This is the first of two linked papers exploring decision making in nursing which integrate research evidence from different clinical and academic disciplines. Currently there are many decision-making theories, each with their own distinctive concepts and terminology, and there is a tendency for separate disciplines to view their own decision-making processes as unique. Identifying good nursing decisions and where improvements can be made is therefore problematic, and this can undermine clinical and organizational effectiveness, as well as nurses' professional status. Within the unifying framework of psychological classification, the overall aim of the two papers is to clarify and compare terms, concepts and processes identified in a diversity of decision-making theories, and to demonstrate their underlying similarities. It is argued that the range of explanations used across disciplines can usefully be re-conceptualized as classification behaviour. This paper explores problems arising from multiple theories of decision making being applied to separate clinical disciplines. Attention is given to detrimental effects on nursing practice within the context of multidisciplinary health-care organizations and the changing role of nurses. The different theories are outlined and difficulties in applying them to nursing decisions highlighted. An alternative approach based on a general model of classification is then presented in detail to introduce its terminology and the unifying framework for interpreting all types of decisions. The classification model is used to provide the context for relating alternative philosophical approaches and to define decision-making activities common to all clinical domains. This may benefit nurses by improving multidisciplinary collaboration and weakening clinical elitism.

For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Laszlo MonostoriResearch Laboratory of Engineering and Management Intelligence, Hungarian Academy of Sciences, Budapest, Hungary Abstract Purpose -The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach -The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings -Aside from providing an extended overview of today's big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications -The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value -The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

Using novel methods, this paper explores sources of uncertainty and gender bias in primary care doctors' diagnostic decision-making about coronary heart disease (CHD). Claims about gendered consultation styles and quality of care are re-examined, along with the adequacy of CHD models for women. Randomly selected doctors in the UK and the US (n = 112, 56 per country, stratified by gender) were shown standardised videotaped vignettes of actors portraying patients with CHD. Patients' age, gender, ethnicity and social class were varied systematically. During interviews, doctors gave free-recall accounts of their decision-making, which were analysed to determine patient and doctor gender effects. We found differences in male and female doctors' responses to different types of patient information. Female doctors recall more patient cues overall, particularly about history presentation, and particularly amongst women. Male doctors appear less affected by patient gender but both male and especially female doctors take more account of male patients' age, and consider more age-related disease possibilities for men than women. Findings highlight the need for better integration of knowledge about female presentations within accepted CHD risk models, and do not support the contention that women receive better-quality care from female doctors.

This is the second of two linked papers exploring decision making in nursing. The first paper, 'Classifying clinical decision making: a unifying approach' investigated difficulties with applying a range of decision-making theories to nursing practice. This is due to the diversity of terminology and theoretical concepts used, which militate against nurses being able to compare the outcomes of decisions analysed within different frameworks. It is therefore problematic for nurses to assess how good their decisions are, and where improvements can be made. However, despite the range of nomenclature, it was argued that there are underlying similarities between all theories of decision processes and that these should be exposed through integration within a single explanatory framework. A proposed solution was to use a general model of psychological classification to clarify and compare terms, concepts and processes identified across the different theories. The unifying framework of classification was described and this paper operationalizes it to demonstrate how different approaches to clinical decision making can be re-interpreted as classification behaviour. Particular attention is focused on classification in nursing, and on re-evaluating heuristic reasoning, which has been particularly prone to theoretical and terminological confusion. Demonstrating similarities in how different disciplines make decisions should promote improved multidisciplinary collaboration and a weakening of clinical elitism, thereby enhancing organizational effectiveness in health care and nurses' professional status. This is particularly important as nurses' roles continue to expand to embrace elements of managerial, medical and therapeutic work. Analysing nurses' decisions as classification behaviour will also enhance clinical effectiveness, and assist in making nurses' expertise more visible. In addition, the classification framework explodes the myth that intuition, traditionally associated with nurses' decision making, is less rational and scientific than other approaches.

Thorough risk assessment helps in developing risk management plans that minimize risks that can impede mental health patients' recovery. Department of Health policy states that risk assessments and risk management plans should be inextricably linked. This paper examines their content and linkage within one Trust. Four inpatient wards for working age adults (18-65 years) in a large mental health Trust in England were included in the study. Completed risk assessment forms, for all patients in each inpatient ward were examined (n= 43), followed by an examination of notes for the same patients. Semi-structured interviews took place with ward nurses (n= 17). Findings show much variability in the amount and detail of risk information collected by nurses, which may be distributed in several places. Gaps in the risk assessment and risk management process are evident, and a disassociation between risk information and risk management plans is often present. Risk information should have a single location so that it can be easily found and updated. Overall, a more integrated approach to risk assessment and management is required, to help patients receive timely and appropriate interventions that can reduce risks such as suicide or harm to others.