Determining Nursing Home Staffing Based on Resident Acuity: A New Evidence-Based Methodology

Guest Editorial freeDetermining Nursing Home Staffing Based on Resident Acuity: A New Evidence-Based Methodology Margaret I. Wallhagen, PhD, GNP-BC, FAAN, AGSF, FGSA, , PhD, GNP-BC, FAAN, AGSF, FGSA Christine Mueller, PhD, RN, FGSA, FAAN, , PhD, RN, FGSA, FAAN Margaret I. Wallhagen, PhD, GNP-BC, FAAN, AGSF, FGSA E-mail Address: [email protected] and Christine Mueller, PhD, RN, FGSA, FAAN Journal of Gerontological Nursing, 2025;51(10):4–6Cite this articlePublished Online:October 01, 2025https://doi.org/10.3928/00989134-20250905-01 PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookxLinkedInRedditEmailBluesky SectionsMoreIntroductionNurses have long known that to provide safe, quality care in nursing homes, staffing must be based on the clinical and functional needs of residents, often referred to as acuity. Although nurse staffing standards for nursing homes have been proposed, they do not account for variation in the acuity of residents. The Centers for Medicare & Medicaid Services (CMS; 2024) recently recommended nursing home staffing standards for RNs, certified nursing assistants (CNAs), and total nurse hours per resident day (HPRD), but included the caveat that, "if the acuity needs of residents in a facility require a higher level of care, as the acuity needs in many facilities will, a higher total, RN, and NA [nursing assistant] staffing level will likely be required" (p. 40877).There are strategies available to determine acuity needs. Nursing facilities participating in Medicare and Medicaid programs are required by the CMS to conduct an annual facility-wide assessment to determine the resources needed to care for their residents. One component of that assessment is related to nurse staffing. Staffing is to be based on the following: The care required by the resident population, using evidence-based, data-driven methods that consider the types of diseases, conditions, physical and behavioral health needs, cognitive disabilities, overall acuity, and other pertinent facts that are present within that population, consistent with and informed by individual resident assessments.(CMS, 2025, p. 717)Until recently, there was little guidance and few resources available to nursing facilities to use evidence-based, data-driven methods to determine how to translate the overall acuity of residents into determining the amount and type of nursing staff needed to meet the needs of residents. Such guidance is now available. A ground-breaking study by Harrington et al. (2025) developed an evidence-based methodology that takes into account the acuity needs of residents and the nursing staff time associated with residents' acuity.Specifically, the methodology proposed by Harrington et al. (2025) uses the CMS Patient-Driven Payment Model (PDPM) system (Acumen, 2018). The PDPM focuses on the unique needs, characteristics, and goals of each resident and categorizes residents into one of 25 case-mix groups. The case-mix group for each resident is determined by data from each resident's Minimum Data Set (MDS) assessment. The MDS provides a standardized assessment of residents' functional abilities and health status and has evolved across time to more effectively capture care needs (Saliba & Buchanan, 2008). Thus, each case-mix group represents the clinical needs and resource requirements for residents and is associated with nursing staff time (i.e., RN staff time, CNA staff time, and total nursing staff time). The case-mix groups have an individually calculated case-mix index (CMI), which is the weighted index assigned to each of the 25 PDPM case-mix groups.Harrington et al. (2025) applied two sets of staffing data to each of the 25 CMIs. For RN and licensed practical/vocational nurse (LP/VN) staff times, they used data from the CMS Staff Time Measurement study (Fries et al., 1994). For CNA staff time, they used data from a study that simulated CNA staff time to prevent negative outcomes for residents (Schnelle et al., 2016). In addition, they aligned the lowest staff time with the expected federal minimum nurse staffing recommendations that were established by the CMS: 0.55 RN HPRD; 2.45 CNA HPRD; and 3.48 total nursing HPRD (CMS, 2024). LP/VN HPRD can be calculated by subtracting RN and CNA HPRD from total nursing HPRD. The full explanation of the methodology used to associate the staff time data to the 25 CMIs can be reviewed in the study by Harrington et al. (2025).Table 1 provides the results of the methodology used by Harrington et al. (2025) to determine the expected RN, CNA, and total nursing HPRD for each of the 25 PDPM groups and associated CMIs. Each nursing home has an average CMI based on the aggregated PDPM groups associated with each resident. To illustrate how to apply PDPM nurse staffing data, a 50-bed nursing facility with an average CMI of 1.53 will be used. The nurse staffing time associated with a CMI of 1.53 is as follows: RN HPRD: 1.09; CNA HPRD: 3.3; total nursing HPRD: 5.18. Each of these HPRD would be multiplied by 50 residents to determine the total staffing needed each day: Total RN HPRD: 54.5; Total CNA HPRD: 165; Total nursing HPRD: 259. The total RN and CNA HPRD is 219.5, which would be subtracted from the total nursing HPRD, leaving 39.5 HPRD that can be applied to RNs, CNAs, or LP/VNs. The acuity-based nursing HPRD would be distributed across shifts. For example, 54.5 RN HPRD represents 6.8 RNs for one 24-hour day; 165 CNA HPRD represents 20.6 CNAs for one 24-hour day; and the remaining 39.5 nursing HPRD represents five RNs, CNAs, or LP/VNs for one 24-hour day.Table 1 Expected RN, CNA, and Total Nurse Staffing Hours Per Resident Day (HPRD) for Each Nursing Group and Case-Mix Index (CMI)PDPM GroupPDPM Nursing HIPPS CharacterCMIExpected RN HPRDExpected CNA HPRDExpected Total Nursing HPRDExtensive servicesES3A3.842.393.67.68ES2B2.901.863.516.76ES1C2.771.793.56.63Special high careHDE2D2.271.513.436.08HDE1E1.881.293.375.63HBC2F2.121.423.415.91HBC1G1.761.223.355.48Special low careLDE2H1.971.343.395.74LDE1I1.641.153.335.33LBC2J1.631.143.325.31LBC3K1.350.983.264.93Clinically complexCDE2L1.771.233.355.49CDE1M1.531.093.35.18CBC2N1.471.053.295.10CA2O1.030.803.164.44CBC1P1.270.943.244.82CA1Q0.890.713.094.19Behavioral symptomsBAB2R0.980.773.144.36BAB1S0.940.743.124.29Reduced physical functionPDE2T1.481.063.295.11PDE1U1.391.013.274.99PBC2V1.150.873.24.64PA2W0.670.582.883.69PBC1X1.070.823.174.51PA1Y0.620.552.453.48Note. CNA = certified nursing assistant; PDPM = patient-driven payment model; HIPPS = Health Insurance Prospective Payment System.Reprinted from Harrington et al. (2025) (article is open access [ https://creativecommons.org/licenses/by-nc-nd/4.0/]; therefore, permission to reprint is not needed).The model proposed by Harrington et al. (2025) can act as a true facilitator of better nursing home care. Its data, and the translation of these data into the exemplars in this guest editorial, underpin the ability of nurse leaders in nursing facilities to more accurately calculate the staffing needs of residents, which provides some exciting opportunities. First, they support staffing that can lead to improvements in the quality of care. Second, they may provide additional data to argue for better standards for nursing homes. Third, they support the leadership role that nurses provide in nursing homes (Harris et al., 2022; National Academies of Sciences, Engineering and Medicine, 2022). All nurse leaders in nursing homes are encouraged to use and apply this evidence-based, data-driven methodology to determine acuity-based staffing for their facility. Using this process will not only assist in meeting the facility assessment regulatory requirements but will also help ensure that residents receive the amount and type of nursing care that their clinical and functional needs require.Margaret I. Wallhagen, PhD, GNP-BC, FAAN,AGSF, FGSAUniversity of California, San FranciscoSan Francisco, California meg.wallhagen@ucsf.eduChristine Mueller, PhD, RN, FGSA, FAANUniversity of MinnesotaMinneapolis, MinnesotaAcumen. (2018).Skilled nursing facilities patient-driven payment model technical report. https://www.cms.gov/medicare/medicare-fee-for-service-payment/snfpps/downloads/pdpm_technical_report_508.pdf > Google ScholarCenters for Medicare & Medicaid Services. (2024). 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