Abstract
Health care systems around the world are under pressure, the costs are high and rising, and the population is growing and ageing. Health information technology is expected to help improving the health care processes capacity. The aim of this work is to analyze the benefits of the Theory of Constraints (TOC) buffer management implementation in the health care environment concerning the improvement in the patient flow and its management. A literature review was conducted, with an automated search on four databases to identify relevant published articles, written in English language between 2000 and 2010, about the TOC buffer management applied to the health care patient flow. Only three relevant articles were included. The analysis was based on the measurements of the implementations realized in seven different hospitals and for three different purposes: Accident & Emergency department (A&E), admissions and discharge. A statistical analysis conducted in the A&E and admissions post-implementation results demonstrated a significant improvement achieved. Four management control functions improvements were also obtained: prioritize, expedite, escalate and improve. Although few papers were available, TOC buffer management appears to be a good solution to improve performance and management in health care.

Abstract
Given the large amount of data mining algorithms, their combinations (e.g. ensembles) and possible parameter settings, finding the most adequate method to analyze a new dataset becomes an ever more challenging task. This is because in many cases testing all possibly useful alternatives quickly becomes prohibitively expensive. In this paper we propose a novel technique, called active testing, that intelligently selects the most useful cross-validation tests. It proceeds in a tournament-style fashion, in each round selecting and testing the algorithm that is most likely to outperform the best algorithm of the previous round on the new dataset. This 'most promising' competitor is chosen based on a history of prior duels between both algorithms on similar datasets. Each new cross-validation test will contribute information to a better estimate of dataset similarity, and thus better predict which algorithms are most promising on the new dataset. We also follow a different path to estimate dataset similarity based on data characteristics. We have evaluated this approach using a set of 292 algorithm-parameter combinations on 76 UCI datasets for classification. The results show that active testing will quickly yield an algorithm whose performance is very close to the optimum, after relatively few tests. It also provides a better solution than previously proposed methods. The variants of our method that rely on crossvalidation tests to estimate dataset similarity provides better solutions than those that rely on data characteristics.

Abstract
Background: The study of length of stay (LOS) outliers is important for the management and financing of hospitals. Our aim was to study variables associated with high LOS outliers and their evolution over time. Methods: We used hospital administrative data from inpatient episodes in public acute care hospitals in the Portuguese National Health Service (NHS), with discharges between years 2000 and 2009, together with some hospital characteristics. The dependent variable, LOS outliers, was calculated for each diagnosis related group (DRG) using a trim point defined for each year by the geometric mean plus two standard deviations. Hospitals were classified on the basis of administrative, economic and teaching characteristics. We also studied the influence of comorbidities and readmissions. Logistic regression models, including a multivariable logistic regression, were used in the analysis. All the logistic regressions were fitted using generalized estimating equations (GEE). Results: In near nine million inpatient episodes analysed we found a proportion of 3.9% high LOS outliers, accounting for 19.2% of total inpatient days. The number of hospital patient discharges increased between years 2000 and 2005 and slightly decreased after that. The proportion of outliers ranged between the lowest value of 3.6% (in years 2001 and 2002) and the highest value of 4.3% in 2009. Teaching hospitals with over 1,000 beds have significantly more outliers than other hospitals, even after adjustment to readmissions and several patient characteristics. Conclusions: In the last years both average LOS and high LOS outliers are increasing in Portuguese NHS hospitals. As high LOS outliers represent an important proportion in the total inpatient days, this should be seen as an important alert for the management of hospitals and for national health policies. As expected, age, type of admission, and hospital type were significantly associated with high LOS outliers. The proportion of high outliers does not seem to be related to their financial coverage; they should be studied in order to highlight areas for further investigation. The increasing complexity of both hospitals and patients may be the single most important determinant of high LOS outliers and must therefore be taken into account by health managers when considering hospital costs.

Abstract
Given the large amount of data mining algorithms, their combinations (e.g. ensembles) and possible parameter settings, finding the most adequate method to analyze a new dataset becomes an ever more challenging task. This is because in many cases testing all possibly useful alternatives quickly becomes prohibitively expensive. In this paper we propose a novel technique, called active testing, that intelligently selects the most useful cross-validation tests. It proceeds in a tournament-style fashion, in each round selecting and testing the algorithm that is most likely to outperform the best algorithm of the previous round on the new dataset. This 'most promising' competitor is chosen based on a history of prior duels between both algorithms on similar datasets. Each new cross-validation test will contribute information to a better estimate of dataset similarity, and thus better predict which algorithms are most promising on the new dataset. We have evaluated this approach using a set of 292 algorithm-parameter combinations on 76 UCI datasets for classification. The results show that active testing will quickly yield an algorithm whose performance is very close to the optimum, after relatively few tests. It also provides a better solution than previously proposed methods. © 2012 Springer-Verlag.

Abstract
This research explores teenagers' knowledge representation of six digital technologies - email, IM, internet, digital photos, sms and games. Instead of pre-imposing a specific structure, teens freely express everything they consider relevant by identifying the meanings associated with each digital technology. Drawing on cognitive psychology theories and teenagers' social development theories, the data from thirteen focus groups were analyzed. The nature of attributes comprising technical features, personal and socially relevant activities/experiences, feelings and attitudes towards these instruments only partially matched other IT conceptualizations. However, those studies applied different methodological approaches. Among the 133 attributes suggested, 30 were shared by at least two digital technologies. The Multiple Correspondence Analysis showed that games were psychologically and functionally (physical attributes) more integrated with IM and internet whereas digital photos were segregated. The communicational and product design implications of assessing attributes are discussed.

Abstract
For museums, developing audiences means both attracting non-visitors to their venues, and improving repeat visitors' attendance patterns and experience. Audience development strategies encourage museums to create open door events in order to deal with barriers preventing a wider audience from becoming their visitors, and to build stronger relationships with their current visitors. Satisfaction is expected to influence future buying decisions - i.e., intention to return and to recommend. Will a satisfying experience at a museum event improve event goers' visiting patterns? This research aims to ascertain the effects of attending open day events on the development of art museum audiences. We present the findings of exploratory quantitative research using the personal interview survey method. Our results indicate that open day events have potential to develop audiences, as such events eliminate attendance barriers, attract first time visitors and provide trial experiences for potential museum visitors. However, the positive association between event experience and intention to return to the museum on an ordinary day was not statistically supported by this study. © 2012 Copyright Taylor and Francis Group, LLC.