Physician Financial Incentives and Care for the Underserved in the United States

Objectives:

To estimate: (1) the percentage of physicians whose compensation is variable; (2) the frequency at which performance incentives for productivity, care quality, patient satisfaction, and resource use were used to determine compensation; and (3) how much incentives differ for physicians who serve greater percentages of patients who are Medicaid-insured, racial/ethnic minorities, or who face language barriers, versus those who do not.

Study Design:

Cross-sectional study of 3234 nationally representative physicians responding to the 2008 Center for Studying Health System Change’s Health Tracking Physician Survey (HTPS).

Methods:

We examined the degree to which practices’ percentage of Medicaid revenues and physicians’ panel characteristics were associated with physicians’ financial incentives using x² statistics and multivariate logistic regression (adjusting for physician specialty, practice type, and capitation levels, and area-based factors).

Results:

Compensation was variable for 69% of respondents, was most frequently tied to productivity (68%), and less often to care quality (19%), patient satisfaction (21%), or resource use (14%). Physicians were significantly less likely to report variable compensation if the percentage Medicaid revenues was 50% or more (adjusted odds ratio [OR] 0.73, 95% confidence interval [CI], 0.57-0.95) or if physician panels were at least 50% Hispanic (adjusted OR 0.74, 95% CI, 0.56-0.99). However, physicians were significantly more likely to report use of all 4 performance incentives if percentage of Medicaid revenues was 6% to 24%.

Conclusions:

Physicians report different types of financial incentives designed to alter care quality and quantity; incentive types differ by the degree that practices derive revenues from Medicaid or serve Hispanic patients. Further investigation is needed to understand how to align financial incentives with disparity-reduction efforts.

Am J Manag Care. 2014;20(2):121-129

• Financial incentives alter the quality and quantity of care that physicians provide.

• Understanding physicians’ past experience with financial incentives can help shape future compensation strategies.

• There is a complex relationship between physicians’ experience of financial incentives and the degree to which practices derive revenue from Medicaid—pay based on performance is more prevalent when percent Medicaid is between 6% and 24%, but fixed salaries are more common when percent Medicaid is greater than or equal to 50%.

• Fixed salaries are also more common when physician panels are greater than or equal to 50% Hispanic.

US healthcare spending continues to rise faster than the gross domestic product (GDP) and significant gaps persist between the care people should and do receive, particularly for persons from lower socioeconomic status (SES) classes and racial/ethnic minorities.^1-3 Since the Affordable Care Act of 2010 (ACA), payment and delivery system reform efforts have intensified, and there is substantial interest in healthcare delivery systems (eg, accountable care organizations [ACOs]) in which physicians increasingly serve as employees.^4-7 This may further catalyze efforts to restructure the types of financial incentives that physicians face (eg, a greater emphasis on cost profiling).^4-7

Until now, payment and delivery system policy discussions have tended to focus on the incentives that flow between payers (eg, governmental and commercial health plans) and practices (which range in size from small solo practitioners to large multidisciplinary medical groups that may include a hospital system), and less attention has been paid to the types of financial incentives that physicians face as individuals through their compensation arrangements.^4-7 Even less is known about how compensation may differ if practices derive a substantial proportion of their revenues from Medicaid-insured patients or if they care for greater percentages of patients with minority racial/ethnic backgrounds or non-English speaking patients, who are disproportionately medically and socially complex.⁸

The types of incentives that physicians face affect healthcare costs, quality, and disparities.^9-11 Physicians who are paid fixed salaries tend to see fewer patients and perform fewer procedures than physicians who are paid according to productivity.^12-15 However, paying physicians according to visit or procedure volumes can cause physicians to focus on services that can be delivered quickly (eg, acute care for self-limited problems like colds) and spend less time on those that require more time (eg, teaching patients how to manage their diabetes), even though the latter has a better chance of improving patients’ health.¹⁶ Tying financial bonuses to care quality (eg, pay for performance [P4P]) can be effective at improving care quality, but many are concerned that this strategy will yield negative unintended consequences (eg, unincentivized care will suffer).^17-21 There is even concern that P4P could cause physicians to avoid caring for patients of lower SES or patients of minority racial/ ethnic backgrounds because it may be more difficult to improve care quality for these patients.^18-21 It is important to understand the quickly evolving payment environment, especially as certain types of incentives may recede (eg, those for productivity) while others increase (eg, those for quality, patient satisfaction, and resource use).

Table 1

Prior studies suggest that although the majority of physicians who receive variable compensation are paid for productivity, the use of productivity incentives may be declining, while performance incentives related to care quality, patient satisfaction, and resource use may be increasing ().^20,22-30 These studies also demonstrate that practice and regional factors (eg, private practice vs health maintenance organizations [HMOs], the degree to which practice revenue depends on capitated contracts, whether practices are located in low-SES areas, and region of the United States) influence the types of financial incentives that physicians face.^20,22,30,31

It is also unclear whether physicians who treat high proportions of underserved patients may be exposed to different financial incentives than those who do not. For example, there is anecdotal evidence that physicians serving vulnerable populations (eg, physicians affiliated with federally qualified health centers or academic medical centers) tend to receive fixed salaries.^12,32-34 To our knowledge, there are no nationally representative data on whether the types of financial incentives that individual physicians face vary depending on the degree to which physicians care for Medicaid-insured patients, patients of minority racial/ethnic backgrounds, or patients who typically face language barriers when seeking medical care, after adjusting for other factors such as practice setting, for one, that may also influence the use of these incentives.^33-36

Thus, this study aims to: (1) estimate the percentage of physicians whose compensation is variable (as opposed to fixed); (2) determine, for physicians whose compensation is variable, the frequency at which physicians experience 4 common performance incentives—productivity, quality, patient satisfaction, and resource use; and (3) assess the degree to which these incentives may vary for physicians whose practices serve more Medicaid-insured patients, whose panels comprise larger proportions of minorities, or whose panels include more patients facing language barriers, adjusting for specialty, practice, and geographic characteristics.

METHODSStudy Design and Data Source

This is a cross-sectional study of physicians responding to the Center for Health System Change’s 2008 Health Tracking Physician Survey (HTPS). The HTPS is a periodic national survey of nonfederal allopathic and osteopathic physicians engaged in patient care at least 20 hours per week. Residents, fellows, and specialty physicians with limited patient contact in ambulatory settings (radiology, anesthesiology, and pathology) were ineligible for the survey. The HTPS utilized a nationally representative sample that included information from more than 4700 physicians and was conducted by mail. To our knowledge, the HTPS (and its predecessor, the Community Tracking Study) is the only nationally representative survey of active, ambulatorybased physicians that gathers this level of detail on physician compensation arrangements; alternate physician surveys do not.^37-39 The response rate for this survey was 61.9%.³⁶

Measures

Our first outcome of interest was the proportion of physicians that reported their compensation to be variable (as opposed to fixed). We defined physician compensation as “variable” if respondents answered “yes” to the survey item asking if a physician’s compensation was tied to any of the following measures: productivity, quality, patient satisfaction, or resource use; otherwise, physician compensation was considered to be fixed.

Our second set of outcomes was the performance measures to which compensation could be tied: productivity, care quality, patient satisfaction, and resource use. For example, we defined compensation as varying according to “productivity” if respondents answered “yes” to the question that asked if their practice took their personal productivity (eg, number of patients seen or procedures done per clinical session) into account when determining their pay. We did the same for questions related to physicians’ performance on clinical quality measures (eg, rates of preventive care services), satisfaction surveys (completed by their own patients), or pattern of using medical resources relative to their peers (ie, resource use or cost profiling). The 2008 HTPS did not ask these questions of solo practitioners, nor did it include questions about the size of the variable component of compensation.

We used 4 predictor variables to assess the degree to which physicians provided care to Medicaid-insured patients or patients with minority racial/ethnic backgrounds: (1) percentage of practice revenue derived from Medicaid, and the percentage of patients in each physician's practice who (2) were Hispanic, (3) were African American, or (4) faced language barriers when seeking healthcare (ie, the percentage of each physician's patient panel that had a “hard time speaking with or understanding because you speak different languages”). The validity of physician report of the racial/ ethnic composition of their patient panels has been previously established.⁴⁰ In each of these cases, since the data were skewed, we defined categories based on those providing care to 5% or less, 6% to 24%, 25% to 49%, and 50% or more of the demographic group of interest. Alternative categorizations (eg, <50% vs >50%) yielded substantively similar results. There was no co-linearity between these 4 variables (Variance Inflation Factor: 1.17) and less than 0.5% of the responses were missing.

Covariates

In multivariate analyses, we adjusted for 5 major factors known to be associated with physician compensation: (1) physician specialty (primary care, medical sub-specialty, or surgery); (2) practice type (1-2 physicians, >3 physicians, group model or staff model health maintenance organization [HMO], hospitalbased practice, medical school and other [eg, community health centers, municipal boards of health]); (3) the proportion of practice revenue that was capitated or prepaid (none, 1%-24%, or >25%); (4) an area-based marker of SES (the median household income of the zip code in which the practice was located using the 2004 Area Resources File [ARF]); and (5) regional location (North, Midwest, South, West).^9-16,18,19

Analysis

We used descriptive statistics to provide the percentages of our 2 outcomes of interest, stratifying by the degree to which physicians served patients insured by Medicaid, patients who were of minority background, or patients who faced language barriers. We then examined bivariate and multivariate associations between our financial incentives of interest and our covariates using x² statistics and logistic regression models. We used a separate multivariate logit model for each type of performance measure (ie, productivity, quality, satisfaction, and/or resource use incentives). In our multivariate models, we included covariates that may contribute to variation in financial incentives: physician specialty, practice type, proportion of practice revenues from capitation, area SES, and regional location.^22,41 We retained all covariates in the adjusted model because of their conceptual significance, regardless of the statistical significance of their bivariate association.⁴² We applied survey weights to account for the complex sampling strategy of the HPTS (ie, known differences between probability of selection, response, nonresponse, and location) in accordance with HTPS technical recommendations.⁴³

We conducted our analyses using STATA 9.0, and all reported P values are 2-sided. The Institutional Review Board (IRB) at the University of Chicago reviewed the study protocol and deemed it exempt from official review.

Role of Funding Source

The funding sources had no role in the design or conduct of the study; collection, management, analysis or interpretation of the data; or preparation, review, or approval of the manuscript for publication. None of the authors have any conflicts of interest to declare.

RESULTSNational Estimates: Variable Compensation and Types of Performance Incentives

Table 2

In this nationally representative survey of physicians, 68.5% of respondents indicate that their compensation is variable (). Among physicians reporting variable compensation, a majority (67.7%) report having their compensation tied to productivity; a minority indicate that their pay is tied to quality measures (18.7%), patient satisfaction (21.4%), or resource use (14.0%).

Relationship Between Variable Compensation and Degree to Which Physicians Serve Vulnerable Populations

Table 3

In bivariate analysis, the degree to which compensation is variable depends significantly upon the degree to which practices derive revenues from Medicaid or physicians care for patients with Hispanic backgrounds; there is no association for physicians caring for varying proportions of African American patients or of patients facing language barriers (Table 2). These relationships remain significant in multivariate analysis (). Physicians with 50% or more of their practice revenue from Medicaid have three-fourths the odds of receiving variable compensation compared with physicians with 5% or less of their practice revenues from Medicaid (adjusted OR: 0.73; 95% CI, 0.57-0.95, P <.05). Findings are similar for physicians with at least 50% Hispanic patients on their panels (adjusted OR: 0.74; 95% CI, 0.56-0.99, P <0.05) compared with those whose patient panels are 5% or less Hispanic.

Relationship Between Performance Incentives and Degree to Which Physicians Serve Vulnerable Populations

Table 4

Among physicians who indicate that their compensation depends on performance incentives, there is a significant bivariate association between physicians’ percentage of practice revenues from Medicaid and all 4 of our performance incentives of interest (). This relationship remains significant after multivariate analysis adjusting for physician specialty, practice type, and area-level factors. Physicians who report deriving 6% to 24% of their practice revenues from Medicaid have significantly greater odds of reporting that their compensation is tied to productivity (adjusted OR 1.19, 95% CI, 1.01-1.40), care quality (adjusted OR 1.45, 95% CI, 1.19-1.76), patient satisfaction (adjusted OR 1.55, 95% CI, 1.28-1.87), and resource use (adjusted OR 1.43, 95% CI, 1.16-1.76) compared with those deriving 5% or less of practice revenues from Medicaid. Within the group of physicians, physicians at practices with 50% or more of their revenues from Medicaid have significantly lower odds of receiving incentives for productivity (adjusted OR 0.69, 95% CI, 0.53-0.88).

After adjusting for covariates, there is no consistently significant relationship between use of our 4 performance incentives of interest and the percentage of patient panels that are Hispanic, African American, or facing language barriers.

Practice Characteristics and Physician Performance Incentives

The above multivariate analysis also confirms that the odds of physicians having variable compensation is significantly associated with practice type, level of capitation, and regional location, but not with physician specialty or area SES (Table 3). For example, physicians working in group/staff model HMOs are nearly 3 times as likely to report variable compensation as physicians working in solo/2-physician practices (adjusted OR 2.84, 95% CI, 1.79-4.51).

When evaluating the types of performance incentives; physician specialty, practice type, capitation level, and regional location are also—to varying degrees&mdash;significantly associated with incentives for productivity, care quality, patient satisfaction, and resource use in multivariate analysis (Table 4). For example, medicine subspecialists and surgeons have significantly lower odds of experiencing incentives for care quality, patient satisfaction, and resource use compared with primary care physicians, and surgeons alone have significantly greater odds of reporting incentives for productivity. Physicians in group/staff model HMOs have 2 to 8 times greater odds of reporting incentives for care quality, patient satisfaction, and resource use, but do not have different odds of exposure to productivity incentives.

DISCUSSION

First, the unadjusted results show that, in 2008, US physicians faced a variety of financial incentives to alter care quantity and quality, and that productivity incentives predominated. Although governmental and private payers implemented numerous large-scale interventions aimed at better aligning financial rewards with healthcare quality in the years immediately preceding the 2008 HTPS, the prevalence of productivity incentives remained similar to past waves of this survey’s predecessor, the Community Tracking Survey (Table 1).^{20,22,23,30,31}

Second, the adjusted multivariate results suggest how complex the relationship between physicians’ financial incentives and Medicaid were at that time. On the one hand, the more that practices derived revenue from Medicaid, the less likely physicians were to report having variable compensation. This finding corroborates anecdotal information about the prevalence of fixed salaries in underserved settings.^12,32-34 On the other hand, physicians receiving variable compensation were significantly more likely to report all 4 types of performance incentives if their practices derived 6% to 24% of their revenues from Medicaid. The observation is consistent with the idea that physicians working in practices that are able to modulate the degree to which their practices care for Medicaid-insured patients may be better able to create and operate performance-based incentive programs.⁴⁴ Practices may need to be of a certain size, level of infrastructure, or degree of technical sophistication in order to titrate Medicaid revenues or operate performance-based compensation programs.

Third, we show that the relationship between physicians’ financial incentives and the degree to which physicians serve patients of minority racial/ethnic backgrounds also appears complex. After adjusting for key covariates, physicians with patient panels that were more than 50% Hispanic were significantly less likely to report variable compensation. Perhaps this is because physicians working with large panels of Hispanic patients find it difficult to provide culturally appropriate care when productivity incentives are in place and negotiate alternate compensation arrangements. Or, possibly, Hispanic patients are located in settings where variable compensation is less common and our covariates were insufficient to adjust for those other factors. Or, another causative mechanism may exist that we are unaware of. Further qualitative work could shed important light on why physicians caring for Hispanic patients and physicians in underserved settings are more likely to receive fixed compensation.

Fourth, among physicians receiving variable compensation, there appeared to be no significant correlation between the degree to which physicians care for minority patients and the types of performance incentives physicians face after these adjustments (eg, practice type and setting). That performance incentives are no different for physicians who serve vulnerable patients versus those who do not is striking given the difference in the complexity of the clinical work and the magnitude of disparities in healthcare quality among these populations. This study underscores the notion that physicians working with greater proportions of minority patients may benefit from performance incentives that support disparity reduction.⁴⁵

Interest in aligning physicians’ financial incentives with desired healthcare processes and outcomes is only likely to rise as physicians increasingly seek employee positions in large provider organizations. Yet each incentive type has an advantage and disadvantage. Productivity incentives may increase visit access, but reduce the amount of time that physicians have with those patients.²² Quality and patient satisfaction incentives may reward physicians for addressing sociocultural barriers important to vulnerable populations,²⁵ but could also deter them if performance assessment is invalid or unreliable.⁴⁶ Resource use incentives may reduce waste, but exacerbate underuse of recommended services that is already prevalent within minority populations (eg, cancer screening, influenza vaccinations).^26,27,30 It is critical to understand how these incentives vary depending on patient, physician and practice characteristics to target interventions and pursue the “right blend” of financial incentives.⁵

Our study has 4 main limitations. First, the data are based on physician self-report and may not accurately reflect their actual financial incentives. However, we have no reason to suspect social desirability bias in responses and would expect even inaccurate responses to be informative. That is, if a physician believes that he or she is exposed to quality-based incentives, that physician is more likely to behave accordingly, even if the incentives are not actually present. Second, our analysis does not account for reputational incentives such as public reporting, which may have different effects among physicians and on healthcare disparities.⁴⁷ Third, although solo/2-physician practices responded that they are less likely to have variable compensation, those working in smaller practices may be more likely to be influenced by variation in their practices' profitability than their counterparts in staff-model practices. Fourth, this study does not contain any information on how financial incentives should be ideally structured for different care settings.

Nonetheless, our study has several strengths. First, it examines a time period that is between 2 different waves of payment reforms taking place across the nation. Second, it is, to our knowledge, the first study to examine relationships between financial incentives and the extent to which a physician treats various types of vulnerable patient populations. Third, it utilizes a nationally representative data set.

In summary, we found that in 2008, the majority of frontline physicians in our nation indicated receiving variable compensation, and that the most frequently reported performance measure was that for productivity. We also found that physicians’ financial incentives are significantly modified by the degree to which physicians care for patients who are Medicaid-insured or Hispanic, and not substantially altered by the degree to which they care for patients who are African American or face language barriers. It remains to be seen whether the ACA—with its emphasis on continued use of P4P, introduction of global payment strategies, and ACOs&mdash; will yield significant changes in the degree to which physicians face different types of financial incentives when they do and do not care for vulnerable populations.Author Affiliations: Division of General Pediatrics, Boston Children’s Hospital, Boston, MA (ATC); Department of Pediatrics, Harvard Medical School, Boston, MA (ATC); Section of General Internal Medicine, Department of Medicine, University of Chicago, Chicago, IL (MHC, MEP); Chicago Center for Diabetes Translation Research, University of Chicago, Chicago, IL (MHC, MEP); Center for Health and Social Sciences, University of Chicago, Chicago, IL (MHC, MEP); MacLean Center for Clinical Medical Ethics, University of Chicago, Chicago, IL (MHC, MEP); Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD (GCA); Center for Drug Safety and Effectiveness, Johns Hopkins School of Public Health, Baltimore, MD (GCA); Department of Pharmacy Practice, University of Illinois at Chicago School of Pharmacy, Chicago, IL (HT); Center for the Study of Race, Politics and Culture, University of Chicago, Chicago, IL (MEP).

Funding Sources: This research was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); Chicago Center for Diabetes Translation Research (P30 DK092949); and Diabetes Research and Training Center (P60 DK20595). At the time that this research was conducted, Dr Chien had a career development award from the Agency for Healthcare Research and Quality (K08 HS017146), Dr Peek was supported by the Robert Wood Johnson Foundation (RWJF) Harold Amos Medical Faculty Development program, and the Mentored Patient-Oriented Career Development Award of the National Institute of Diabetes and Digestive and Kidney Diseases (K23 DK075006), and Dr Alexander had career development awards from the Robert Wood Johnson Physician Faculty Scholars Program and the Agency for Healthcare Research and Quality (K08 HS15699-01A1). Support for Dr Chin is provided by a Midcareer Investigator Award in Patient-Oriented Research from the NIDDK (K24 DK071933).

Author Disclosures: The authors (ATC, MHC, GCA, HT, MEP) 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 (ATC, MHC, GCA, MEP); analysis and interpretation of data (ATC, MHC, GCA, HT, MEP); drafting of the manuscript (ATC, GCA, MEP); critical revision of the manuscript for important intellectual content (ATC, MHC, GCA, HT, MEP); statistical analysis (ATC, HT); administrative, technical, or logistical support (ATC, MEP); and supervision (ATC, MHC).

Address correspondence to: Alyna T. Chien, MD, MS, 300 Longwood Ave, Boston, MA 02115. E-mail: alyna.chien@childrens.harvard.edu.1. Committee on Quality of Health Care in America, Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: Committee on Quality of Health Care in America; 2001.

2. Hartman M, Catlin A, Lassman D, Cylus J, Heffler S. US health spending by age, selected years through 2004. Health Aff (Millwood). 2008;27(1):w1-w12.

3. Agency for Healthcare Research and Quality. National healthcare disparities report, 2010. Rockville, MD: U.S. Department of Health and Human Services; 2011.

4. H.R. 3590. 111th Congress. Patient Protection and Affordable Care Act. United States of America; 2009. Medicaid Services. Advance Payment Accountable Care Organization (ACO) Model. http://innovations.cms.gov/documents/payment-care/ AdvancePaymentsFactSheet_10_20_2011.pdf. Published 2011. Accessed October 27, 2011.

6. Chernew ME, Mechanic RE, Landon BE, Safran DG. Private-payer innovation in Massachusetts: the ‘Alternative Quality Contract’. Health Aff (Millwood). 2011;30(1):51-61.

7. Song Z, Safran DG, Landon BE, et al. Health care spending and quality in year 1 of the alternative quality contract. N Engl J Med. 2011;365(10):909-918.

8. Chien AT. Can pay for performance improve the quality of adolescent substance abuse treatment? Arch Pediatr Adolesc Med. 2012;166(10):964-965.

9. Prendergast C. The provision of incentives in firms. J Econ Lit. 1999;37(1):7-63.

10. Conrad DA, Christianson JB. Penetrating the “black box”: financial incentives for enhancing the quality of physician services. Med Care Res Rev. 2004;61(3 suppl):37S-68S.

11. Gosden T, Forland F, Kristiansen IS, et al. Capitation, salary, feefor- service and mixed systems of payment: effects on the behaviour of primary care physicians. Cochrane Database Syst Rev. 2000(3): CD002215.

12. Helmchen LA, Lo Sasso AT. How sensitive is physician performance to alternative compensation schedules? Evidence from a large network of primary care clinics. Health Econ. 2010;19(11):1300-1317.

13. Conrad DA, Sales A, Liang SY, et al. The impact of financial incentives on physician productivity in medical groups. Health Serv Res. 2002;37(4):885-906.

14. Shafrin J. Operating on commission: analyzing how physician financial incentives affect surgery rates. Health Econ. 2010;19(5): 562-580.

15. Conrad DA, Maynard C, Cheadle A, et al. Primary care physician compensation method in medical groups: does it influence the use and cost of health services for enrollees in managed care organizations? JAMA. 1998;279(11):853-858.

16. Hillman AL, Pauly MV, Kerstein JJ. How do financial incentives affect physicians’ clinical decisions and the financial performance of health maintenance organizations? N Engl J Med. 1989;321(2):86-92.

17. Frist B, Schroeder S. Changing the way physicians are paid: report of the National Commission On Physician Payment Reform. HealthAffairs Blog. http://healthaffairs.org/blog/2013/03/04/changingthe- way-physicians-are-paid-report-of-the-national-commission-onphysician- payment-reform/. Published 2013. Accessed March 14, 2012.

18. Coleman K, Reiter KL, Fulwiler D. The Impact of Pay-for-Performance on Diabetes Care in a Large Network of Community Health Centers. J Health Care Poor Underserved. 2007;18(4):966-983.

19. 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.

20. 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.

21. Chien AT, Chin MH, Davis AM, Casalino LP. Pay for performance, public reporting, and racial disparities in health care: how are programs being designed? Med Care Res Rev. 2007;64(5 suppl): 283S-304S.

22. Reschovsky J, Hadley J. Physician financial incentives: use of quality incentives inches up, but productivity still dominates. Issue Brief Cent Stud Health Syst Change. 2007(108):1-4.

23. Reschovsky JD, Hadley J, Landon BE. Effects of compensation methods and physician group structure on physicians’ perceived incentives to alter services to patients. Health Serv Res. 2006;41(4, pt 1):1200-1220.

24. Gold MR, Hurley R, Lake T, Ensor T, Berenson R. A national survey of the arrangements managed-care plans make with physicians. N Engl J Med.1995;333(25):1678-1683.

25. Keating NL, Landon BE, Ayanian JZ, Borbas C, Guadagnoli E. Practice, clinical management, and financial arrangements of practicing generalists. J Gen Intern Med. 2004;19(5, pt 1):410-418.

26. Pedersen CA, Rich EC, Kralewski J, Feldman R, Dowd B, Bernhardt TS. Primary care physician incentives in medical group practices. Arch Fam Med. 2000;9(5):458-462.

27. Remler DK, Donelan K, Blendon RJ, et al. What do managed care plans do to affect care? Results from a survey of physicians. Inquiry. 1997;34(3):196-204.

28. Kuhmerker K, Hartman T. Pay-for-performance in state Medicaid programs: Appendix B: state pay-for-performance program summaries. http://www.commonwealthfund.org/usr_doc/Kuhmerker_P4PstateMedicaidprogsappendixB. pdf?section=4039. Published 2007. Accessed March 14, 2012.

29. Rosenthal MB, Fernandopulle R, Song HR, Landon B. Paying for quality: providers’ incentives for quality improvement. Health Aff (Millwood). 2004;23(2):127-141.

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

31. Kuhmerker K, Hartman T. Pay-for-performance in state Medicaid programs: a survey of state Medicaid directors and programs. The Commonwealth Fund; April 2007.

32. Andreae MC, Blad K, Cabana MD. Physician compensation programs in academic medical centers. Health Care Manage Rev. 2006;31(3):251-258.

33. Bach PB, Pham HH, Schrag D, Tate RC, Hargraves JL. Primary care physicians who treat blacks and whites. N Engl J Med. 2004;351(6): 575-584.

34. Reschovsky JD, O’Malley AS. Do primary care physicians treating minority patients report problems delivering high-quality care? Health Aff (Millwood). 2008;27(3):w222-w231.

35. Doescher MP, Saver BG, Fiscella K, Franks P. Racial/ethnic inequities in continuity and site of care: location, location, location. Health Serv Res. 2001;36(6, pt 2):78-89.

36. Center for Studying Health System Change. CTS Physician Surveys and the HSC 2008 Health Tracking Physician Survey; 2012. http://www .hschange.com/index.cgi?data=04.

37. Deloitte Center for Health Solutions. Deloitte 2013 Survey of U.S. Physicians: Physician perspectives about health care reform and the future of the medical profession. Published 2013.

38. American Medical Association. Physician Practice Information Survey. http://www.ama-assn.org/ama/pub/physician-resources/solutions- managing-your-practice/coding-billing-insurance/the-resourcebased- relative-value-scale/physician-practice-information-survey.page. Published 2013. Accessed June 6, 2013.

39. Centers for Disease Control and Prevention. National Ambulatory Medical Care Survey. http://www.cdc.gov/nchs/ahcd/about_ahcd.htm. Updated July 14, 2009.

40. Pham HH, Landon BE, Reschovsky JD, Wu B, Schrag D. Rapidity and modality of imaging for acute low back pain in elderly patients. Arch Intern Med. 2009;169(10):972-981.

41. Allen S. Medicaid and pediatric accountable care organizations: a case study. Accountable Care News. 2010;1(5).

42. Sun GW, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol. 1996;49(8):907-916.

43. Center for Studying Health System Change. HSC 2008 Health Tracking Physician Survey Methodology Report. 2009. 77.

44. Chien AT, Wroblewski K, Damberg C, et al. Do physician organizations located in lower socioeconomic status areas score lower on payfor- performance measures? J Gen Intern Med. 2011;27(5):548-554.

45. Children’s Health Insurance Program Reauthorization Act of 2009. Pub L 111-3, 123 Stat 8.

46. Hofer TP, Hayward RA, Greenfield S, Wagner EH, Kaplan SH, Manning WG. The unreliability of individual physician “report cards” for assessing the costs and quality of care of a chronic disease. JAMA. 1999;281(22):2098-2105. 47. Werner RM, Asch DA, Polsky D. Racial profiling: the unintended consequences of coronary artery bypass graft report cards. Circulation. 2005;111(10):1257-1263.