Researchers are studying the relationship between nurse staffing levels and nursing-sensitive patient outcomes

2000 ◽  
Keyword(s):  
Patient Outcomes ◽  
Nurse Staffing ◽  
Staffing Levels ◽  
The Relationship

scholarly journals Hospital nurse staffing and staff–patient interactions: an observational study

2019 ◽  
Vol 28 (9) ◽  
pp. 706-713 ◽  
Author(s):  
Jackie Bridges ◽  
Peter Griffiths ◽  
Emily Oliver ◽  
Ruth M Pickering
Keyword(s):  
Nurse Staffing ◽  
Care Quality ◽  
Multilevel Regression ◽  
Negative Interaction ◽  
Skill Mix ◽  
Staffing Levels ◽  
Patient Interactions ◽  
Number Of Patients ◽  
The Relationship

BackgroundExisting evidence indicates that reducing nurse staffing and/or skill mix adversely affects care quality. Nursing shortages may lead managers to dilute nursing team skill mix, substituting assistant personnel for registered nurses (RNs). However, no previous studies have described the relationship between nurse staffing and staff–patient interactions.SettingSix wards at two English National Health Service hospitals.MethodsWe observed 238 hours of care (n=270 patients). Staff–patient interactions were rated using the Quality of Interactions Schedule. RN, healthcare assistant (HCA) and patient numbers were used to calculate patient-to-staff ratios. Multilevel regression models explored the association between staffing levels, skill mix and the chance of an interaction being rated as ‘negative’ quality, rate at which patients experienced interactions and total amount of time patients spent interacting with staff per observed hour.Results10% of the 3076 observed interactions were rated as negative. The odds of a negative interaction increased significantly as the number of patients per RN increased (p=0.035, OR of 2.82 for ≥8 patients/RN compared with >6 to <8 patients/RN). A similar pattern was observed for HCA staffing but the relationship was not significant (p=0.056). When RN staffing was low, the odds of a negative interaction increased with higher HCA staffing. Rate of interactions per patient hour, but not total amount of interaction time, was related to RN and HCA staffing levels.ConclusionLow RN staffing levels are associated with changes in quality and quantity of staff–patient interactions. When RN staffing is low, increases in assistant staff levels are not associated with improved quality of staff–patient interactions. Beneficial effects from adding assistant staff are likely to be dependent on having sufficient RNs to supervise, limiting the scope for substitution.

The Association of Registered Nurse Staffing Levels and Patient Outcomes

Medical Care ◽  
2007 ◽  
Vol 45 (12) ◽  
pp. 1195-1204 ◽  
Author(s):  
Robert L. Kane ◽  
Tatyana A. Shamliyan ◽  
Christine Mueller ◽  
Sue Duval ◽  
Timothy J. Wilt
Keyword(s):  
Patient Outcomes ◽  
Registered Nurse ◽  
Nurse Staffing ◽  
Staffing Levels

scholarly journals Rationing of Nursing Care and Its Relationship with Nurse Staffing and Patient Outcomes: The Mediation Effect Tested by Structural Equation Modeling

2019 ◽  
Vol 16 (10) ◽  
pp. 1672 ◽  
Author(s):  
Xiaowen Zhu ◽  
Jing Zheng ◽  
Ke Liu ◽  
Liming You
Keyword(s):  
Adverse Events ◽  
Patient Outcomes ◽  
Nursing Care ◽  
Structural Equation ◽  
Nurse Staffing ◽  
Mediation Effect ◽  
Equation Modeling ◽  
Patient Reported ◽  
Quality Assessments ◽  
The Relationship

Purpose: The purpose of this study is to test the mediation effect of rationing of nursing care (RONC) and the relationship this has between nurse staffing and patient outcomes. Methods: The analytic sample included 7802 nurse surveys and 5430 patient surveys. Three patient outcome indicators, nurse staffing, RONC, and confounding factors were considered in the model pathways. Results: The hypothesized model was shown to be statistically significant. In the model, nurses who were in the units with lower nurse-to-patient ratios reported higher scores on RONC, which meant that an increased level of withheld nursing care or a failure to carry out nursing duties was apparent. Nurses who reported a higher score on RONC, scored poorly on the quality assessment and were more frequently involved in patient adverse events. Nurse staffing influenced quality assessments and patient adverse events through RONC. In units with poorer nurse-reported quality assessments or more frequently patient adverse events, patient-reported dissatisfaction scores were higher. Conclusions: The results suggest that a lack of nurse staffing leads to RONC, which leads to poorer patient outcomes. These results are seen when considering the evaluations completed by both nurses and patients. The relationship between staffing numbers and patient outcomes explains the mediating role of RONC.

The relationship between nurse staffing and patient outcomes: A case study

2009 ◽  
Vol 46 (7) ◽  
pp. 986-992 ◽  
Author(s):  
Caroline Shuldham ◽  
Claire Parkin ◽  
Ashi Firouzi ◽  
Michael Roughton ◽  
Margaret Lau-Walker
Keyword(s):  
Patient Outcomes ◽  
Nurse Staffing ◽  
The Relationship

scholarly journals The effect of nurse-to-patient ratios on nurse-sensitive patient outcomes in acute specialist units: a systematic review and meta-analysis

2017 ◽  
Vol 17 (1) ◽  
pp. 6-22 ◽  
Author(s):  
Andrea Driscoll ◽  
Maria J Grant ◽  
Diane Carroll ◽  
Sally Dalton ◽  
Christi Deaton ◽  
...  
Keyword(s):  
Systematic Review ◽  
Patient Outcomes ◽  
Meta Analysis ◽  
Nurse Staffing ◽  
Administrative Databases ◽  
Inhospital Mortality ◽  
Staffing Level ◽  
Cross Sectional ◽  
Staffing Levels ◽  
Number Of Patients

Background: Nurses are pivotal in the provision of high quality care in acute hospitals. However, the optimal dosing of the number of nurses caring for patients remains elusive. In light of this, an updated review of the evidence on the effect of nurse staffing levels on patient outcomes is required. Aim: To undertake a systematic review and meta-analysis examining the association between nurse staffing levels and nurse-sensitive patient outcomes in acute specialist units. Methods: Nine electronic databases were searched for English articles published between 2006 and 2017. The primary outcomes were nurse-sensitive patient outcomes. Results: Of 3429 unique articles identified, 35 met the inclusion criteria. All were cross-sectional and the majority utilised large administrative databases. Higher staffing levels were associated with reduced mortality, medication errors, ulcers, restraint use, infections, pneumonia, higher aspirin use and a greater number of patients receiving percutaneous coronary intervention within 90 minutes. A meta-analysis involving 175,755 patients, from six studies, admitted to the intensive care unit and/or cardiac/cardiothoracic units showed that a higher nurse staffing level decreased the risk of inhospital mortality by 14% (0.86, 95% confidence interval 0.79–0.94). However, the meta-analysis also showed high heterogeneity (I2=86%). Conclusion: Nurse-to-patient ratios influence many patient outcomes, most markedly inhospital mortality. More studies need to be conducted on the association of nurse-to-patient ratios with nurse-sensitive patient outcomes to offset the paucity and weaknesses of research in this area. This would provide further evidence for recommendations of optimal nurse-to-patient ratios in acute specialist units.

scholarly journals The impact of nurse staffing levels on nursing-sensitive patient outcomes: a multilevel regression approach

2021 ◽  
Author(s):  
Karina Dietermann ◽  
Vera Winter ◽  
Udo Schneider ◽  
Jonas Schreyögg
Keyword(s):  
Patient Outcomes ◽  
Nurse Staffing ◽  
Large Set ◽  
Multilevel Regression ◽  
Cross Sectional ◽  
Data Set ◽  
Staffing Levels ◽  
Regression Approach ◽  
Clinical Complexity ◽  
The Impact

AbstractThe 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.

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