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Performance and Penalties in Year 1 of the Skilled Nursing Facility Value‐Based Purchasing Program
Author(s) -
Qi Andrew C.,
Luke Alina A.,
Crecelius Charles,
Joynt Maddox Karen E.
Publication year - 2020
Publication title -
journal of the american geriatrics society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.992
H-Index - 232
eISSN - 1532-5415
pISSN - 0002-8614
DOI - 10.1111/jgs.16299
Subject(s) - medicine , skilled nursing facility , odds ratio , value based purchasing , confidence interval , staffing , nursing homes , odds , medicaid , demography , nursing , purchasing , logistic regression , health care , operations management , economics , sociology , economic growth
BACKGROUND/OBJECTIVES Launched in October 2018, Medicareʼs 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