Andrew C Qi1, Alina A Luke1, Charles Crecelius2, Karen E Joynt Maddox1,3. 1. Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri. 2. Post-Acute and Long Term Care Services, Barnes Jewish Christian Medical Group, St. Louis, Missouri. 3. Center for Health Economics and Policy, Institute for Public Health, Washington University in St. Louis, St. Louis, Missouri.
Abstract
BACKGROUND/ OBJECTIVES: Launched in October 2018, Medicare's Skilled Nursing Facility Value-Based Purchasing (SNF VBP) program mandates financial penalties for SNFs with high 30-day readmission rates. Our objective was to identify characteristics of SNFs associated with provider performance under the program. DESIGN: Retrospective cross-sectional analysis using Nursing Home Compare data for the 2019 SNF VBP. Facility-level regressions examined the relationship between structural characteristics (nursing home size, rurality, profit status, hospital affiliation, region, and Star Ratings) and patient characteristics (neighborhood income, race/ethnicity, dual eligibility, disability, and frailty) and facility performance. SETTING: US Medicare. PARTICIPANTS: A total of 14 558 SNFs. MEASUREMENTS: The 2019 SNF VBP performance scores and penalties. RESULTS: Nationally, 72% (10 436) of SNFs were penalized; 21% (2996) received the maximum penalty of 1.98%. In multivariate analyses, rural SNFs were less likely to be penalized (odds ratio [OR] = 0.85; 95% confidence interval [CI] = 0.78-0.92; P < .001; vs urban), while small SNFs were more likely to be penalized (≤70 beds: OR = 1.28; 95% CI = 1.15-1.42; P < .001; 71-120 beds: OR = 1.15; 95% CI = 1.05-1.26; P = .003; vs >120 beds). SNFs with lower nurse staffing had higher odds of penalties (low: OR = 1.15; 95% CI = 1.03-1.27; P = .010; vs high); nonprofit and government-owned SNFs had lower odds of penalties (OR = 0.79; 95% CI = 0.72-0.87; P < .001; government: OR = 0.72; 95% CI = 0.61-0.84; P < .001; vs for profit); and SNFs with higher Star Ratings had lower odds of penalties (5 stars: OR = 0.47; 95% CI = 0.40-0.54; P < .001; vs 1 star). In terms of patient population, SNFs located in low-income ZIP codes (OR = 1.17; 95% CI = 1.03-1.34; P = .019) or serving a high proportion of frail patients (OR = 1.39; 95% CI = 1.21-1.60; P < .001) were more likely to be penalized than other SNFs. SNFs with high proportions of dual, black, Hispanic, or disabled patients did not have higher odds of penalization. CONCLUSION: Structural and patient characteristics of SNFs may significantly impact provider performance under the SNF VBP. These findings have implications for policy makers and clinical leaders seeking to improve quality and avoid unintended consequences with VBP in SNFs. J Am Geriatr Soc 68:826-834, 2020.
BACKGROUND/ OBJECTIVES: Launched in October 2018, Medicare's Skilled Nursing Facility Value-Based Purchasing (SNF VBP) program mandates financial penalties for SNFs with high 30-day readmission rates. Our objective was to identify characteristics of SNFs associated with provider performance under the program. DESIGN: Retrospective cross-sectional analysis using Nursing Home Compare data for the 2019 SNF VBP. Facility-level regressions examined the relationship between structural characteristics (nursing home size, rurality, profit status, hospital affiliation, region, and Star Ratings) and patient characteristics (neighborhood income, race/ethnicity, dual eligibility, disability, and frailty) and facility performance. SETTING: US Medicare. PARTICIPANTS: A total of 14 558 SNFs. MEASUREMENTS: The 2019 SNF VBP performance scores and penalties. RESULTS: Nationally, 72% (10 436) of SNFs were penalized; 21% (2996) received the maximum penalty of 1.98%. In multivariate analyses, rural SNFs were less likely to be penalized (odds ratio [OR] = 0.85; 95% confidence interval [CI] = 0.78-0.92; P < .001; vs urban), while small SNFs were more likely to be penalized (≤70 beds: OR = 1.28; 95% CI = 1.15-1.42; P < .001; 71-120 beds: OR = 1.15; 95% CI = 1.05-1.26; P = .003; vs >120 beds). SNFs with lower nurse staffing had higher odds of penalties (low: OR = 1.15; 95% CI = 1.03-1.27; P = .010; vs high); nonprofit and government-owned SNFs had lower odds of penalties (OR = 0.79; 95% CI = 0.72-0.87; P < .001; government: OR = 0.72; 95% CI = 0.61-0.84; P < .001; vs for profit); and SNFs with higher Star Ratings had lower odds of penalties (5 stars: OR = 0.47; 95% CI = 0.40-0.54; P < .001; vs 1 star). In terms of patient population, SNFs located in low-income ZIP codes (OR = 1.17; 95% CI = 1.03-1.34; P = .019) or serving a high proportion of frail patients (OR = 1.39; 95% CI = 1.21-1.60; P < .001) were more likely to be penalized than other SNFs. SNFs with high proportions of dual, black, Hispanic, or disabled patients did not have higher odds of penalization. CONCLUSION: Structural and patient characteristics of SNFs may significantly impact provider performance under the SNF VBP. These findings have implications for policy makers and clinical leaders seeking to improve quality and avoid unintended consequences with VBP in SNFs. J Am Geriatr Soc 68:826-834, 2020.
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