists (eg, cardiologists, endocrinologists). We excluded visits to surgical specialists and nonmedical specialists (eg, dermatologists, psychiatrists, occupational medicine) because the measures of high-quality care of interest did not pertain to their areas of practice. Finally, we excluded visits in which a physician was not seen.

Study Variables

Physician Incentives. We hypothesized that 3 physician incentives routinely collected as part of the NAMCS physician induction survey³⁶ would be associated with better quality of care. First, visits were categorized as whether or not the physician’s patient care compensation was at least partially based on quality measures. Second, and similarly, visits were categorized as whether or not the physician’s patient care compensation was at least partially based on patient satisfaction. Of note, the proportion of physician compensation based on quality or satisfaction was reported in quartiles (<25%, 25%-50%, 51%-75%, and 76%-100%). For our study both incentives were categorized as any or none because in the large majority of visits (93.7%), incentives accounted for less than 25% of compensation. Finally, visits were categorized as whether or not the physician or practice publicly reported quality measures.

Appendix

Ambulatory Quality of Care. We examined 12 measures of ambulatory quality of care (11 process measures and 1 outcome measure) that were identified using the Physician Quality Reporting System measures from CMS ().³⁷ The Physician Quality Reporting System measures include 216 processes and outcomes of care, but the majority of these measures could not be examined because the measure (1) was not related to ambulatory care; (2) could not be analyzed on a per visit basis; (3) was not systematically collected in NAMCS; or (4) examined a disease or condition for which the sample of eligible visits was too small (<30 visits) for accurate analyses.

Eligible visits for our study included visits to physicians for preventive care; chronic disease care for 5 conditions (diabetes mellitus, heart failure, coronary artery disease, atrial fibrillation, and chronic obstructive pulmonary disease [COPD]); and upper respiratory tract infections. Visits were identified using ICD-9-CM codes. Preventive care visits were defined as visits to the patient’s primary care physician with 1 of the following: (1) ICD-9-CM code V70.0X or V70.9X (or both); (2) general medical examination as a reason for the visit (reasonfor- visit code 31000); or (3) a preventive or nonillness care code as the NAMCS major reason for visit.

We examined 4 measures of high-quality ambulatory care during visits to physicians for preventive care: smoking cessation counseling for smokers, body mass index (BMI) screening, weight reduction counseling for overweight patients, and urinalysis not performed or ordered. Of note, the urinalysis measure was the only measure that we considered achieved if the test was not performed. For this measure we excluded visits by patients with urinary symptoms, renal disease, diabetes, HIV, AIDS, or hypertension.

We examined 1 measure of high-quality diabetes care: blood pressure measurement of less than 130/80 mm Hg.

We examined 2 measures of high-quality heart failure care: (1) prescription of either angiotensin-converting enzyme inhibitor or angiotension receptor blocker therapy, excluding visits by patients with hyperkalemia or angioedema; and (2) prescription of beta-blocker therapy, excluding visits by patients with heart block, bradycardia, COPD, or asthma.

We examined 2 measures of high-quality coronary artery disease care: (1) prescription of oral antiplatelet therapy, excluding visits by patients with peptic ulcer disease, gastritis, gastrointestinal bleeding, duodenitis, or renal disease; and (2) prescription of beta-blocker therapy, excluding visits by patients with heart block, bradycardia, COPD, or asthma.

Finally, we examined 3 additional measures of high-quality ambulatory care: (1) no prescription of antibiotic therapy during visits by patients for upper respiratory infection, excluding visits by patients with COPD, HIV, AIDS, or cancer; (2) prescription of anticoagulation therapy during visits by patients with atrial fibrillation, excluding visits by patients with peptic ulcer disease, gastritis, gastrointestinal bleeding, duodenitis, cerebral hemorrhage, central nervous system tumors, renal disease, thrombocytopenia, or gait abnormality; and (3) prescription of bronchodilator therapy during visits by patients with COPD. For each measure of high-quality care, eligible visits were categorized by whether or not there was documentation that the patient received the recommended ambulatory care.

Other Variables of Interest. All visits were additionally categorized by other patient and physician characteristics. For patient characteristics, we examined the following: age, sex, race/ethnicity, number of chronic conditions, and insurance type (private, Medicare, Medicaid, and other, which included self-pay, worker’s compensation, and no fee). For physician and practice characteristics, we examined practice size (solo or group), practice type (private practice, community health center, or HMO), physician employment status (owner, employee, or contractor), physician specialty (primary care or medical specialist), region of practice (Northeast, Midwest, South, or West), and urban or rural practice location.

Statistical Analysis

We performed a visit-level analysis using visit-level sampling weights to account for physician and practice clustering.^38-41 We described visit characteristics using standard frequency analyses, presenting them as the weighted proportion of visits in our study sample. We used the c2 test to study the bivariate association between physician incentives or public reporting and delivery of each of the 12 quality indicators. We used multivariable logistic regression to assess the independent effect of physician incentives on the delivery of each of the 12 quality indicators, creating independent models for each outcome while controlling for the patient and physician characteristics as outlined above and whether the physician received other incentives for quality or publicly reported quality measures. We also performed logistic regression to determine whether more incentives or specific combinations of incentives had incremental associations with quality.

In order to understand whether the results of our study differed among subgroups of patients, we performed additional sensitivity analyses stratifying patients by insurance type (private insurance, Medicare, and Medicaid) and age (<65 and >65 years).

All analyses took into account the complex survey design and weighted sampling probabilities of the data source for the calculation of nationally representative point and variance estimates. All analyses were performed using Stata statistical software, version 11.0 (Stata Corp, College Station, Texas). Because we repeated our analyses for 3 different physician incentives, we used the Bonferroni correction and used a P value of .01 to signify statistical significance.⁴²

RESULTS

Sociodemographic and Clinical Characteristics

Among 62,170 visits in the 2006 and 2007 NAMCS, 28,287 (46.4%) were by adult, nonpregnant patients to primary care physicians and medical specialists, representing 920 million visits. Overall, 20.8% of visits were to physicians whose compensation was partially based on quality, 17.7% of visits were to physicians whose compensation was partially based on satisfaction, and 10.0% of visits were to physicians who publicly reported performance measures.

There were few differences in patient and physician characteristics in visits to physicians whose compensation was partially based on quality or satisfaction compared with visits to physicians whose compensation was not (Table 1). Non-Hispanic black patients were less likely to be seen by a physician whose compensation was partially based on quality (adjusted odds ratio [AOR] 0.62, 95% confidence interval [CI] 0.46-0.87) or satisfaction (AOR 0.62, 95% CI 0.45- 0.86). Visits to practice owners were also less likely to be to a physician whose compensation was partially based on quality (AOR 0.39, 95% CI 0.23-0.66) or satisfaction (AOR 0.40, 95% CI 0.20-0.80). Patient characteristics were also unassociated with visits to physicians who publicly reported quality measures with the exception of the number of comorbidities. In this case, patients seeing physicians who publicly reported quality measures were more likely to have 3 or more chronic conditions than patients seeing physicians who did not report quality measures (AOR 2.62, 95% CI 1.63-4.19).

Quality of Medical Care

Table 2

Overall, there was wide variation in the quality of ambulatory medical care for adult patients (). For example, urinalysis was not performed in 93.0% of preventive care visits (an indicator of high quality), whereas weight reduction counseling occurred in only 12.0%. Appropriate medications for heart failure, coronary artery disease, atrial fibrillation, and COPD were prescribed in 43.5% to 64.9% of visits. Antibiotics for visits for upper respiratory tract infections were not prescribed in 45.5% of

visits.

Quality Incentives

Financial Incentives for Quality and Satisfaction. In multivariate analyses, we found no statistically significant associations between quality-based compensation and quality of care. We found that only BMI screening during preventive visits was associated with satisfaction- based compensation (47.8% vs 56.2%, adjusted P = .004).

Public Reporting of Quality Measures. As with financial incentives, there was no consistent association between public reporting and quality performance. The only performance measure positively associated with public reporting was weight reduction counseling (10.0% vs 25.5%, adjusted P = .01).

Multiple Incentives for Quality. We found no association between the number of incentives for quality and high-quality medical care for any ambulatory care measures we studied (data not shown).

DISCUSSION

In this analysis of a national sample of ambulatory visits to primary care physicians and internal medicine specialists, we found that approximately 20% of visits were to physicians whose compensation was partially based on quality. Fewer visits were to physicians whose compensation was partially based on patient satisfaction or who publicly reported quality measures. Although prior surveys have found that almost half of commercial HMOs and state Medicaid programs engage in pay for performance,^5,6,15-17,43 we found that a minority of ambulatory visits are to physicians who receive compensation from incentive programs. The lower percentage may not reflect less performance measurement but rather whether financial rewards are directed to an entire practice or an individual physician. In many cases, rewards directed to an entire practice may not be apportioned to individual physicians.

Consistent with prior studies,^1,2,41 we found wide variation in the measured performance of ambulatory medical care for a discrete set of 12 ambulatory quality measures assessable within our data. For example, fewer than a quarter of overweight patients received weight reduction counseling during preventive visits; appropriate medications were prescribed in only 43% to 65% of visits. This variation may reflect the relative ease of performing certain benchmarks of quality (eg, not performing urinalysis versus counseling overweight patients). We found, however, no consistent association between the financial incentives for quality or public reporting and 12 measures of high-quality ambulatory care.

Several prior evaluations of incentive programs have found correlations between financial incentives for quality and improvements in quality, but many of these were assessments of a single payer’s incentive programs or evaluations of small numbers of practices.^18,21-34 Other studies, consistent with our results, have not found a relationship between incentives for quality and the delivery of high-quality ambulatory care. A 2006 systematic review found that of 6 randomized, controlled studies of physician-level incentives, only 2 showed a positive effect.¹⁸

Several factors may explain our results. First, physicians may not respond to incentives, particularly if the incentives are not substantial enough to significantly affect their incomes or patients’ perceptions of quality. This hypothesis was substantiated in interviews with leaders of physician organizations enrolled in one of the country’s largest HMO pay for performance programs. The majority of those interviewed believed that incentive amounts needed to be greater for the program to be effective.⁴⁴ Second, physicians may not want to cooperate with incentive programs, especially if they disagree with the indicators used to measure quality. A 2007 survey found that a majority of general internists felt that quality measures were not accurate and were not accurately adjusted for patient risk factors.⁴⁵

Some limitations of our study are worth noting. First, we were limited to 12 ambulatory quality measures that could be assessed with our data set. These measures of quality may have little or no relation to measures that were the basis for the incentive programs in which physicians were enrolled. Thus, we cannot make conclusions about the performance on directly relevant measures but rather can conclude that broad quality incentives—which may be diverse and different for each physician&mdash;were not associated with better performance as measured by a set of highly specific process measures.

We also cannot make generalizations about the association of incentives and reporting with other measures of highquality ambulatory care such as preventive cancer screening and immunizations, which could not be evaluated on a per visit level. In addition, this was a cross-sectional study; thus, we cannot determine whether incentive programs were used to improve performance in poorly performing practices or implemented regardless of the practices’ performance. Also, significant differences in the rates of smoking cessation and weight reduction counseling may partially reflect differences in documentation practice. Finally, we were unable to perform a physician level-analysis because the number of eligible visits at the physician level was too small to make reasonable performance estimates.

CMS and a number of private payers have invested considerable time and money in the development of public reporting and pay for performance programs. Our finding that on a national level financial incentives and public reporting were not associated with quality for a discrete set of 12 measures corroborates other findings from local and regional studies. More research is likely needed to understand whether and how quality incentive programs should be structured and implemented.Author Affiliations: From Division of Outcomes and Effectiveness (TFB), Department of Public Health, Weill Cornell Medical College, New York, NY; Department of Medicine (TFB), Weill Cornell Medical College, New York, NY; Division of General Internal Medicine (ADF), Department of Medicine, Mount Sinai School of Medicine, New York, NY; Section of General Internal Medicine (JSR), Department of Medicine, Yale University School of Medicine and Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT.

Funding Source: Dr Bishop is supported in part by funds provided to her as a Nanette Laitman Clinical Scholar in Public Health at Weill Cornell Medical College. Dr Federman is supported by the National Institute on Aging and by the American Federation of Aging Research (1K23AG028955-01). Dr Ross is supported by the National Institute on Aging and by the American Federation of Aging Research through the Paul B. Beeson Career Development Awards Program (K08 AG032886).

Author Disclosures: Dr Ross reports receiving consultancies for a scientific advisory board from Fair Health Inc, and reports receiving a research grant from the Centers for Medicare & Medicaid Services. The other authors (TFB, ADF) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (TFB, ADF, JSR); acquisition of data (TFB); analysis and interpretation of data (TFB, ADF, JSR); drafting of the manuscript (TFB); critical revision of the manuscript for important intellectual content (TFB, ADF, JSR); statistical analysis (TFB); administrative, technical, or logistic support (ADF); and supervision (ADF, JSR).

Address correspondence to: Tara F. Bishop, MD, Department of Public Health, Weill Cornell Medical College, 402 E 67th St, Rm LA-218, New York, NY 10021. E-mail: tlfernan@med.cornell.edu.1. Institute of Medicine. Crossing the Quality Chasm: A New Health System for the Twenty-first Century. Washington, DC: National Academies Press; 2001.

2. McGlynn EA, Asch SM, Adams J, et al. The quality of health care delivered to adults in the United States. N Engl J Med. 2003;348(26): 2635-2645.

3. Landon BE, Rosenthal MB, Normand SL, Frank RG, Epstein AM. Quality monitoring and management in commercial health plans. Am J Manag Care. 2008;14(6):377-386.

4. Rosenthal MB, Landon BE, Normand SL, Frank RG, Ahmad TS, Epstein AM. Employers’ use of value-based purchasing strategies. JAMA. 2007;298(19):2281-2288.

5. Rosenthal MB, Landon BE, Howitt K, Song HR, Epstein AM. Climbing up the pay-for-performance learning curve: where are the early adopters now? Health Aff (Millwood). 2007;26(6):1674-1682.

6. Rosenthal MB, Landon BE, Normand SL, Frank RG, Epstein AM. Pay for performance in commercial HMOs. N Engl J Med. 2006;355(18): 1895-1902.

7. Rosenthal MB, Frank RG, Buchanan JL, Epstein AM. Transmission of financial incentives to physicians by intermediary organizations in California. Health Aff (Millwood). 2002;21(4):197-205.

8. 109th United States Congress. The Tax Relief and Health Care Act of 2006. Pub L No. 109-432.

9. 109th United States Congress. The Deficit Reduction Act of 2005. Pub L No. 109-171.

10. Centers for Medicare & Medicaid Services. Roadmap for Implementing Value Driven Healthcare in the Traditional Medicare Fee-for- Service Program. https://www.cms.gov/QualityInitiativesGenInfo/downloads/VBPRoadmap_OEA_1-16_508.pdf. Accessed September 9, 2011.

11. Centers for Medicare & Medicaid Services. Medicare Physician Group Practice Demonstration: Physicians Groups Continue to Improve Quality and Generate Savings Under Medicare Physician Pay for Performance Demonstration. https://www.cms.gov/DemoProjectsEvalRpts/ downloads/PGP_Fact_Sheet.pdf. Published July 2011. Accessed September 9, 2011.

12. 111th United States Congress. Patient Protection and Affordable Care Act of 2010. Pub L No. 111-1482010.

13. Brook RH. Physician compensation, cost, and quality. JAMA. 2010; 304(7):795-796.

14. Rosenthal MB, Dudley RA. Pay-for-performance: will the latest payment trend improve care? JAMA. 2007;297(7):740-744.

15. Epstein AM. Paying for performance in the United States and abroad. N Engl J Med. 2006;355(4):406-408.

16. Epstein AM. Pay for performance at the tipping point. N Engl J Med. 2007;356(5):515-517.

17. Epstein AM, Lee TH, Hamel MB. Paying physicians for high-quality care. N Engl J Med. 2004;350(4):406-410.

18. Petersen LA, Woodard LD, Urech T, Daw C, Sookanan S. Does payfor- performance improve the quality of health care? Ann Intern Med. 2006;145(4):265-272.

19. Tu JV, Donovan LR, Lee DS, et al. Effectiveness of public report cards for improving the quality of cardiac care: the EFFECT study: a randomized trial. JAMA. 2009;302(21):2330-2337.

20. Hibbard JH, Stockard J, Tusler M. Hospital performance reports: impact on quality, market share, and reputation. Health Aff (Millwood). 2005;24(4):1150-1160.

21. Rosenthal MB, Frank RG, Li ZH, Epstein AM. Early experience with pay-for-performance: from concept to practice. JAMA. 2005;294(14): 1788-1793.

22. Doran T, Fullwood C, Gravelle H, et al. Pay-for-performance programs in family practices in the United Kingdom. N Engl J Med. 2006;355(4):375-384.

23. Campbell SM, Reeves D, Kontopantelis E, Sibbald B, Roland M. Effects of pay for performance on the quality of primary care in England. N Engl J Med. 2009;361(4):368-378.

24. Campbell S, Reeves D, Kontopantelis E, Middleton E, Sibbald B, Roland M. Quality of primary care in England with the introduction of pay for performance. N Engl J Med. 2007;357(2):181-190.

25. Campbell SM, McDonald R, Lester H. The experience of pay for performance in English family practice: a qualitative study. Ann Fam Med. 2008;6(3):228-234.

26. Coleman K, Reiter KL, Fulwiler D. The impact of pay-for-performance on diabetes care in a large network of community health centers [published correction appears in J Health Care Poor Underserved. 2008;19(2):657]. J Health Care Poor Underserved. 2007;18(4):966-983.

27. Chung S, Palaniappan LP, Trujillo LM, Rubin HR, Luft HS. Effect of physician-specific pay-for-performance incentives in a large group practice. Am J Manag Care. 2010;16(2):e35-e42.

28. An LC, Bluhm JH, Foldes SS, et al. A randomized trial of a pay-forperformance program targeting clinician referral to a state tobacco quitline. Arch Intern Med. 2008;168(18):1993-1999.

29. Chen JY, Tian HJ, Taira Juarez DT, et al. The effect of a PPO pay-forperformance program on patients with diabetes. Am J Manag Care. 2010;16(1):e11-e19.

30. Pearson SD, Schneider EC, Kleinman KP, Coltin KL, Singer JA. The impact of pay-for-performance on health care quality in Massachusetts, 2001-2003. Health Aff (Millwood). 2008;27(4):1167-1176.

31. Young GJ, Meterko M, Beckman H, et al. Effects of paying physicians based on their relative performance for quality. J Gen Intern Med. 2007;22(6):872-876.

32. Beaulieu ND, Horrigan DR. Putting smart money to work for quality improvement. Health Serv Res. 2005;40(5, pt 1):1318-1334.

33. Levin-Scherz J, DeVita N, Timbie J. Impact of pay-for-performance contracts and network registry on diabetes and asthma HEDIS measures in an integrated delivery network. Med Care Res Rev. 2006;63(1) (suppl):14S-28S.

34. Gilmore AS, Zhao YX, Kang N, et al. Patient outcomes and evidence-based medicine in a preferred provider organization setting: a six-year evaluation of a physician pay-for-performance program. Health Serv Res. 2007;42(6, pt 1):2140-2159.

35. Centers for Disease Control and Prevention. Ambulatory health care data. NAMCS scope and sample design. http://www.cdc.gov/nchs/ ahcd/ahcd_scope.htm. Published 2006. Accessed September 9, 2011.

36. Centers for Disease Control and Prevention. National Ambulatory Medical Care Survey 2006 Panel. http://www.cdc.gov/nchs/data/ahcd/ namcs1-2006.pdf. Published 2006. Accessed January 18, 2011.

37. Centers for Medicare & Medicaid Services. Physician quality reporting initiative: measures codes. http://www.cms.gov/PQRI/15_Measures Codes.asp#TopOfPage. Published 2010. Accessed September 9, 2011.

38. Ma J, Stafford RS. Quality of US outpatient care: temporal changes and racial/ethnic disparities. Arch Intern Med. 2005;165(12):1354-1361.

39. Linder JA, Ma J, Bates DW, Middleton B, Stafford RS. Electronic health record use and the quality of ambulatory care in the United States. Arch Intern Med. 2007;167(13):1400-1405.

40. Romano MJ, Stafford RS. Electronic health records and clinical decision support systems: impact on national ambulatory care quality. Arch Intern Med. 2011;171(10):897-903.

41. Chen LM, Farwell WR, Jha AK. Primary care visit duration and quality: does good care take longer? Arch Intern Med. 2009;169(20): 1866-1872.

42. Rice TK, Schork NJ, Rao DC. Methods for handling multiple testing. Adv Genet. 2008;60:293-308.

43. Kuhmerker K, Hartman, T. Pay-For-Performance in State Medicaid Programs: A Survey of State Medicaid Directors and Programs. New York: Commonwealth Fund; 2007. http://www.commonwealthfund. org/Publications/Fund-Reports/2007/Apr/Pay-for-Performance-in-State- Medicaid-Programs--A-Survey-of-State-Medicaid-Directors-and-Programs. aspx. Accessed March 8, 2012.

44. Damberg CL, Raube K, Teleki SS, Dela Cruz E. Taking stock of payfor- performance: a candid assessment from the front lines. Health Aff (Millwood). 2009;28(2):517-525.

45. Casalino LP, Alexander GC, Jin L, Konetzka RT. General internists’ views on pay-for-performance and public reporting of quality scores: a national survey. Health Aff (Millwood). 2007;26(2):492-499.