Measuring health disparities using a continuous social risk factor.

OBJECTIVE: To propose and evaluate a novel approach for measuring hospital-level disparities according to the effect of a continuous, polysocial risk factor on those outcomes. STUDY SETTING: Our cohort consisted of Medicare Fee-for-Service (FFS) patients 65 years and older admitted to acute care hospitals for one of six common conditions or procedures. Medicare administrative claims data for six hospital readmission measures including hospitalizations from July 2015 to June 2018 were used. STUDY DESIGN: We adapted existing methodologies that were developed to report hospital-level disparities using dichotomous social risk factors (SRFs). The existing methods report disparities within and across hospitals; we developed and tested modified approaches for both methods using the Agency for Healthcare Research and Quality Socioeconomic Status Index. We applied the adapted methodologies to six 30-day hospital readmission measures included in the Centers for Medicare & Medicaid Services Hospital Readmissions Reduction Program measures. We compared the within- and across-hospital results for each to those obtained from using the original methods and dichotomizing the AHRQ SES Index into "low" and "high" scores. DATA COLLECTION: We used Medicare FFS administrative claims data linked to U.S. Census data. PRINCIPAL FINDINGS: For all six readmission measures we find that, when compared with the existing methods, the methods for continuous SRFs provide disparity results for more facilities though across a narrower range of values. Measures of disparity based on this approach are moderately to highly correlated with those based on a dichotomous version of the same risk factor, while reflecting a fuller spectrum of risk. This approach represents an opportunity for detection of provider-level results that more closely align with underlying social risk. CONCLUSION: We have demonstrated the feasibility and utility of estimating hospital disparities of care using a continuous, polysocial risk factor. This approach expands the potential for reporting hospital-level disparities while better accounting for the multifactorial nature of social risk on hospital outcomes.

Incorporating Present-on-Admission Indicators in Medicare Claims to Inform Hospital Quality Measure Risk Adjustment Models.

Importance: Present-on-admission (POA) indicators in administrative claims data allow researchers to distinguish between preexisting conditions and those acquired during a hospital stay. The impact of adding POA information to claims-based measures of hospital quality has not yet been investigated to better understand patient underlying risk factors in the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision setting. Objective: To assess POA indicator use on Medicare claims and to assess the hospital- and patient-level outcomes associated with incorporating POA indicators in identifying risk factors for publicly reported outcome measures used by the Centers for Medicare & Medicaid Services (CMS). Design, Setting, and Participants: This comparative effectiveness study used national CMS claims data between July 1, 2015, and June 30, 2018. Six hospital quality measures assessing readmission and mortality outcomes were modified to include POA indicators in risk adjustment models. The models using POA were then compared with models using the existing complications-of-care algorithm to evaluate changes in risk model performance. Patient claims data were included for all Medicare fee-for-service and Veterans Administration beneficiaries aged 65 years or older with inpatient hospitalizations for acute myocardial infarction, heart failure, or pneumonia within the measurement period. Data were analyzed between September 2019 and March 2020. Main Outcomes and Measures: Changes in patient-level (C statistics) and hospital-level (quintile shifts in risk-standardized outcome rates) model performance after including POA indicators in risk adjustment. Results: Data from a total of 6 027 988 index admissions were included for analysis, ranging from 491 366 admissions (269 209 [54.8%] men; mean [SD] age, 78.2 [8.3] years) for the acute myocardial infarction mortality outcome measure to 1 395 870 admissions (677 158 [48.5%] men; mean [SD] age, 80.3 [8.7] years) for the pneumonia readmission measure. Use of POA indicators was associated with improvements in risk adjustment model performance, particularly for mortality measures (eg, the C statistic increased from 0.728 [95% CI, 0.726-0.730] to 0.774 [95% CI, 0.773-0.776] when incorporating POA indicators into the acute myocardial infarction mortality measure). Conclusions and Relevance: The findings of this quality improvement study suggest that leveraging POA indicators in the risk adjustment methodology for hospital quality outcome measures may help to more fully capture patients' risk factors and improve overall model performance. Incorporating POA indicators does not require extra effort on the part of hospitals and would be easy to implement in publicly reported quality outcome measures.

Nearly One In Five Skilled Nursing Facilities Awarded Positive Incentives Under Value-Based Purchasing.

Medicare's Skilled Nursing Facility Value-Based Purchasing Program, which awards value-based incentive payments based on hospital readmissions, distributed its first two rounds of incentives during fiscal years 2019 and 2020. Incentive payments were based on achievement or improvement scores-whichever was better. Incentive payments were as low as -2.0 percent in both program years and as high as +1.6 percent in FY 2019 and +3.1 percent in FY 2020. In FY 2019, 26 percent of facilities earned positive incentives and 72 percent earned negative incentives, compared with 19 percent positive and 65 percent negative incentives in FY 2020. Larger, rural, and not-for-profit facilities were more likely to earn positive incentives, as were those with the highest registered nurse staffing levels. Although these findings indicate the potential to reward high-quality care at skilled nursing facilities, intended and unintended outcomes of this new value-based purchasing program should be monitored closely for possible program refinements, particularly in light of the disproportionate impacts of coronavirus disease 2019 (COVID-19) on nursing facilities.

Assessment of the Medicare quality improvement organization program.

BACKGROUND: Studies have shown improvement in quality of health care in the United States. However, the factors responsible for this improvement are largely unknown. OBJECTIVE: To evaluate the effect of the Medicare Quality Improvement Organization (QIO) Program in 4 clinical settings by using performance data for 41 quality measures during the 7th Scope of Work. DESIGN: Observational study in which differences in quality measures were compared between baseline and remeasurement periods for providers that received different levels of QIO interventions. SETTING: Nursing homes, home health agencies, hospitals, and physician offices in the 50 U.S. states, the District of Columbia, and 2 U.S. territories. PARTICIPANTS: Providers receiving focused QIO assistance related to quality measures and providers receiving general informational assistance from QIOs. MEASUREMENTS: 5 nursing home quality measures, 11 home health measures, 21 hospital measures, and 4 physician office measures. RESULTS: For nursing home, home health, and physician office measures, providers recruited specifically by QIOs for receipt of assistance showed greater improvement in performance on 18 of 20 measures than did providers who were not recruited; similar improvement was seen on the other 2 measures. Nursing homes and home health agencies improved more in all measures on which they chose to work with the QIO than in other measures. Nineteen of 21 hospital measures showed improvement; in this setting, QIOs were contracted for improvement initiatives solely at the statewide level. Overall, improvement was seen in 34 of 41 measures from baseline to remeasurement in the 7th Scope of Work. LIMITATIONS: As in any observational study, selection bias, regression to the mean, and secular trends may have influenced the results. CONCLUSIONS: These findings are consistent with an impact of the QIO Program and QIO technical assistance on the observed improvement. Future evaluations of the QIO Program will attempt to better address the limitations of the design of this study.