Objective To derive a comprehensive list of nursing‐sensitive patient outcomes (NSPOs) from published research on nurse staffing levels and from expert opinion. Data Sources/Study Setting Published literature reviews and their primary studies analyzing the link between nurse staffing levels and NSPOs and interviews with 16 experts on nursing care. Study Design Umbrella review and expert interviews. Data Collection/Extraction Methods We screened three electronic databases for literature reviews on the association between nurse staffing levels and NSPOs. After screening 430 potentially relevant records, we included 15 literature reviews, derived a list of 22 unique NSPOs from them, and ranked these in a systematic fashion according to the strength of evidence existing for their association with nurse staffing. We extended this list of NSPOs based on data from expert interviews. Principal Findings Of the 22 NSPOs discussed in the 15 included literature reviews, we rated the strength of evidence for four as high, for five as moderate, and for 13 outcomes as low. Four additional NSPOs that have not been considered in literature were identified through expert interviews. Conclusions We identified strong evidence for a significant association between nurse staffing levels and NSPOs. Our results may guide researchers in selecting NSPOs they might wish to prioritize in future studies. In particular, rarely studied NSPOs as well as NSPOs that were only identified through expert interviews but have not been considered in literature so far should be subject to further research.

ObjectiveTo examine the impact of nurse staffing on patient-perceived quality of nursing care. We differentiate nurse staffing levels and nursing skill mix as two facets of nurse staffing and use a multidimensional instrument for patient-perceived quality of nursing care. We investigate non-linear and interaction effects.SettingThe study setting was 3458 hospital units in 1017 hospitals in Germany.ParticipantsWe contacted 212 554 patients discharged from non-paediatric, non-intensive and non-psychiatric hospital units who stayed at least two nights in the hospital between January and October 2019. Of those, 30 174 responded, yielding a response rate of 14.2%. Our sample included only those patients. After excluding extreme values for our nurse staffing variables and removing observations with missing values, our final sample comprised 28 136 patients ranging from 18 to 97 years of age (average: 61.12 years) who had been discharged from 3458 distinct hospital units in 1017 hospitals.Primary and secondary outcome measuresPatient-perceived quality of nursing care (general nursing care, guidance provided by nurses, and patient loyalty to the hospital).ResultsFor all three dimensions of patient-perceived quality of nursing care, we found that they significantly decreased as (1) nurse staffing levels decreased (with decreasing marginal effects) and (2) the proportion of assistant nurses in a hospital unit increased. The association between nurse staffing levels and quality of nursing care was more pronounced among patients who were less clinically complex, were admitted to smaller hospitals or were admitted to medical units.ConclusionsOur results indicate that, in addition to nurse staffing levels, nursing skill mix is crucial for providing the best possible quality of nursing care from the patient perspective and both should be considered when designing policies such as minimum staffing regulations to improve the quality of nursing care in hospitals.

The goal of this study is to provide empirical evidence of the impact of nurse staffing levels on seven nursing-sensitive patient outcomes (NSPOs) at the hospital unit level. Combining a very large set of claims data from a German health insurer with mandatory quality reports published by every hospital in Germany, our data set comprises approximately 3.2 million hospital stays in more than 900 hospitals over a period of 5 years. Accounting for the grouping structure of our data (i.e., patients grouped in unit types), we estimate cross-sectional, two-level generalized linear mixed models (GLMMs) with inpatient cases at level 1 and units types (e.g., internal medicine, geriatrics) at level 2. Our regressions yield 32 significant results in the expected direction. We find that differentiating between unit types using a multilevel regression approach and including postdischarge NSPOs adds important insights to our understanding of the relationship between nurse staffing levels and NSPOs. Extending our main model by categorizing inpatient cases according to their clinical complexity, we are able to rule out hidden effects beyond the level of unit types.