Year: 2025
Type: article
Abstract: BACKGROUND: Nursing homes (NHs) are required to provide sufficient nursing staff to meet the needs of their residents. This study provides a guide for NHs to align nurse staffing levels with resident needs and calculates the gap between reported and expected NH staffing. METHODS: Using the best available research data and recent federal minimum staffing requirements, expected nurse staffing levels were estimated for the 25 resident case-mix index (CMI) groups established by the Centers for Medicare & Medicaid Services (CMS) using nonlinear least-squares regression analyses. We compared the reported to expected staffing for all US NHs (14,420) in 2024. RESULTS: Expected registered RN hours per resident day (HRPD) ranged from 0.55 to 2.39 for the lowest and highest CMIs. Certified nursing assistants HPRD ranged from 2.45 to 3.6 HPRD, and total nursing ranged from 3.48 to 7.68 HPRD. In 2024, US NHs reported that the average facility CMI was 1.35. Reported RN HPRD was 32% below expected staffing, CNA HPRD was 30% below, and total nursing HPRD was 22% below expected staffing. NHs in the three highest CMI groups were more likely to meet expected RN or total HPRD than NHs in lower CMI groups. CONCLUSIONS: Most NHs need to increase staffing to meet resident acuity needs.
DOI: 10.1111/jgs.19501
Authors: Charlene A. Harrington, Robert A. McLaughlin, Debra Saliba, Elizabeth Halifax, Richard J. Mollot, Patrick S. Romano, Daniel J. Tancredi, Dana B. Mukamel
Institutions: University of California, San Francisco, RAND Corporation, University of California, Los Angeles, Greater Los Angeles Geriatric Research Education and Clinical Center, Long Term Care Community Coalition, University of California Davis Medical Center, UC Davis Health, University of California, Davis, University of California, Irvine
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,760
Funder: University of California, Davis
OpenAlex ID: W4410035646