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The purpose of this paper is to present a social network approach for identification of micro-organizational re-design interventions to make more efficient and fluid the knowledge flow in a rehabilitation multidisciplinary team. The structural information of different kinds of knowledge networks within a team is augmented with additional analyses aimed at collecting information about the ways through which participants use knowledge, the motivation behind knowledge exchange, and the non-human knowledge sources used by subjects to perform their work. This paperwork was supported by CNCSIS – UEFISCDI, project number PNII – IDEI 810/2008.

The authors propose a definition of knowledge network including human and non-human knowledge source (documents and knowledge repositories) as it is more adequate for the analysis of knowledge flows in multidisciplary medical teams. The mapping and analysis of the network are carried out through: elicitation of knowledge flows between people within and outside the team through a structured questionnaire; mapping of the knowledge flows toward non-human knowledge sources; and identification of critical aspects and proposal of re-engineering interventions to make knowledge flow more efficient and effective.

The analysis of the critical aspects emerged in the field study identifies a number of opportunities to improve the efficiency and effectiveness of knowledge sharing through the re-design of the team network. The re-design interventions concern three main features of knowledge network: “knowledge centralization,” “Over-reliance on External experts,” “Unshared knowledge tools and sources.”

The originality of the work resides in applying social network analysis (SNA) for healthcare management settings, proving evidence and guidelines to show how healthcare organizations can benefit from the adoption of SNA-based approaches.

The purpose of this paper is to discuss how tacit and explicit knowledge determine grey knowledge and how these are stimulated through interactions within networks, forming the grey hybrid intelligent systems (HISs). The feedback processes and mechanisms between internal and external knowledge determine the apparition of grey knowledge into an intelligent system (IS). The extension of ISs is determined by the ubiquity of the internet but, in our framework, the grey knowledge flows assure the viability and effectiveness of these systems.

Some characteristics of the Hybrid Intelligent Knowledge Systems are put forward along with a series of models of hybrid computational intelligence architectures. More, relevant examples from the literature related to the hybrid systems architectures are presented, underlying their main advantages and disadvantages.

Due to the lack of a common framework it remains often difficult to compare the various HISs conceptually and evaluate their performance comparatively. Different applications in different areas are needed for establishing the best combinations between models that are designed using grey, fuzzy, neural network, genetic, evolutionist and other methods. But all these systems are knowledge dependent, the main flow that is used in all parts of every kind of system being the knowledge. Grey knowledge is an important part of the real systems and the study of its proprieties using the methods and techniques of grey system theory remains an important direction of the researches.

The paper discusses the differences among the three types of knowledge and how they and the grey systems theory can be used in different hybrid architectures.

The present paper tries to give a new vision on the firm's future evolution forecasting. By taking into account some of the current values of its symptoms and applying one of the most used models in the grey systems theory, namely the GM(1,1), the predictions related to its future symptoms' values can be determined. Having these projected values and the grey economic-financial matrix, K, the future diseases that can hit a company can be depicted along with their causes. The paper aims to discuss these issues.

Forecasting the future development of a firm is always an important issue in firm's survival in nowadays economy. Most of all, it is extremely important to be aware all the time about the inner and outer factors than can make a difference between a successful and a bankrupt firm. For this, here the authors have used three GM(1,1) models for forecasting the future symptoms (expressed through financial indicators) and performance indicator of a firm. Each time, based on the determined accuracy rate, a specific GM model has been chosen for every indicator's forecasting.

Considering some previous researches and findings in bankruptcy modelling and diagnosis, this paper enlarges their applicability by adding the possibility to make future predictions on the indicators' evolution and to observe and to better manage their causes. As it was expected, the GM(1,1) models used for the forecasting of the various time series variables taken into account were different from one case to another, choosing the best-specific model for each variable case conducted to more accurate data-fit, with direct results in the causes hierarchy.

By knowing the main causes that determine a certain state in firms' development and understanding them, the manager can action upon them in a manner that can make the difference between a bankrupt and a real successful firm.

The paper succeeds in enlarging the view regarding bankruptcy forecasting by adding a dynamic view over the considered variables. If, in most of the cases when facing with financial forecasting, a single model is used for predictions, here the best GM model has been chosen for each variable based on the obtained accuracy rate. The results are concluding.

By making an analogy with the human body, the purpose of this paper is to underline the relations between a firm's failure causes and its current situation, in terms of the syndrome that can be identified at its level.

For this reason, some elements taken from medicine, such as symptoms, causes and syndromes are presented and characterized from the point of view of the firm.

It is shown that knowing the type of syndrome and the relation between it and the causes that determined its occurrence, a firm's situation can be improved and its life can be lengthened. The study was conducted on 19 firms and the results are conclusive.

By knowing the main causes that determine a certain type of bankruptcy syndrome, the firm's management can properly intervene to establish a new order and a new equilibrium, which can “save” the firm from an imminent collapse, bringing it to a normality situation and even leading it, in future, to increased performance.

The paper succeeds in shaping the relation between a firm's situation, its symptoms, the bankruptcy syndrome and the causes that led to a certain situation. Using one of the newest developed theories – the grey systems theory – and one of the newest concept used in firms' diagnosis – the bankruptcy syndrome – the paper extends the characteristics of this term and uses it in determining the causes that generate anomalies at firm level.

Performance indicates how well the activities, operations and business processes of an enterprise are performed. In health care, performance illustrates the quality of medical services and targets the strategic objectives, the efficiency and effectiveness and the obtaining of the desired financial results. In economic analysis, the key performance indicators (KPIs) offers real, relevant and accurate information regarding the performance, using analysis and diagnosis techniques. The purpose of this paper is to discuss grey systems theory in order to bridge the KPIs and the hospital’s performance.

Using the grey systems theory, the impact of seven selected KPIs (the beds utilization rate, the average length of hospitalization, the average cost of hospitalization/day, the proportion of physicians in total staff, the nosocomial infection rate, the death rate and the diagnostic concordance) on the hospital’s turnover is determined.

By analyzing the grey incidence between turnover and several KPIs, for a period of six years, it has been concluded that the greatest impact on performance has the diagnostic concordance and the percent of the physicians in the total staff, followed by the nosocomial infection rate, the average bed utilization rate and the death rate.

The considered case study should be extended by including a greater number of hospitals into the analysis.

The usage of the grey systems theory in an uncertain and limited information field such as the medical one, is a premier in this field. According to the grey incidence analysis results, the performance of health care institutions depends mainly on the quality of medical staff (that influence the diagnostic concordance, the nosocomial infection rate and the death rate) and on the management ability to attract competent and well-trained physicians, which can conduct in a new approach that should be considered by the hospitals’ managerial staff.

The purpose of this paper is to discuss the interval forecasting, prediction interval and its reliability. When the predicted interval and its reliability are construction, the general rule which must satisfy is studied, grey wrapping band forecasting method is perfect.

A forecasting method puts forward a process of prediction interval. It also elaborates on the meaning of interval (the probability of the prediction interval including the real value of predicted variable). The general rule is abstracted and summarized by many forecasting cases. The general rule is discussed by axiomatic method.

The prediction interval is categorized into three types. Three axioms that construction predicted interval must satisfy are put forward. Grey wrapping band forecasting method is improved based on the proposed axioms.

Take the Shanghai composite index as the example, according to the K-line diagram from 4 January 2013 to 9 May 2013, the reliability of predicted rebound height of subsequent two or three trading day does not exceed the upper wrapping curve is 80 per cent. It is significant to understand the forecasting range correctly, build a reasonable range forecasting method and to apply grey wrapping band forecasting method correctly.

Grey wrapping band forecasting method is improved based on the proposed axioms.