Integrating Emerging Areas of Nursing Science into PhD Programs

The Council for the Advancement of Nursing Science aims to “facilitate and recognize life-long nursing science career development” as an important part of its mission. In light of fast-paced advances in science and technology that are inspiring new questions and methods of investigation in the health sciences, the Council for the Advancement of Nursing Science convened the Idea Festival for Nursing Science Education and appointed the Idea Festival Advisory Committee to stimulate dialogue about linking PhD education with a renewed vision for preparation of the next generation of nursing scientists. Building on the 2010 American Association of Colleges of Nursing Position Statement “The Research-Focused Doctoral Program in Nursing: Pathways to Excellence,” Idea Festival Advisory Committee members focused on emerging areas of science and technology that impact the ability of research-focused doctoral programs to prepare graduates for competitive and sustained programs of nursing research using scientific advances in emerging areas of science and technology. The purpose of this article is to describe the educational and scientific contexts for the Idea Festival, which will serve as the foundation for recommendations for incorporating emerging areas of science and technology into research-focused doctoral programs in nursing

Quality and Cost Analysis of Nurse Staffing, Discharge Preparation, and Postdischarge Utilization

Objectives. To determine the impact of unit-level nurse staffing on quality of discharge teaching, patient perception of discharge readiness, and postdischarge readmission and emergency department (ED) visits, and cost-benefit of adjustments to unit nurse staffing. Data Sources. Patient questionnaires, electronic medical records, and administrative data for 1,892 medical–surgical patients from 16 nursing units within four acute care hospitals between January and July 2008. Design. Nested panel data with hospital and unit-level fixed effects and patient and unit-level control variables. Data Collection/Extraction. Registered nurse (RN) staffing was recorded monthly in hours-per-patient-day. Patient questionnaires were completed before discharge. Thirty-day readmission and ED use with reimbursement data were obtained by cross-hospital electronic searches. Principal Findings. Higher RN nonovertime staffing decreased odds of readmission (OR=0.56); higher RN overtime staffing increased odds of ED visit (OR=1.70). RN nonovertime staffing reduced ED visits indirectly, via a sequential path through discharge teaching quality and discharge readiness. Cost analysis projected total savings from 1 SD increase in RN nonovertime staffing and decrease in RN overtime of U.S.$11.64 million and U.S.$544,000 annually for the 16 study units. Conclusions. Postdischarge utilization costs could potentially be reduced by investment in nursing care hours to better prepare patients before hospital discharge

Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.Comment: arXiv admin note: substantial text overlap with arXiv:1503.0625

A multilevel integrative approach to hospital case mix and capacity planning.

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.Health care; Case mix and capacity planning; Master surgery schedule; Multilevel; Resource efficiency; Service level;

Word Adjacency Graph Modeling: Separating Signal From Noise in Big Data

There is a need to develop methods to analyze Big Data to inform patient-centered interventions for better health outcomes. The purpose of this study was to develop and test a method to explore Big Data to describe salient health concerns of people with epilepsy. Specifically, we used Word Adjacency Graph modeling to explore a data set containing 1.9 billion anonymous text queries submitted to the ChaCha question and answer service to (a) detect clusters of epilepsy-related topics, and (b) visualize the range of epilepsy-related topics and their mutual proximity to uncover the breadth and depth of particular topics and groups of users. Applied to a large, complex data set, this method successfully identified clusters of epilepsy-related topics while allowing for separation of potentially non-relevant topics. The method can be used to identify patient-driven research questions from large social media data sets and results can inform the development of patient-centered interventions

Collective Bargaining and Technological Investment: The Case of Nurses’ Unions and the Transition from Paper-Based to Electronic Health Records

Does the presence of a unionized nursing workforce retard U.S. hospitals’ transition from paper-based to electronic health records (EHRs)? After tying archival data on hospitals’ structural features and health information technology (IT) investment patterns to self-gathered data on unionism, I find that hospitals that bargain collectively with their registered nurses (RNs) appear to delay or forego the transition away from paper, consistent with existing theory and research in industrial relations and institutional economics. However, this relationship is fully mediated by a hospital’s payer mix: those serving a larger share of less lucrative, elderly, disabled, and indigent patients are more likely to adopt EHRs if they are unionized than if they are not, a result that holds even at the median payer mix. Indeed, this accords with research on the interplay of labour and technology as the aforementioned dynamics are driven entirely by RN-exclusive bargaining units for whom the new IT serves as a complement rather than as a substitute in production. Given the outsized role that unions play in the U.S. healthcare sector, the overall sluggish performance of the sector, and the expectations that policymakers have for EHRs, evidence that these unions are welfare-enhancing should be welcome news

Identifying the determinants of chronic absenteeism: A bioecological systems approach

Background/Context: Chronic school absenteeism is a pervasive problem across the US; in early education, it is most rampant in kindergarten and its consequences are particularly detrimental, often leading to poorer academic, behavioral and developmental outcomes later in life. Though prior empirical research has identified a broad range of determinants of chronic absenteeism, there lacks a single, unified theoretically driven investigation examining how such factors concurrently explain the incidence of chronic absenteeism among our nation 's youngest schoolchildren. Thus, it is difficult to determine the relative importance of one factor over another, hence making it challenging to develop appropriate supports and services to reduce school absences. Purpose/Research Questions: Our study filled this critical void-we investigated multiple determinants of chronic absenteeism that were grounded, theoretically and empirically, in Bronfenbrenner's bioecological model of development. Specifically, using data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 and the method of hierarchical generalized linear modeling, we analyzed how the co-occurrence of key (a) process, (b) person, and (c) context (micro-, meso-, exo- and macrosystem) factors was associated with kindergarteners' probability of being chronically absent. Findings/Results: Children who have poorer health, higher internalizing behaviors, and more frequent engagement in learning activities at home had higher odds of chronic absenteeism. Also, children from larger families and of lower socioeconomic status faced increased odds of chronic absenteeism. Conversely, children holding positive attitudes towards school had lowered odds of chronic absenteeism, a finding that remained robust across socioeconomic status groups. Finally, parent-school connections were associated with lowered odds of absenteeism. Conclusions/Recommendations: Overall, our findings strongly suggested that addressing chronic absenteeism will require comprehensive and multifaceted approaches that recognize these multiple factors. With this theoretically grounded, more descriptive approach, it is more feasible to identify key factors and subsequently design policies and practices to prevent absence behavior

Collective Turnover at the Group, Unit, and Organizational Levels: Evidence, Issues, and Implications

Studies of the causes and consequences of turnover at the group, unit, or organizational level of analysis have proliferated in recent years. Indicative of its importance, turnover rate research spans numerous academic disciplines and their respective journals. This broad interest is fueled by the considerable implications of turnover rates predicting broader measures of organizational effectiveness (productivity, customer outcomes, firm performance) as well as by the related perspective that collective turnover is an important outcome in its own right. The goal of this review is to critically examine and extract meaningful insights from research on the causes and consequences of group, unit, and organizational turnover. The review is organized around five major “considerations,” including (1) measurement and levels of analysis issues, (2) consequences, (3) curvilinear and interaction effects, (4) methodological and conceptual issues, and (5) antecedents. The review concludes with broad directions for future research