Facility characteristics and costs associated with meeting proposed minimum staffing levels in skilled nursing...

Federal minimum nurse staffing levels for skilled nursing facilities (SNFs) were proposed in 2019 U.S. Congressional bills. We estimated costs and personnel needed to meet the proposed staffing levels, and examined characteristics of SNFs not meeting these thresholds.

This was a cross-sectional analysis of 2019Q4 payroll data, the Hospital Wage Index, and other administrative data for 14,964 Medicare and Medicaid-certified SNFs. We examined characteristics of SNFs not meeting proposed minimum thresholds: 4.1 total nursing hours per resident day (HPRD); 0.75 registered nurse (RN) HPRD; 0.54 licensed practical nurse (LPN) HPRD; and 2.81 certified nursing assistant (CNA) HPRD. For SNFs falling below the thresholds, we calculated the additional HPRD needed, along with the associated full-time equivalent (FTE) personnel and salary costs.

In 2019, 25.0% of SNFs met the minimum 4.1 total nursing HPRD, while 31.0%, 84.5%, and 10.7% met the RN, LPN, and CNA thresholds, respectively. Only 5.0% met all four categories. In adjusted analyses, factors most strongly associated with SNFs not meeting the proposed minimums were: higher Medicaid census, larger bed size, for-profit ownership, higher county SNF competition; and, for RNs specifically, higher community poverty and lower Medicare census. Rural SNFs were less likely to meet all categories and this was explained primarily by county SNF competition. We estimate that achieving the proposed federal minimums across SNFs nationwide would require an estimated additional 35,804 RN, 3509 LPN, and 116,929 CNA FTEs at $7.25 billion annually in salary costs based on current wage rates and prepandemic resident census levels.

Achieving proposed minimum nurse staffing levels in SNFs will require substantial financial investment in the workforce and targeted support of low-resource facilities. Extensive recruitment and retention efforts are needed to overcome supply constraints, particularly in the aftermath of the COVID-19 pandemic.