Patient-Centered Quality Measures for Dialysis Care: A Report of a Kidney Disease Outcomes Quality Initiative (KDOQI) Scientific Workshop Sponsored by the National Kidney Foundation.

Providing high-quality patient-centered care is the central mission of dialysis facilities. Assessing quality and patient-centeredness of dialysis care is necessary for continuous dialysis facility improvement. Based predominantly on readily measured items, current quality measures in dialysis care emphasize biochemical and utilization outcomes, with very few patient-reported items. Additionally, current metrics often do not account for patient preferences and may compromise patient-centered care by limiting the ability of providers to individualize care targets, such as dialysis adequacy, based on patient priorities rather than a fixed numerical target. Developing, implementing, and maintaining a quality program using readily quantifiable data while also allowing for individualization of care targets that emphasize the goals of patients and their care partners provided the motivation for a September 2022 Kidney Disease Outcomes Quality Initiative (KDOQI) Workshop on Patient-Centered Quality Measures for Dialysis Care. Workshop participants focused on 4 questions: (1) What are the outcomes that are most important to patients and their care partners? (2) How can social determinants of health be accounted for in quality measures? (3) How can individualized care be effectively addressed in population-level quality programs? (4) What are the optimal means for collecting valid and robust patient-reported outcome data? Workshop participants identified numerous gaps within the current quality system and favored a conceptually broader, but not larger, quality system that stresses highly meaningful and adaptive measures that incorporate patient-centered principles, individual life goals, and social risk factors. Workshop participants also identified a need for new, low-burden tools to assess patient goals and priorities.

Using race as a case-mix adjustment factor in a renal dialysis payment system: potential and pitfalls.

BACKGROUND: Racial disparities in health care are widespread in the United States. Identifying contributing factors may improve care for underserved minorities. To the extent that differential utilization of services, based on need or biological effect, contributes to outcome disparities, prospective payment systems may require inclusion of race to minimize these adverse effects. This research determines whether costs associated with end-stage renal disease (ESRD) care varied by race and whether this variance affected payments to dialysis facilities. STUDY DESIGN: We compared the classification of race across Medicare databases and investigated differences in cost of care for long-term dialysis patients by race. SETTING & PARTICIPANTS: Medicare ESRD database including 890,776 patient-years in 2004-2006. PREDICTORS: Patient race and ethnicity. OUTCOMES: Costs associated with ESRD care and estimated payments to dialysis facilities under a prospective payment system. RESULTS: There were inconsistencies in race and ethnicity classification; however, there was significant agreement for classification of black and nonblack race across databases. In predictive models evaluating the cost of outpatient dialysis care for Medicare patients, race is a significant predictor of cost, particularly for cost of separately billed injectable medications used in dialysis. Overall, black patients had 9% higher costs than nonblack patients. In a model that did not adjust for race, other patient characteristics accounted for only 31% of this difference. LIMITATIONS: Lack of information about biological causes of the link between race and cost. CONCLUSIONS: There is a significant racial difference in the cost of providing dialysis care that is not accounted for by other factors that may be used to adjust payments. This difference has the potential to affect the delivery of care to certain populations. Of note, inclusion of race into a prospective payment system will require better understanding of biological differences in bone and anemia outcomes, as well as effects of inclusion on self-reported race.