Pay for Performance Affects Access to Healthcare


Pay-for-performance (P4P) can increase the quality of healthcare for the services under review or assessment; however, unless carefully designed, P4P may create unintended consequences by increasing racial and ethnic disparities and as a result, decreasing access to healthcare.



  1. What problems does Pay for Performance Intend to Solve?

The quality of healthcare in the United States is variable and it is not what it capable of being, given the nation’s specialized resources.1 During the past decade, public and private purchasers of health care have begun implementing “pay-for-performance” (P4P) programs to induce physicians and hospitals to invest in improving quality of healthcare.2 During the same period, it has become evident that the quality of care for racial and ethnic minority patients is worse than that for whites, and increasing attempts have been made to develop interventions to help physicians and hospitals reduce this disparity.3

While the traditional fee for reimbursement service methods has been paying for physician services that use recognized and acceptable fee schedule, payments for healthcare rewards providers for the patient care volume. The greater the service amount, the higher the compensation and vice versa. With this model of incentive, the costs of health care are increasing at an alarming rate of 5.3 % annually. In the year 2014, health care system accounted for 17.7 % of the Gross Domestic Product (GDP), and economic projections indicate that it will reach 19.6% of the GDP by the year 2024.10 (See Appendix A). Nonetheless, even with these different costs, the U.S health care system is at the lowest rank among industrialized nations regarding price, quality, efficiency, health care outcomes and access dimensions.11



The U.S healthcare system is at the lowest rank because the ranking considers the entire nation’s population rather than the privileged people who can pay no matter the cost.12 (See Appendix B). Besides, the U.S ranks lowest concerning women death during difficult childbirth, adult heart-related mortality, childhood mortality, as well as the pulmonary disease incidence.10 The financial burden of the increasing healthcare costs as well as the relatively poor outcomes have been forcing medical policy makers to invent alternate payment models to ensure the provision of greater value. The aim is to try to ameliorate patient access while at the same time controlling costs and maintaining quality. Healthcare payments by the public and private payer will reimburse providers by complex formulas incorporating P4P in the reimbursements. Credible estimations indicate that, by the year twenty-twenty, fifty percent of compensations by CMC (Center for Medicare and Medicaid Services) will embrace an alternative P4P model.12 The common practice of regulating and reimbursing medicine by private and governmental healthy policy tries to balance quality, cost, and access to care.



P4P can have serious unintended consequences, and one of these consequences may be to increase health care disparities.4 During the next few years, as the number and size of programs expands—including, perhaps, the introduction of P4P for physicians by the Centers for Medicare and Medicaid Services (CMS)—there will be an important opportunity to design programs to minimize unintended consequences and to reduce disparities.5 The purpose of this paper is to explore two questions: 1) does P4P decrease access to healthcare and 2) how can these programs be designed to reduce disparities instead of increasing them?



Before further reading, it is very important to address stipulations regarding the scope of this research paper. First, an increase in health disparity directly leads to a decrease in access to care. Second, P4P should be understood as an external incentive—that is, an incentive in addition to physicians’ professional desire to provide better care—to induce physicians to invest in improving quality of healthcare. Third, the term “external incentives” refers to P4P throughout the paper. Fourth, the term “minority patients” refers to patients who are members of ethnic/racial minority groups. Fifth, the assumption that “minority patients” vary tremendously in income, education, acculturation to the United States, and English language ability is made throughout the paper. Sixth, the term “at-risk patients” typically encompasses the “minority patients.” Seventh, the unintended consequences discussed are likely to be more severe for the more disadvantaged range of this spectrum. It is important to not misconstrue what is being said. Although the low-income, poorly educated white patients are not usually included in the term “minority patients,” it is important to note that similar unintended consequences of external incentive programs may increase health care disparities between affluent and poor whites as well


What is access to care?

There is a need first to evaluate what it means by “access to health care” to have a solid understanding of the impact of P4P on healthcare access. Due to the complexity of the concept of health care access, we require four dimensions for evaluation. First, is the availability of services with an adequate supply which implies that there exists an opportunity to “have access.” Second, the extent to which a particular population gains access surpasses the financial, socio-cultural, and organizational barriers that may limit or hinder the utilization of services. Third, the measurement of access is utilization which is proportional to the availability of services, physical accessibility, and affordability. Fourth, there should be an existence of services to gain access to satisfactory health results. There is the need to consider the availability of services as well as barriers to access in the context of different aspects, health needs, and cultural affiliations of diverse people in the community.3


P4P Increases Disparities in Health Care Delivery

Empirical data on the effects of P4P and public quality reporting are still relatively scarce; the evidence to date may be summarized as suggesting that external incentives lead to positive, but generally modest, improvements in the areas of quality that are measured, at least in the short run.6 There are very few data available on the effects of the external incentives on disparities in quality; they may be summarized by stating that such effects do in fact occur.7 This paper will address three ways in which external incentives for quality may have the unintended consequence of increasing health care disparities and decreasing access to healthcare.


  1. Physicians Earn Lower Incomes in Poor Minority Communities.

P4P may adversely affect the income of physicians practicing in minority communities, particularly poor minority communities. This effect on income could potentially reduce both the number of physicians who work in such communities as well as their ability to invest in processes to improve quality.14 Physicians in these areas are doubly disadvantaged in trying to achieve high quality scores: First, their “payer mix” is likely to include a high proportion of uninsured and Medicaid patients, so there will be less revenue for them to invest in information systems, staff, and the development of organized processes to improve quality.15 Second, patients in these areas might be less likely to adhere to treatment recommendations.16-17 These patients might, for example, be less likely to obtain preventive care such as mammograms and pap smears and less likely to return for follow-up of abnormal results, because of problems with transportation and child care and because of difficulty in comprehending the recommendation.18-19 If compared directly to physicians in wealthier areas, physicians in poor minority communities might be less likely to receive P4P incentive pay and more likely to be listed in public report cards as poor-quality physicians. Health care plans often require patients to make higher copayments for seeing these “poor-quality” physicians, resulting in poor patients not being able to see physicians that are located in their communities. Only one research study provides data directly relevant to this “poor may get poorer” problem: In the first evaluation of a P4P program recently introduced for primary care physicians by the British National Health Service (NHS), practices that served lower-income populations had lower quality scores.20


  1. Patient Care Loses Its Holistic Approach

P4P could induce physicians to focus their time and attention on types of care that are being measured, to the detriment of non-measured areas that could be equally or more important. This “teaching to the test,” could disproportionately affect minorities.21 For example, with a relatively uneducated diabetic patient who speaks poor English, the physician might focus on making sure the patient has a hemoglobin A1c test but not on the time-consuming task of explaining to the patient how to control his or her diabetes and blood pressure.31sg The same physician might not “teach to the test” to the same degree with a better educated, English-speaking patient, because he or she is more comfortable with that patient or believes that the patient is more capable of adhering to a therapeutic regimen, or because the patient is more assertive in demanding time and explanation from the physician.


  1. Avoiding at-risk patients.

P4P might induce individual physicians and medical groups to avoid patients whom they perceive as being likely to lower their quality scores (at-risk patients), particularly if quality measures are not adequately adjusted for the patients’ overall health status and perhaps for racial or socioeconomic characteristics as well. At-risk patients refer to those who have health care costs of higher than average and are traditionally considered to have a greater health care disparity from the population health mean.22-23 There is ample evidence that shows physicians perceive minority patients as less likely to comply with their recommendations for treatment and preventive services and more likely to have bad outcomes. There is also evidence that physicians subject to external incentives will try to avoid minority patients because they perceive them as more likely to have poor outcomes from treatments, even when this is not the case.17 After New York State initiated its report card measuring death rates from coronary artery bypass graft (CABG) surgery for individual surgeons and hospitals, the gap between CABG rates for whites and blacks increased. The report card appears to have made surgeons more reluctant to operate on black patients.18 (See Appendix C)

It is widely recognized that outcome measures of quality should be adjusted for patients’ health status, and some attempt to do so is often made. However, racial or socioeconomic factors, or both, may also be associated with worse outcomes.19 For example; hospitalized children whose parents did not speak English well had a twofold increased risk for a serious medical event.20

The use of process measures, rather than outcome measures, is often proposed as a solution to the problem that patients’ characteristics, in addition to physicians’ actions, affect quality scores. It is often stated, without evidence being cited, that patients’ characteristics do not affect physicians’ scores on process measures and therefore that process measures, unlike outcome measures, need not be adjusted for patients’ health status or socioeconomic status.21 To practicing physicians, this might seem implausible. For example, it should be easier for physicians who practice in affluent areas such as Marin County, California, than for those who practice in poor areas of Oakland to achieve higher rates of screening mammography: Their patients are more likely to be wealthy, well-educated, well-insured women who are aware of the benefits of mammograms, are more likely to request them, and are more able to travel to obtain them.

Many studies have indicated that scores on process measures are likely to be affected by the characteristics of patients, including their health status, primary language, and socioeconomic status (SES), although one important recent study showed little or no effect.22 Patients with lower SES have been found to be less likely to obtain Pap smears, mammograms, and diabetic eye exams, holding physician practice constant.23 Health plan scores on Health Plan Employer Data and Information Set (HEDIS) measures have been shown to be affected by patients’ race and SES.24 Fully insured women with diabetes have been found to be one-third less likely than women without diabetes to have a screening mammogram.25 Patients with chronic illnesses, which are more common among minorities, may find it more difficult to travel to obtain these tests and may be less interested in having them than is the case for healthy patients, and their physicians may be so focused on one of the patients’ illnesses that preventive care and the care of other illnesses may slip through the cracks.26


Potential Solutions

Creating programs likely to reduce, or at least not to increase, disparities will not be easy, technically or politically. The discussion below suggests three design elements that, if implemented, would be likely to help increase access to these at-risk patients.


  1. Use risk adjustment or stratified analyses.

To the extent that minority patients, especially poor minority patients, have worse health status, risk adjustment could be used to minimize physicians’ incentive to avoid minority patients. Risk adjustment should be used for process as well as outcome measures, except where data show that adjustment is not necessary. Risk adjustment for health status alone would not be enough, however. Adjustment for patients’ race/ethnicity or SES, or both, would be necessary to level the playing field.

Stratification is the process of dividing members of the population into homogeneous subgroups before sampling.26 Stratified analyses could be used instead of or in addition to risk adjustment. For example, physicians practicing in poor minority areas could be compared with, for the sake of P4P, other physicians practicing in similar areas. Alternatively, organizations could be compared based on their care of minority patients in general, of African American patients, of low-SES patients, and so on. Public reporting programs would provide measures of a physician’s, medical groups’, hospitals’, or health plan’s care for these populations. P4P programs would provide rewards both for overall quality scores and for scores for minority populations, thus directly incentivizing the reduction of disparities32-34.


Stratified analyses could have three other advantages as well. First, it can provide information that might be useful for directing quality improvement activities—for example, how physicians in a group are performing for specific categories of patients. Second, while risk adjustment at best reduces physicians’ incentive to avoid certain patients, stratification gives a more tangible and positive incentive: “If you provide high-quality care for minority patients, we will pay you more.”34 Third, compared to risk adjustment, stratified analyses are more transparent and may be more appealing to physicians because they can rely on hard fact and statistics. However, implementing stratification and risk adjustment is easier said than done. There are sizable technical barriers to using stratification or risk adjustment for minority and low-SES patients. The measures of race/ethnicity and SES would have to be defined, the data would have to be collected, and risk-adjustment methods would have to be refined. There is an additional, very important barrier to using stratification. Unlike risk adjustment, which can be done for each individual patient, reliable and valid stratified analyses of quality require that a physician or medical groups have a large number of, for example, African American patients with diabetes. Most do not, so stratified analyses would be reserved for large medical groups, hospitals, and health plans—and even some of these organizations might not have enough minority patients to permit stratified analyses.

In addition, risk adjustment and stratification are two-edged swords: They would reduce physicians’ incentive to avoid patients perceived as likely to lower their quality scores but would also reduce their incentive to improve care for these patients. By rewarding physicians even when their quality scores for minority patients are lower, P4P programs would avoid penalizing physicians for caring for these patients but would also risk rewarding them for continuing to provide mediocre care.35-38 This problem probably cannot be entirely eliminated but would be reduced by basing rewards partly on absolute quality scores and partly on improvement over time. When sufficient numbers of patients are involved to permit reliable measurements, P4P programs might reward both absolute quality scores and improvement for an organization’s patient population as a whole and—based on stratified analyses—for the organization’s minority patients. Public reporting programs could report both overall quality scores and stratified scores for minority patients 39-41.

An alternative approach, used by the British NHS P4P program, is to permit physicians to exclude patients from quality measurements be designating them as “exceptions,” based on rather broad criteria.    Overall, physicians designated only 6 percent of patients as exceptions, but the range of patients so designated varied from zero to 86 percent. One percent of practices excluded more than 15 percent of their patients.


  1. Reward both overall quality and reduction in disparities.

As just noted, external incentives could be given both for quality of care for all an organization’s patients as well as for one or more subgroups of patients—specifically, the minority patients. Alternatively, incentives could be given both for quality for all of an organization’s patients and for reducing quality disparities between white and minority patients. Either of these alternatives would be technically challenging and politically controversial. Massachusetts, however, has recently announced a plan to include reduction of racial and ethnic disparities as a goal for its Medicaid P4P program for hospitals.42-46


  1. Methods to minimize the “teaching to the test” problem.

Rotating the measures used or adding new measures at relatively frequent intervals would reduce the “teaching to the test” problem. Including data from patient satisfaction surveys could counterbalance incentives to focus on narrow quality measures at the expense of communicating with patients and coordinating patient care. More ambitiously, in a model that comes from agency theory, some or all high-scoring physicians (or medical groups) would be evaluated on a second set of quality measures that would not be announced in advance 47-47. If they score well on these measures, they would receive substantial additional reward, but if they score poorly, they would lose some of their reward for scoring well on the initial set of measures. Payments would be designed so that physicians would, on balance, expect to gain from being scored on the second set of measures and would want to be scored on them. Since the second set of measures would involve a limited number of physicians or medical groups, they could (although need not) be directed at types of quality that are important but relatively costly to measure—for example, measures that require review of medical charts.49



Awareness that P4P and public reporting may increase disparities does exist among the leaders of medical societies, organizations that devise quality measures, organizations that are creating public reporting and P4P programs, and among public policymakers.50- 52 Nevertheless, it appears that most P4P and public reporting programs are not well designed to avoid increasing disparities, much less to reduce them.53 The next few years will be critical for the future of P4P and public reporting programs in the United States. These programs are proliferating rapidly but do not appear to be devoting a great deal of attention to their possible effects on health care disparities. Programs designed to minimize the unintended consequences of increasing disparities will be more costly and time-consuming to implement, maintain, and modify over time than those that ignore the possibility of such consequences. But they should have more staying power and should be less likely to generate a backlash from physicians and patients.54 Only well-designed programs are likely to lead both to improved quality for all and to the reduction of disparities in health care. Finding ways to reduce care gaps instead of enhancing them is vital to ensuring the integrity of the pay-for-performance model and providing true quality care for all patients, no matter what their health or economic status is.


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