gms | German Medical Science

Background and status of (inter)national research: Nursing-sensitive events (NSEs) are common and have been found to occur in hospitalized patient up to 75%. NSEs, such as fall-related harm, pressure ulcers, and healthcare-associated infections, are harmful and lead to suffering for patients. Additionally, they constitute an economic burden on hospitals for generating high medical costs through a prolonged length of stay and additional medical procedures. To reduce such NSEs and to ensure high-quality nursing care, appropriate nurse staffing levels are needed.

While the link between nurse staffing and NSEs have been described in many studies, appropriate nurse staffing levels are lacking. Existing studies describe constant staffing exposure at the unit or hospital level without assessing individual patient-level exposure to nurse staffing during the hospital stay. Only few studies have assessed nurse staffing and patient outcomes using single-center longitudinal design with a limited generalizability.

Research question and objective: The overall aim of the TAILR.DE (Nursing-sensiTive events and the Association with Individual patient Levels nuRse staffing in German hospitals) Study is to explore individual patient-level exposure to nurse staffing and its influence on NSEs in Germany. Specifically, TAILR.DE addresses the following aims:

1.

To determine the frequency, severity, preventability and types of NSEs;

2.

To describe individual patient-level nurse staffing;

3.

To describe the association between NSEs and nurse staffing;

4.

To determine thresholds of safe nurse staffing levels and test them against NSEs.

Method: TAILR.DE is a 3-year multicenter, longitudinal observational study with a participatory research design. Together with collaborators from Switzerland, Sweden, Iran, and Italy, TAILR.DE is part of the international TAILR consortium.

TAILR.DE will include four German hospitals, with four medical, surgical or mixed units each. To assess NSEs, standardized retrospective record reviews of patient´s admissions will be used. Shift-level nursing staffing incl. workload will be assessed every day and for every shift. Within a 16 weeks data collection period, for each unit, 60 patient records as well as 240 weekday shifts and 96 weekend shifts will be assessed.

For aim 1, the type, frequency, severity and preventability of NSEs will be analyzed using descriptive statistics. For aims 2, descriptive analysis of the number of patients and nurses and the patient-to-nurse ratio will be applied. Workload will be modelled with an observed-over expected (O/E) estimator. With the results for aim 1 and aim 2, regression analysis of the NSE data and O/E estimator of individual-level nurse staffing will be conducted (aim 3). For aim 4, for each hospital, different thresholds of safe nurse staffing levels will be explored based on the O/E model using discrete event simulation.