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Background: Electronic data collection is increasingly being used for discrete choice experiments (DCEs).Objectives: To study whether paper or electronic administration results in measurement effects.Methods: Respondents were drawn from the same sample frame (an Internet panel) and completed a nearly identical DCE survey either online or on paper during the same period. A DCE on preferences for basic health insurance served as a case study. We used panel mixed logit models for the analysis.Results: In total, 898 respondents completed the survey: 533 respondents completed the survey online, whereas 365 respondents returned the paper survey. There were no significant differences with respect to sociodemographic characteristics between the respondents in both samples. The median response time was shorter for the online sample than for the paper sample, and a smaller proportion of respondents from the online sample were satisfied with the number of choice sets. Although some willingness- to-pay estimates were higher for the online sample, the elicited preferences for basic health insurance characteristics were similar between both modes of administration.Conclusions: We find no indication that online surveys yield inferior results compared with paper-based surveys, whereas the price per respondent is lower for online surveys. Researchers might want to include fewer choice sets per respondent when collecting DCE data online. Because our findings are based on a nonrandomized DCE that covers one health domain only, research in other domains is needed to support our findings. [ABSTRACT FROM AUTHOR]

Background: Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity.Objectives: In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available.Conclusions: Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force's first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods. [ABSTRACT FROM AUTHOR]

This paper highlights a preference evaluation methodology for text entry methods in a touch keyboard smartphone using analytic network process (ANP). Evaluation of text entry methods in literature mainly considers speed and accuracy. This study presents an alternative means for selecting text entry method that considers user preference. A case study was carried out with a group of experts who were asked to develop a selection decision model of five text entry methods. The decision problem is flexible enough to reflect interdependencies of decision elements that are necessary in describing real-life conditions. Results showed that QWERTY method is more preferred than other text entry methods while arrangement of keys is the most preferred criterion in characterizing a sound method. Sensitivity analysis using simulation of normally distributed random numbers under fairly large perturbation reported the foregoing results reliable enough to reflect robust judgment. The main contribution of this paper is the introduction of a multi-criteria decision approach in the preference evaluation of text entry methods. [ABSTRACT FROM AUTHOR]

Research synthesis is the integration of existing knowledge and research findings pertinent to an issue. The aim of synthesis is to increase the generality and applicability of those findings and to develop new knowledge through the process of integration. Synthesis is promoted as an approach that deals with the challenge of öinformation overload’, delivering products that further our understanding of problems and distil relevant evidence for decision-making. However, despite the increasing prominence of synthesis efforts in the science and policy landscape, we know very little about the impacts these initiatives have on research, policy and practice and the assumptions underpinning how they will lead to change. This paper presents a framework for considering the conceptual, strategic, instrumental and network-based impacts of research synthesis on policy. This framework provides insight into the range of underlying assumptions and impacts on policy and practice from 10 case studies of research synthesis related to contemporary sustainability challenges. Findings suggest that research synthesis is having diverse impacts on research, policy and practice including creating a new understanding of problems, establishing new networks, and contributing to changes in policy and practice. These impacts emerged across a range of contexts, synthesis methods, assumptions and operating models. This suggests that there is no single öcorrect way’ to design research synthesis for impact, but rather a need to tailor the approach for the context of intended use. [ABSTRACT FROM AUTHOR]

One of the most popular induction scheme for supervised learning is also one of the oldest. It builds a classifier in a top-down fashion, following the minimization of a so-called index criterion. While numerous papers have reported experiments on this scheme, little has been known on its theoretical aspect until recent works on decision trees and branching programs using a powerful classification tool: boosting.In this paper, we look at this problem from a worst-case computational (rather than informational) standpoint. Our conclusions for the ranking of these indexes’ minimization follow almost exactly that of boosting (with matching upper and lowerbounds), and provide extensions to more classes of Boolean formulas such as decision lists, multilinear polynomials and symmetric functions. Our results also exhibit a strong worst-case for the induction scheme, as we build particularly hard samples for which the replacement of most index criteria, or the class of concept representation, even when producing the same ranking as boosting does for the indexes, makes no difference at all for the concept induced. This is clearly not a limit of previous analyses, but a consequence of the induction scheme. [Copyright &y& Elsevier]

Targeted intervention and resource allocation are essential for effective malaria control, particularly in remote areas, with predictive models providing important information for decision making. While a diversity of modeling technique have been used to create predictive models of malaria, no work has made use of Bayesian networks. Bayes nets are attractive due to their ability to represent uncertainty, model time lagged and nonlinear relations, and provide explanations. This paper explores the use of Bayesian networks to model malaria, demonstrating the approach by creating village level models with weekly temporal resolution for Tha Song Yang district in northern Thailand. The networks are learned using data on cases and environmental covariates. Three types of networks are explored: networks for numeric prediction, networks for outbreak prediction, and networks that incorporate spatial autocorrelation. Evaluation of the numeric prediction network shows that the Bayes net has prediction accuracy in terms of mean absolute error of about 1.4 cases for 1 week prediction and 1.7 cases for 6 week prediction. The network for outbreak prediction has an ROC AUC above 0.9 for all prediction horizons. Comparison of prediction accuracy of both Bayes nets against several traditional modeling approaches shows the Bayes nets to outperform the other models for longer time horizon prediction of high incidence transmission. To model spread of malaria over space, we elaborate the models with links between the village networks. This results in some very large models which would be far too laborious to build by hand. So we represent the models as collections of probability logic rules and automatically generate the networks. Evaluation of the models shows that the autocorrelation links significantly improve prediction accuracy for some villages in regions of high incidence. We conclude that spatiotemporal Bayesian networks are a highly promising modeling alternative for prediction of malaria and other vector-borne diseases. [ABSTRACT FROM AUTHOR]

Background: Serious games (SGs) are a type of simulation technology that may provide nursing students with the opportunity to practice their clinical reasoning and decision-making skills in a safe and authentic environment. Despite the growing number of SGs developed for healthcare professionals, few SGs are video based or address the domain of home health care.Aims: This paper aims to describe the design, development, and usability evaluation of a video based SG for teaching clinical reasoning and decision-making skills to nursing students who care for patients with chronic obstructive pulmonary disease (COPD) in home healthcare settings.Methods: A prototype SG was developed. A unified framework of usability called TURF (Task, User, Representation, and Function) and SG theory were employed to ensure a user-centered design. The educational content was based on the clinical decision-making model, Bloom's taxonomy, and a Bachelor of Nursing curriculum. A purposeful sample of six participants evaluated the SG prototype in a usability laboratory. Cognitive walkthrough evaluations, a questionnaire, and individual interviews were used for the usability evaluation. The data were analyzed using qualitative deductive content analysis based on the TURF framework elements and related usability heuristics.Results: The SG was perceived as being realistic, clinically relevant, and at an adequate level of complexity for the intended users. Usability issues regarding functionality and the user-computer interface design were identified. However, the SG was perceived as being easy to learn, and participants suggested that the SG could serve as a supplement to traditional training in laboratory and clinical settings.Conclusions: Using video based scenarios with an authentic COPD patient and a home healthcare registered nurse as actors contributed to increased realism. Using different theoretical approaches in the SG design was considered an advantage of the design process. The SG was perceived as being useful, usable, and satisfying. The achievement of the desired functionality and the minimization of user-computer interface issues emphasize the importance of conducting a usability evaluation during the SG development process. [ABSTRACT FROM AUTHOR]

Abstract: Risk-taking is normal in everyday life if there are associated (perceived) benefits. Benefit-Risk Analysis (BRA) compares the risk of a situation to its related benefits and addresses the acceptability of the risk. Over the past years BRA in relation to food and food ingredients has gained attention. Food, and even the same food ingredient, may confer both beneficial and adverse effects. Measures directed at food safety may lead to suboptimal or insufficient levels of ingredients from a benefit perspective. In BRA, benefits and risks of food (ingredients) are assessed in one go and may conditionally be expressed into one currency. This allows the comparison of adverse and beneficial effects to be qualitative and quantitative. A BRA should help policy-makers to make more informed and balanced benefit-risk management decisions. Not allowing food benefits to occur in order to guarantee food safety is a risk management decision much the same as accepting some risk in order to achieve more benefits. BRA in food and nutrition is making progress, but difficulties remain. The field may benefit from looking across its borders to learn from other research areas. The BEPRARIBEAN project (Best Practices for Risk-Benefit Analysis: experience from out of food into food; http://en.opasnet.org/w/Bepraribean) aims to do so, by working together with Medicines, Food Microbiology, Environmental Health, Economics & Marketing-Finance and Consumer Perception. All perspectives are reviewed and subsequently integrated to identify opportunities for further development of BRA for food and food ingredients. Interesting issues that emerge are the varying degrees of risk that are deemed acceptable within the areas and the trend towards more open and participatory BRA processes. A set of 6 ‘state of the art’ papers covering the above areas and a paper integrating the separate (re)views are published in this volume. [Copyright &y& Elsevier]

Objective: Antimicrobial stewardship programs have been shown to limit the inappropriate use of antimicrobials. Hospitals are increasingly relying on clinical decision support systems to assist in the demanding prescription reviewing process. In previous work, we have reported on an emerging clinical decision support system for antimicrobial stewardship that can learn new rules supervised by user feedback. In this paper, we report on the evaluation of this system.Methods: The evaluated system uses a knowledge base coupled with a supervised learning module that extracts classification rules for inappropriate antimicrobial prescriptions using past recommendations for dose and dosing frequency adjustments, discontinuation of therapy, early switch from intravenous to oral therapy, and redundant antimicrobial spectrum. Over five weeks, the learning module was deployed alongside the baseline system to prospectively evaluate its ability to discover rules that complement the existing knowledge base for identifying inappropriate prescriptions of piperacillin-tazobactam, a frequently used antimicrobial.Results: The antimicrobial stewardship pharmacists reviewed 374 prescriptions, of which 209 (56% of 374) were identified as inappropriate leading to 43 recommendations to optimize prescriptions. The baseline system combined with the learning module triggered alerts in 270 prescriptions with a positive predictive value of identifying inappropriate prescriptions of 74%. Of these, 240 reviewed prescriptions were identified by the alerts of the baseline system with a positive predictive value of 82% and 105 reviewed prescriptions were identified by the alerts of the learning module with a positive predictive value of 62%. The combined system triggered alerts for all 43 recommendations, resulting in a rate of actionable alerts of 16% (43 recommendations of 270 reviewed alerts); the baseline system triggered alerts for 38 interventions, resulting in a rate of actionable alerts of 16% (38 of 240 reviewed alerts); and the learning module triggered alerts for 17 interventions, resulting in a rate of actionable alerts of 16% (17 of 105 reviewed alerts). The learning module triggered alerts for every inappropriate prescription missed by the knowledge base of the baseline system (n=5).Conclusions: The learning module was able to extract clinically relevant rules for multiple types of antimicrobial alerts. The learned rules were shown to extend the knowledge base of the baseline system by identifying pharmacist interventions that were missed by the baseline system. The learned rules identified inappropriate prescribing practices that were not supported by local experts and were missing from its knowledge base. However, combining the baseline system and the learning module increased the number of false positives. [ABSTRACT FROM AUTHOR]

Objective: Feature selection is a technique widely used in data mining. The aim is to select the best subset of features relevant to the problem being considered. In this paper, we consider feature selection for the classification of gene datasets. Gene data is usually composed of just a few dozen objects described by thousands of features. For this kind of data, it is easy to find a model that fits the learning data. However, it is not easy to find one that will simultaneously evaluate new data equally well as learning data. This overfitting issue is well known as regards classification and regression, but it also applies to feature selection.Methods and Materials: We address this problem and investigate its importance in an empirical study of four feature selection methods applied to seven high-dimensional gene datasets. We chose datasets that are well studied in the literature-colon cancer, leukemia and breast cancer. All the datasets are characterized by a significant number of features and the presence of exactly two decision classes. The feature selection methods used are ReliefF, minimum redundancy maximum relevance, support vector machine-recursive feature elimination and relaxed linear separability.Results: Our main result reveals the existence of positive feature selection bias in all 28 experiments (7 datasets and 4 feature selection methods). Bias was calculated as the difference between validation and test accuracies and ranges from 2.6% to as much as 41.67%. The validation accuracy (biased accuracy) was calculated on the same dataset on which the feature selection was performed. The test accuracy was calculated for data that was not used for feature selection (by so called external cross-validation).Conclusions: This work provides evidence that using the same dataset for feature selection and learning is not appropriate. We recommend using cross-validation for feature selection in order to reduce selection bias. [ABSTRACT FROM AUTHOR]

Compared to document-based systems engineering (SE) models, IT-enabled ones can introduce radical changes in how SE is practiced. Such models are increasingly important today, but how well do we understand their use, impact, and issues? Real-world cases are helpful but tell us little about how and why models work (or don’t), their suitability for a particular context, or how to guide selection and use. Lacking sound knowledge and theory, we cannot assess the value of Model-Based Systems Engineering (MBSE) [1] or predict its outcomes [2] . Instead, we rely on heuristics and judgment, e.g., to make process tailoring decisions [3] . Our motivations include identifying core research needs, provoking discussion on key issues and questions [4] and generating interest by applied and theoretical researchers, including from outside engineering. Other authors have noted that SE would benefit from broader research methodologies and methods (e.g., [5]; [6] ) and greater participation by non-engineering disciplines (e.g., [2]; [7] ). This paper identifies MBSE research topics culled from literature and briefly discusses some key ones based on a work-centered organizational approach with the goal of sparking interest in foundational research. [ABSTRACT FROM AUTHOR]

Choices in business processes are often based on the process history saved as a log-file listing events and their time stamps. In this paper we introduce LogLogics, a finite-path variant of the Timed Propositional Temporal Logic with Past, which can be in particular used for specifying guards in business process models. The novelty is due to the presence of boundary points corresponding to the starting and current observation points, which gives rise to a three-valued logic allowing us to distinguish between temporal formulas that hold for any log extended with some possible past and future (true), those that do not hold for any extended log (false) and those that hold for some but not all extended logs (unknown). We reduce the check of the truth value of a LogLogics formula to a check on a finite abstraction and present an evaluation algorithm. We also define LogLogics patterns for commonly occurring properties. [Copyright &y& Elsevier]

The purpose of this paper is to provide a review of the current state of fuzzy logic theory in epidemioloy, which is a recent area of research. We present four applications of fuzzy logic theory in epidemic problems, using linguistic fuzzy models, possibility measure, probability of fuzzy events and fuzzy decision making techniques. The results demonstrate that the applications of fuzzy sets in epidemiology is a very promising area of research. The final discussion sets the future stage of fuzzy sets application in epidemiology. [Copyright &y& Elsevier]

This paper begins with a discussion of why concerns about decision-making capacity are warranted in palliative medicine research. Next, procedures for assessing decision-making capacity are discussed. It concludes with recommendations to guide the judicious use of these procedures in the design of palliative care research. [Copyright &y& Elsevier]

Time plays an important role in medicine, both the past and the future. The medical history of a patient represents the past, which needs to be understood by the physician to make the right decisions. The past contains two different kinds of information: measured data (such as blood pressure) and incidents (such as seizures). Planning therapies, on the other hand, requires looking into the future to a certain extent. Visual representations exist for both the past and the future, and they are very useful for getting a better understanding of data or a plan. This paper surveys visualization techniques for both data analysis and planning, and compares them based on a number of criteria. [Copyright &y& Elsevier]