Pay for Performance (P4P), designed to be added pay “for greater performance” to health care providers, can have serious unintended consequences, such as an increase in health care disparities and decrease in access to healthcare. This is especially true as providers improve their complication profile with at risk patients. In addition, the prospective of the consumer, the patient, must be included in solutions to improve P4P.
Potential Solutions:
The use of risk adjustment combined with stratified analyses.
Risk adjustment is a tool used by payers to standardize payments to health providers based on the relative health of the patient populations. Risk adjustment payment could be used to incentivize physicians’ reimbursements for those caring for at risk patients. Simply stated, greater payment to those who care for sicker patients, at risk patients. However, the details of this risk adjustment can be misleading.
Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. Stratified analyses could be beneficial to increasing access to at risk, minority patients. For example, in a geographic area, there is town A of population 100,000 that has mostly migrant workers; and Town B population of 200,000 with mostly has white collar workers; and town C of 300,000 mostly retires. To analyze 60 random patients from this area may not reflect the true representation of the population in terms of normalizing health and access to care. However, if we analyze 10, 20, and 30 patients from town A, B, and C respectively, there may be less sampling error with the representation of true patient mean. The combination of this risk adjustment factor and stratified analysis together would decrease the disparity of the physicians’ practices in poor, minority areas compared with other physicians practicing in more affluent areas. Because of stratified analysis, medical organizations could more accurately determine true risk adjusted factors.
Stratified analyses could lead to three other positive outcomes that can benefit the overall healthcare system. First, it can provide data and statistics that might be useful for directing quality improvement initiatives such as how physicians in a particular group are performing for a specific group of patients. Second, stratification analysis is more beneficial than risk adjustment alone. While risk adjustment alone reduces physicians’ incentives to avoid at-risk patients, stratification gives physicians an incentive to provide high quality care for minority patients. Third, stratified analyses rely on numbers and statistics, thus objective in presenting outcome data.
Even though risk adjustment and stratification could potentially improve access to care for minority patients, implementing these systems is a daunting task. There are sizable technical barriers to using these two systems to increase access for minority, at-risk patients. The measures of race or ethnicity would have to be defined, the data would have to be collected, and risk-adjustment methods would have to be refined. This is in of itself a very arbitrary and abstract idea to measure. To benefit from the stratified analysis requires that a physician sees a large number of at risk patients. This is mostly dictated to where the physician practices.
It is also important to note that even though risk adjustment and stratification would reduce physicians’ incentives to avoid at-risk patients, these two systems of implementation would also reduce physicians’ incentives to improve care for these at-risk patients. Physicians would be given incentives to provide care for these minority patients and this would lead to more access to care for deprived patients. However, stratification analyses and risk adjustment are double edged swords: there is a positive and a negative outcome. P4P programs would risk rewarding physicians for continuing to provide mediocre care. Access to poor care does not improve access to care. Every individual deserves access to quality healthcare. This problem cannot be entirely eliminated but would be reduced by basing incentives partly on absolute quality scores and partly on patients’ 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.
Adding another Dimension to Quality
Adding patient satisfaction factor to the overall P4P data would broaden the quality of care representation to a holistic representation of the patient. Improvement of provider reimbursement for better patient satisfaction data incentivizes more caring providers as perceived by the consumer.
Conclusion
Policy makers in the healthcare industry know of the unintended consequences of P4P. P4P programs were intended to improve quality of care and not well designed to avoid increasing disparities. Creating programs likely to reduce, or at least not to increase, disparities will not be easy. These next few years will be very interesting for healthcare policy and reform. These P4P programs are currently becoming the “go-to” solution to improve quality of care. Well-designed programs that address these present issues are likely to improve quality for all and to the reduce 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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