Abstract

Background: There is a growing body of evidence that nurse staffing (nurse to patient ratio, adjusted for patient acuity/need for nursing care) and characteristics of the nurse such as experience, education level, tenure working on specific unit, affects patient outcomes. Except for a few smaller, single site studies, these investigations have not matched patients with the nurses that directly cared for them during each shift. Previous studies have only been able to produce limited recommendations, such as suggested minimum staffing ratios or to increase the percentage of RNs with baccalaureate degrees. Virtually nothing is known about how individual nurse-patient assignments impact patient outcomes, thus precluding us from developing smart staffing approaches tailored to the needs of each patient. Significance: Match individual patients to the nurses who directly cared for them each shift to address the current gap in the understanding of how individual nurse-patient assignments affect patient outcomes. Controlling for other factors known to affect the relationship between nurse staffing and patient outcomes such as patient acuity and unit- and hospital-level characteristics. Innovation and Impact: This innovative project will be the first large-scale study of the effects of nurse staffing that links nurses to patients. It will provide the information needed for VA to effectively utilize the nurse staffing tools in the Clarvia component of the Cerner electronic medical record. Specific Aims: Aim 1: Examine the joint effects of the association of nurse staffing levels and individual nurse characteristics and outcomes for hospitalized Veterans. Examine independent and joint associations of individual nurses’ characteristics (e.g., education and experience) with the outcomes of the patients assigned to these nurses’ direct care (in-hospital mortality, failure to rescue, hospital-acquired infections, and risk-adjusted length of stay (LOS)). Aim 2. Test how the associations of nurse staffing and nurse characterstings with patient outcomes are modified by varying unit-shift circumstances. Nurses don’t care for patients in isolation; the nurses working each shift work as a team and often help each other; we will test how the effects of this teamwork vary by the staffing levels each shift for the unit as a whole, the characteristics of the staff each shift, and the unit work, measured by the patient acuity/need for nursing care and the patient throughput (admissions and discharges). Aim 3: We will work with the Office of Nursing Services to present the project findings to key stakeholders to facilate the translation of project findings into recommendations for nurse- patient assignments that promote high quality outcomes for hospitalized Veterans. Methodology: VA 2010-2023 nurse staffing data, including the characteristics of each nurse (e.g., experience) will be matched with the extensive VA clinical data available for each patient using the TIU Nursing Assessment extract that must be completed by the nurse caring for each patient, each shift. Multilevel regression models will be used to estimate the association between the characteristics of the nurses (e.g., education) who cared for each patient, the nurse staffing level for each patient and the outcomes for hospitalized Veterans. This will yield both precise estimates of the effects of nursing input and how this varies by the characteristics of each nurse e.g., the extent that more experienced nurses can safely manage larger workloads). Next Steps/Implementation: This project will be conducted in close collaboration with our VA Office of Nursing Services partners to maximize the potential benefits for the care for Veterans.

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DRA: Health Systems

DRE: TRL - Applied/Translational

Keywords: Practice Patterns/Trends

MeSH Terms: None at this time.