The effect of variations in nurse staffing on patient length of stay in the acute care setting.
ABSTRACT This study examines the relationship between nurse staffing and patient length of stay (LOS). Data were collected on nurses employed and patients admitted to one of four study units located in two Midwest hospitals. Three nursing variables (hours per patient day [HPPD], skill mix, and nursing expertise) were collected through survey and administrative forms. The nursing data were then linked with patient-specific characteristics (deviation from expected LOS) to test the relationship at the patient level of analysis. Average HPPD was a positive predictor of deviation from expected LOS, whereas overall expertise was a negative predictor of deviation from expected LOS. Higher staffing levels may result in patients being discharged sooner than expected. Nurse administrators must consider the quantity as well as quality of staff when determining optimal staffing levels. Unit staffing levels must include nurses who have both experiential and theoretical knowledge in order to achieve optimal patient outcomes.
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ABSTRACT: Effective provisioning of healthcare services during patient hospitalization requires collaboration involving a set of interdependent complex tasks, which needs to be carried out in a synergistic manner. Improved patients' outcome during and after hospitalization has been attributed to how effective different health services provisioning groups carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among physicians on the effective outcome in delivering health services for improved patient outcomes. However, there are very few systematic empirical studies with a focus on the effect of collaboration networks among healthcare professionals and patients' medical condition. On the basis of the fact that collaboration evolves among physicians when they visit a common hospitalized patient, in this study, we first propose an approach to map collaboration network among physicians from their visiting information to patients. We termed this network as physician collaboration network (PCN). Then, we use exponential random graph (ERG) models to explore the microlevel network structures of PCNs and their impact on hospitalization cost and hospital readmission rate. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks such as PCN. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the electronic health insurance claims dataset of a very large Australian health insurance organization, we construct and model PCNs. We notice that the 2-star (subset of 3 nodes in which 1 node is connected to each of the other 2 nodes) parameter of ERG has significant impact on hospitalization cost. Further, we identify that triangle (subset of 3 nodes in which each node is connected to the rest 2 nodes), alternative k-star (subset of k nodes in which 1 node is connected to each of other k - 1 nodes), and alternative k - 2 path (subset of k nodes in which, between a specific pair of nodes, there exists k - 2 paths of length 2) parameters of ERG have impact on the hospital readmission rate. Our findings can have implications for healthcare administrators or managers who could potentially improve the practice cultures in their organizations by following these outcomes. Copyright © 2013 John Wiley & Sons, Ltd.Statistics in Medicine 03/2013; · 2.04 Impact Factor
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ABSTRACT: To investigate the current literature to gain an understanding of skill mix, why it is being manipulated and how it affects patient care and health-care costs. Due to workforce shortages, economic constraints and increasing patient acuity, employers are looking at methods of providing patient care whilst maintaining costs. Registered nurses make up a large percentage of the health-care budget. The manipulation of skill mix (i.e. the percentage of registered nurses available for patient care) is seen as one method of managing the increasing cost whilst still ensuring patient care. Research literature was used to determine the current use of skill mix and its impact on patient care and health-care costs. The use of a higher proportion of registered nurses is associated with better health outcomes, shorter length of stay and reduced patient morbidity. Economic savings from substituting registered nurses with other health professionals may be offset by increased patient length of stay in hospital and increased patient mortality. When evaluating nursing skill mix, a higher percentage of registered nurses may result in health-care facility cost savings by providing a shorter length of stay and decreased patient complications.Journal of Nursing Management 09/2013; · 1.45 Impact Factor
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ABSTRACT: Physician collaboration, which evolves among physicians during the course of providing healthcare services to hospitalised patients, has been seen crucial to effective patient outcomes in healthcare organisations and hospitals. This study aims to explore physician collaborations using measures of social network analysis (SNA) and exponential random graph (ERG) model. Based on the underlying assumption that collaborations evolve among physicians when they visit a common hospitalised patient, this study first proposes an approach to map collaboration network among physicians from the details of their visits to patients. This paper terms this network as physician collaboration network (PCN). Second, SNA measures of degree centralisation, betweenness centralisation and density are used to examine the impact of SNA measures on hospitalisation cost and readmission rate. As a control variable, the impact of patient age on the relation between network measures (i.e. degree centralisation, betweenness centralisation and density) and hospital outcome variables (i.e. hospitalisation cost and readmission rate) are also explored. Finally, ERG models are developed to identify micro-level structural properties of (i) high-cost versus low-cost PCN; and (ii) high-readmission rate versus low-readmission rate PCN. An electronic health insurance claim dataset of a very large Australian health insurance organisation is utilised to construct and explore PCN in this study. It is revealed that the density of PCN is positively correlated with hospitalisation cost and readmission rate. In contrast, betweenness centralisation is found negatively correlated with hospitalisation cost and readmission rate. Degree centralisation shows a negative correlation with readmission rate, but does not show any correlation with hospitalisation cost. Patient age does not have any impact for the relation of SNA measures with hospitalisation cost and hospital readmission rate. The 2-star parameter of ERG model has significant impact on hospitalisation cost. Furthermore, it is found that alternative-k-star and alternative-k-two-path parameters of ERG model have impact on readmission rate. Collaboration structures among physicians affect hospitalisation cost and hospital readmission rate. The implications of the findings of this study in terms of their potentiality in developing guidelines to improve the performance of collaborative environments among healthcare professionals within healthcare organisations are discussed in this paper.BMC Health Services Research 06/2013; 13(1):234. · 1.77 Impact Factor