Diya, L.

Effective strategies for nurse retention in acute hospitals: A mixed method study (Article)

2011-12-26T00:00:00Z

Background: The realization of an organizational context that succeeds to retain nurses within their job is one of the most effective strategies of dealing with nursing shortages. Objectives: First, to examine the impact of nursing practice environments, nurse staffing and nurse education on nurse reported intention to leave the hospital. Second, to provide understanding of which best practices in the organization of nursing care are being implemented to provide sound practice environments and to retain nurses. Methods: 3186 bedside nurses of 272 randomly selected nursing units in 56 Belgian acute hospitals were surveyed. A GEE logistic regression analysis was used to estimate the impact of organization of nursing care on nurse reported intention to leave controlling for differences in region (Walloon, Flanders, and Brussels), hospital characteristics (technology level, teaching status, and size) and nurse characteristics (experience, gender, and age). For the second objective, in-depth semi-structured interviews with the chief nursing officers of the three high and three low performing hospitals on reported intention to leave were held. Results: 29.5% of Belgian nurses have an intention-to-leave the hospital. Patient-to-nurse staffing ratios and nurse work environments are significantly (p < 0.05) associated with intention-to-leave. Interviews with Chief Nurse Officers revealed that high performing hospitals showing low nurse retention were - in contrast to the low performing hospitals - characterized by a flat organization structure with a participative management style, structured education programs and career opportunities for nurses. Conclusion: This study, together with the international body of evidence, suggests that investing in improved nursing work environments is a key strategy to retain nurses.

The relationship between in-hospital mortality, readmission into the intensive care nursing unit and/or operating theatre and nurse staffing levels (Article)

2011-08-30T00:00:00Z

diya l., van den heede k., sermeus w. & lesaffre e. (2011)The relationship between in-hospital mortality, readmission into the intensive care nursing unit and/or operating theatre and nurse staffing levels. Journal of Advanced Nursing00(0), 000-000. doi: 10.1111/j.1365-2648.2011.05812.x Aim. The aim of this article was to assess the relationship between (1) in-hospital mortality and/or (2) unplanned readmission to intensive care units or operating theatre and nurse staffing variables. Background. Adverse events are used as surrogates for patient safety in nurse staffing and patient safety research. A single adverse event cannot adequately capture the multi-dimensional attributes of patient safety; hence, there is a need to consider composite measures. Unplanned readmission into the postoperative Intensive Care nursing unit and/or operating Theatre and in-hospital mortality can be viewed as measures that incorporate the effects of several adverse events. Methods. We conducted a Bayesian multilevel analysis on a subset of the 2003 Belgian Hospital Discharge and Nursing Minimum Data sets. The sample included 9054 patients who underwent coronary artery bypass surgery or heart valve procedures from 28 Belgian acute hospitals. Two proxies of patient safety were considered, namely postoperative in-hospital mortality in the first postoperative intensive care unit and unplanned readmission into the intensive care and/or operating theatre (including mortality beyond the first postoperative intensive care unit) after the first-operative intensive care nursing unit. Results. There is an association between in-hospital mortality and/or unplanned readmissions and nurse staffing levels, but the relationship is moderated by volume and severity of illness respectively. In addition, the relationship differs between the two endpoints. Conclusion. Higher nurse staffing levels on postoperative general nursing cardiac surgery units protected patients from unplanned readmission to intensive care units or operating theatre and in-hospital mortality. © 2011 The Authors. Journal of Advanced Nursing

The use of "lives saved" measures in nurse staffing and patient safety research: Statistical considerations (Article)

2011-03-01T00:00:00Z

Background: Lives saved predictions are used to quantify the impact of certain remedial measures in nurse staffing and patient safety research, giving an indication of the potential gain in patient safety. Data collected in nurse staffing and patient safety are often multilevel in structure, requiring statistical techniques to account for clustering in the data. Objective: The purpose of this study was to assess the impact of model specifications on lives saved estimates and inferences in a multilevel context. Methods: A simulation study was carried out to assess the impact of model assumptions on lives saved predictions. Scenarios considered were omitting an important covariate, taking different link functions, neglecting the correlations coming from the multilevel data structure, and neglecting a level in a multilevel model. Finally, using a cardiac surgery data set, predicted lives saved from the random intercept logistic model and the clustered discrete time logistic model were compared. Results: Omitting an important covariate, neglecting the association between patients within the same hospital, and the complexity of the model affect the prediction of lives saved estimates and the inferences thereafter. On the other hand, a change in the link function led to the same predicted lives saved estimates and standard deviations. Finally, the lives saved estimates from the two-level random intercept model were similar to those of the clustered discrete time logistic model, but the standard deviations differed greatly. Conclusions: The results stress the importance of verifying model assumptions. It is recommended that researchers use sensitivity analyses to investigate the stability of lives saved results using different statistical models or different data sets. Copyright

Factors that influence data quality in caries experience detection: A multilevel modeling approach (Article)

2010-11-01T00:00:00Z

Caries experience detection is prone to misclassification. For this reason, calibration exercises which aim at assessing and improving the scoring behavior of dental raters are organized. During a calibration exercise, a sample of children is examined by the benchmark scorer and the dental examiners. This produces a 2 × 2 contingency table with the true and possibly misclassified responses. The entries in this misclassification table allow to estimate the sensitivity and the specificity of the raters. However, in many dental studies, the uncertainty with which sensitivity and specificity are estimated is not expressed. Further, caries experience data have a hierarchical structure since the data are recorded for the surfaces nested in the teeth within the mouth. Therefore, it is important to report the uncertainty using confidence intervals and to take the clustering into account. Here we apply a Bayesian logistic multilevel model for estimating the sensitivity and specificity. The main goal of this research is to find the factors that influence the true scoring of caries experience accounting for the hierarchical structure in the data. In our analysis, we show that the dentition type and tooth or surface type affect the quality of caries experience detection. Copyright

Establishing the relationship between nurse staffing and hospital mortality using a clustered discrete-time logistic model (Article)

2010-03-01T00:00:00Z

Studies based on aggregated hospital outcome data have established that there is a relationship between nurse staffing and adverse events. However, this result could not be confirmed in Belgium where 96 per cent of the variability of nurse staffing levels over nursing units (belonging to different hospitals) is explained by within-hospital variability. To better appreciate the possible impact of nurse staffing levels on adverse events, we propose a multilevel approach reflecting the complex nature of the data. In particular we suggest a clustered discrete-time logistic model that captures the risks associated with a given unit in the patient's trajectory through the hospital. The model also allows for nurse staffing levels to affect the current and subsequent nursing unit (carry-over effect). In the model 'time' is represented by the sequential number of the nursing unit that the patient is passing through. The model incorporates hospital and nursing unit random effects to express that patients treated in the same hospital and taken care of by nurses of the same unit share a common environment. In this study we used Belgian national administrative databases for the year 2003 to assess the relationship between nurse staffing levels and nurse education variables with in-hospital mortality. The analysis was restricted to elective cardiac surgery patients. Lower nursing unit staffing levels in the general nursing units were associated with high in-hospital mortality in units past the traditional cardiac surgery nursing units. Copyright

Nurse staffing and patient outcomes in Belgian acute hospitals: Cross-sectional analysis of administrative data (Article)

2009-07-01T00:00:00Z

Background: Studies have linked nurse staffing levels (number and skill mix) to several nurse-sensitive patient outcomes. However, evidence from European countries has been limited. Objectives: This study examines the association between nurse staffing levels (i.e. acuity-adjusted Nursing Hours per Patient Day, the proportion of registered nurses with a Bachelor's degree) and 10 different patient outcomes potentially sensitive to nursing care. Design-setting-participants: Cross-sectional analyses of linked data from the Belgian Nursing Minimum Dataset (general acute care and intensive care nursing units: n = 1403) and Belgian Hospital Discharge Dataset (general, orthopedic and vascular surgery patients: n = 260,923) of the year 2003 from all acute hospitals (n = 115). Methods: Logistic regression analyses, estimated by using a Generalized Estimation Equation Model, were used to study the association between nurse staffing and patient outcomes. Results: The mean acuity-adjusted Nursing Hours per Patient Day in Belgian hospitals was 2.62 (S.D. = 0.29). The variability in patient outcome rates between hospitals is considerable. The inter-quartile ranges for the 10 patient outcomes go from 0.35 for Deep Venous Thrombosis to 3.77 for failure-to-rescue. No significant association was found between the acuity-adjusted Nursing Hours per Patient Day, proportion of registered nurses with a Bachelor's degree and the selected patient outcomes. Conclusion: The absence of associations between hospital-level nurse staffing measures and patient outcomes should not be inferred as implying that nurse staffing does not have an impact on patient outcomes in Belgian hospitals. To better understand the dynamics of the nurse staffing and patient outcomes relationship in acute hospitals, further analyses (i.e. nursing unit level analyses) of these and other outcomes are recommended, in addition to inclusion of other study variables, including data about nursing practice environments in hospitals.

The relationship between inpatient cardiac surgery mortality and nurse numbers and educational level: Analysis of administrative data (Article)

2009-06-01T00:00:00Z

Background: In most multicenter studies that examine the relationship between nurse staffing and patient safety, nurse-staffing levels are measured per hospital. This can obscure relationships between staffing and outcomes at the unit level and lead to invalid inferences. Objective: In the present study, we examined the association between nurse-staffing levels in nursing units that treat postoperative cardiac surgery patients and the in-hospital mortality of these patients. Design-setting-participants: We illustrated our approach by using administrative databases (Year 2003) representing all Belgian cardiac centers (n = 28), which included data from 58 intensive care and 75 general nursing units and 9054 patients. Methods: We used multilevel logistic regression models and controlled for differences in patient characteristics, nursing care intensity, and cardiac procedural volume. Results: Increased nurse staffing in postoperative general nursing units was significantly associated with decreased mortality. Nurse staffing in postoperative intensive care units was not significantly associated with in-hospital mortality possibly due to lack of variation in ICU staffing across hospitals. Conclusion: This study, together with the international body of evidence, suggests that nurse staffing is one of several variables influencing patient safety. These findings further suggest the need to study the impact of nurse-staffing levels on in-hospital mortality using nursing-unit-level specific data.

Benchmarking nurse staffing levels: The development of a nationwide feedback tool (Article)

2008-09-01T00:00:00Z

Title. Benchmarking nurse staffing levels: the development of a nationwide feedback tool. Aim. This paper is a report of a study to develop a methodology that corrects nurse staffing for nursing care intensity in a way that allows nationwide benchmarking of nurse staffing data. Background. Although nurse workload measurement systems are recognized to be informative in nurse staffing decisions, they are rarely used. When these systems are used, however, it is only possible to compare units within hospitals, because currently available instruments are not standardized for comparisons beyond hospital boundaries. The Belgian Nursing Minimum Dataset (B-NMDS) contains uniformly measured data about the intensity of nursing care and nurse staffing levels for all hospitals in Belgium. Method. We conducted a retrospective multilevel analysis of the B-NMDS for the year 2003. The sample included 690,258 inpatient days for 298,691 patients, recorded from 1637 acute care nursing units in 115 hospitals. We corrected the number of nursing staff by using different covariates available in the B-NMDS: intensity of nursing care, type of day (week vs. weekend), service type (general vs. intensive) and hospital type (academic vs. general). Findings. The multilevel approach allowed us to explain about 70% of the variability in the number of nursing staff per nursing unit using hospital type (P = 0·0053); intensity of nursing care (P < 0·0001) and service type (P < 0·0001) as the only covariates. Conclusion. The feedback tool we developed can inform nurse managers and policymakers about nursing intensity-adjusted nurse staffing levels according to different benchmarks. Our study demonstrates that investing in large nursing datasets is appropriate for the international nursing community.