Factors to Consider in Decisions About Staffing in Rehabilitation Nursing Settings
Staffing decisions in the rehabilitation setting involve a process of determining patient care needs and providing staff with the education and skill levels that offers an effective number of nursing hours per patient day to deliver care (ARN, 2003.) Rehabilitation nursing settings differ in patient population, number of staff available, talent, experience and skill set of staff, administrative/budgetary issues, admission criteria, levels of care, and workload (Nelson, et al, 2007.)
Review and accrediting agencies expect staffing to be within an assessed level which is based on patients’ diagnoses and patient census ((ANA, 2020.) Rehabilitation nurse managers must be creative in meeting the needs of the nursing service areas by scheduling the available staff to correspond with the days of the week and shifts to the needs of the patient (assessment, education, care, treatment, and evaluation of progress) and organization (Gender, 2008). Maximum flexibility is desirable in terms of assignment of specialized rehabilitation nursing professionals to meet patient care needs effectively and in a fiscally sound manner.
Value-based care has added new challenges. The Quadruple Aim, (Arnetz et al., 2020) requires staffing levels which allows nurses to reach success in meeting patient care needs. This requires strategic planning with the patient, resources, and the care giver in mind.
Current Status:
Research demonstrates good outcomes result from the quality of staff more than quantity (Lasatar, Aiken, Sloane, 2021) Rehabilitation must use big data sets that includes Functional Independence Measures(FIM) that are rehab specific, and quality measures such as those collected by the National Database of Nursing Quality Indicators (NDNQI®) tool. Patient satisfaction data gleaned from resources such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAPS) and Press Ganey scores. Other tools such as The Logical Observation Identifiers Names and Codes (LOINC) developed from the normalizing of data including the Nursing Management Minimum Data (NMMDS) is a profession-specific data set adjusted for care setting which can be utilized by a rehabilitation organization and organizations providing rehabilitation services to determine how staffing influences both patient experience and outcomes. Most rehabilitation programs already collect the data elements used in the NMMDS (Pruinelli, 2016.)
Variables to be considered when planning and assigning staff include:
- Total level of patient acuity and diagnoses
- Availability of specialized rehabilitation nursing professionals
- Accessibility of support systems
- Availability of substitute/float staff that are appropriately oriented and cross-trained
- Number of proposed admissions
- Number of proposed discharges
- Number of proposed transfers
- Cultural diversity of patients
- Cultural diversity of staff
- Available technology
- Availability of resources for evidence-based practice
- Architecture and geography of the environment
Recommendations:
The Association of Rehabilitation Nurses supports the development of nurse staffing ratios specific to rehabilitation nursing based on available research. Rehabilitation nurse managers should:
Use data to develop an organization-specific, outcomes-driven staffing model based on its nursing value that balances workload, outcomes, and staffing satisfaction.
Support nursing in its ongoing quality improvement endeavors that support staffing as it relates to positive patient outcomes, functional improvement, patient satisfaction, nurse satisfaction, and prevention of patient readmissions.
Provide for opportunities to develop staff abilities, encourage specialty recognition such as the Certified Rehabilitation Registered Nurse credentialing, pay for advanced degrees and establish a clinical ladder that recognizes nurses who prepare to meet the ongoing challenges in caring for patients across the continuum.
Provide input into the selection of a written nurse staffing plan and validation of its applicability to their individual setting.
Updated by the Continuing Education Provider Unit in 2024.
References:
Arnetz et al. (2020). Enhancing healthcare efficiency to achieve the quadruple aim: An exploratory study. BMC research notes, 13:362.
doi.org/10.1186/s13104-020-05199-8
American Nurses Association (ANA). (2020). Principles for nurse staffing (3rd ed.). Silver Spring, MD: Author. https://cdn2.hubspot.net/hubfs/4850206/PNS3E_ePDF.pdf
Association of Rehabilitation Nurses (ARN). (2003). Role Description: Rehabilitation nurse manager. [Brochure]. Retrieved 7/28/2021 from https://rehabnurse.org/about/roles/rehab-nurse-manager
Unable to find updated reference - Gender, A.R. (2008). Administration and Leadership. In S. P. Hoeman (Ed.), Rehabilitation nursing: Process and application (4th ed.124-145). St. Louis: Mosby.
Lasatar, KB, Aiken, LH, Sloane, D. et al. (2021). Patient outcomes and cost savings associated with hospital safe nurse staffing legislation: An open observational study. BMJ open research, 11:e052899. doi:10.1136/ bmjopen-2021-052899
Commission on Accreditation of Rehabilitation Facilities. (2021). Standards manual and interpretive guidelines for medical rehabilitation. Tucson: CARF.
Nelson, A., Powell-Cope, G., Palacios, P., Luther, S.L., Black, T., Hillman, T., Christiansen, B., Nathenson, P. and Gross, J.C. (2007), Nurse Staffing and Patient Outcomes in Inpatient Rehabilitation Settings. Rehabilitation Nursing, 32: 179-202. https://doi.org/10.1002/j.2048-7940.2007.tb00173.x
Pruinelli, L., Garcia, A., Delaney, C., Caspers, B., & Westra, B. (2016). Nursing management minimum data set: Cost-effective tool to demonstrate the value of nurse staffing in the big data science era. Nursing Economic$, 34(2), 66–89.
