A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems.

The objective of this review is to describe the implementation of human factors principles for the design of alerts in clinical information systems. First, we conduct a review of alarm systems to identify human factors principles that are employed in the design and implementation of alerts. Second, we review the medical informatics literature to provide examples of the implementation of human factors principles in current clinical information systems using alerts to provide medication decision support. Last, we suggest actionable recommendations for delivering effective clinical decision support using alerts. A review of studies from the medical informatics literature suggests that many basic human factors principles are not followed, possibly contributing to the lack of acceptance of alerts in clinical information systems. We evaluate the limitations of current alerting philosophies and provide recommendations for improving acceptance of alerts by incorporating human factors principles in their design

A Novel Approach for Performance Characterization of IaaS Clouds

The ability to predict the energy consumption of an HPC task, varying the number of assigned nodes, can lead to the ability to assign the correct number of nodes to tasks, saving large amount of energy. In this paper we present LBM, a model capable of predicting the resource usage (applicable to different resources, such as completion time and energy consumption) of programs, following a black box approach, where only passive measures of the running program are used to build the prediction model, without requiring its source code, or static analysis of the binary. LBM builds the predicting model using other programs as benchmarks. We tested LBM predicting the energy consumption of pitzDaily, a case of the OpenFOAM CFD suite, using a very low number of benchmarks (3), obtaining extremely precise predictions

Criteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records

Background: High override rates for drug-drug interaction (DDI) alerts in electronic health records (EHRs) result in the potentially dangerous consequence of providers ignoring clinically significant alerts. Lack of uniformity of criteria for determining the severity or validity of these interactions often results in discrepancies in how these are evaluated. The purpose of this study was to identify a set of criteria for assessing DDIs that should be used for the generation of clinical decision support (CDS) alerts in EHRs. Methods: We conducted a 20-year systematic literature review of MEDLINE and EMBASE to identify characteristics of high-priority DDIs. These criteria were validated by an expert panel consisting of medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use. Results: Forty-four articles met the inclusion criteria for assessing characteristics of high-priority DDIs. The panel considered five criteria to be most important when assessing an interaction- Severity, Probability, Clinical Implications of the interaction, Patient characteristics, and the Evidence supporting the interaction. In addition, the panel identified barriers and considerations for being able to utilize these criteria in medication knowledge bases used by EHRs. Conclusions: A multi-dimensional approach is needed to understanding the importance of an interaction for inclusion in medication knowledge bases for the purpose of CDS alerting. The criteria identified in this study can serve as a first step towards a uniform approach in assessing which interactions are critical and warrant interruption of a provider’s workflow

Modeling of objects using planar facets in noisy range images

Products designed and manufactured before the advent of Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) technology have not been documented electronically. To avoid the laborious procedure of redesigning the parts, a reverse engineering approach can be adopted. This approach involves, taking a picture of the object and constructing a solid model from the image data. Range image is a three dimensional image of an object or a scene. This image can be obtained from special cameras, called range image cameras, or can be constructed from the Coordinate Measuring Machine\u27s (CMM) output data. Adaptive Fuzzy c-Elliptotype (AFC) clustering algorithm is used to identify the planar facets in a range image. A modified version of AFC algorithm can handle noisy range images. Unknown number of planar facets can be identified using the Agglomerative clustering approach. The object is reconstructed using segmented image data. The equations of the edge are obtained from the plane intersections. An edge validity criterion is developed to validate the existence of an edge. Vertices are the two extreme points on the edge. A Boundary representation of the object is developed. The information about this object is then passed to a CAD software using Initial Graphics Exchange Specification (IGES)

Measuring program similarity for efficient benchmarking and performance analysis of computer systems

textComputer benchmarking involves running a set of benchmark programs to measure performance of a computer system. Modern benchmarks are developed from real applications. Applications are becoming complex and hence modern benchmarks run for a very long time. These benchmarks are also used for performance evaluation in the early design phase of microprocessors. Due to the size of benchmarks and increase in complexity of microprocessor design, the effort required for performance evaluation has increased significantly. This dissertation proposes methodologies to reduce the effort of benchmarking and performance evaluation of computer systems. Identifying a set of programs that can be used in the process of benchmarking can be very challenging. A solution to this problem can start by identifying similarity between programs to capture the diversity in their behavior before they can be considered for benchmarking. The aim of this methodology is to identify redundancy in the set of benchmarks and find a subset of representative benchmarks with the least possible loss of information. This dissertation proposes the use of program characteristics which capture the performance behavior of programs and identifies representative benchmarks applicable over a wide range of system configurations. The use of benchmark subsetting has not been restricted to academic research. Recently, the SPEC CPU subcommittee used the information derived from measuring similarity based on program behavior characteristics between different benchmark candidates as one of the criteria for selecting the SPEC CPU2006 benchmarks. The information of similarity between programs can also be used to predict performance of an application when it is difficult to port the application on different platforms. This is a common problem when a customer wants to buy the best computer system for his application. Performance of a customer's application on a particular system can be predicted using the performance scores of the standard benchmarks on that system and the similarity information between the application and the benchmarks. Similarity between programs is quantified by the distance between them in the space of the measured characteristics, and is appropriately used to predict performance of a new application using the performance scores of its neighbors in the workload space.Electrical and Computer Engineerin

Development and preliminary evidence for the validity of an instrument assessing implementation of human-factors principles in medication-related decision-support systems—I-MeDeSA

Background Medication-related decision support can reduce the frequency of preventable adverse drug events. However, the design of current medication alerts often results in alert fatigue and high over-ride rates, thus reducing any potential benefits. Methods The authors previously reviewed human-factors principles for relevance to medication-related decision support alerts. In this study, instrument items were developed for assessing the appropriate implementation of these human-factors principles in drug-drug interaction (DDI) alerts. User feedback regarding nine electronic medical records was considered during the development process. Content validity, construct validity through correlation analysis, and inter-rater reliability were assessed. Results The final version of the instrument included 26 items associated with nine human-factors principles. Content validation on three systems resulted in the addition of one principle (Corrective Actions) to the instrument and the elimination of eight items. Additionally, the wording of eight items was altered. Correlation analysis suggests a direct relationship between system age and performance of DDI alerts (p=0.0016). Inter-rater reliability indicated substantial agreement between raters (κ=0.764). Conclusion The authors developed and gathered preliminary evidence for the validity of an instrument that measures the appropriate use of human-factors principles in the design and display of DDI alerts. Designers of DDI alerts may use the instrument to improve usability and increase user acceptance of medication alerts, and organizations selecting an electronic medical record may find the instrument helpful in meeting their clinicians' usability need

Assessing net economic gains from domestic and industrial water supply: cases from NRLP schemes

This paper attempts to identify and evolve a method for valuing and estimating the net gains from domestic and industrial water supply from the interbasin transfer schemes contemplated in the National River Link Project (NRLP). An existing interbasin transfer (IBT) scheme, namely Indira Gandhi Nahar Project (IGNP) and a proposed IBT scheme namely Polavaram- Vijaywada (PV) Link Canal were chosen for detailed analyses. Secondary data were used for identifying the region and the populations that benefited from the schemes. Economic gains arising out of water supply to the actual or potentially benefited areas were estimated. The estimation involved assessment of current costs incurred by the people in the area, in terms of both paid-out costs and time spent in fetching water. The saving in time was valued at market wage rates prevalent in the area and paid-out costs were assessed in terms of current market prices, ignoring the administered prices involved. The gains to urban populations were assessed by estimating the reduction in energy costs incurred by municipal authorities in undertaking the supply. Amortized capital costs for putting necessary hardware for distributing water from the IBT schemes as well as operation and maintenance (O&M) costs of running these schemes were netted from the gains to obtain the figures for net economic gains. More indirect benefits such as reduced drudgery or improved educational performance as well as reduced health expenditure were recognized but were all ignored to ensure greater robustness in the estimates. Only net gains to the society were considered and hence gains arising out of creation of industrial estates within the commands were ignored since similar gains could also be obtained by locating these estates elsewhere. The net economic gains are seen to depend on both demographic features of the region and its ecology. Desert-like conditions of the IGNP-benefited areas tend to make the gains from domestic water supply schemes large, while similar gains in the Polavaram-Vijaywada areas are smaller. The net economic gains are of a significant order and would seem to indicate that, at least insofar as the dry areas of the country are concerned, these can perhaps exceed the gains due to increased agricultural production and hence could perhaps justify the creation of the schemes by themselves

A Systematic Literature Review With Bibliometric Meta-Analysis Of Deep Learning And 3D Reconstruction Methods In Image Based Food Volume Estimation Using Scopus, Web Of Science And IEEE Database

Purpose- Estimation of food portions is necessary in image based dietary monitoring techniques. The purpose of this systematic survey is to identify peer reviewed literature in image-based food volume estimation methods in Scopus, Web of Science and IEEE database. It further analyzes bibliometric survey of image-based food volume estimation methods with 3D reconstruction and deep learning techniques. Design/methodology/approach- Scopus, Web of Science and IEEE citation databases are used to gather the data. Using advanced keyword search and PRISMA approach, relevant papers were extracted, selected and analyzed. The bibliographic data of the articles published in the journals over the past twenty years were extracted. A deeper analysis was performed using bibliometric indicators and applications with Microsoft Excel and VOS viewer. A comparative analysis of the most cited works in deep learning and 3D reconstruction methods is performed. Findings: This review summarizes the results from the extracted literature. It traces research directions in the food volume estimation methods. Bibliometric analysis and PRISMA search results suggest a broader taxonomy of the image-based methods to estimate food volume in dietary management systems and projects. Deep learning and 3D reconstruction methods show better accuracy in the estimations over other approaches. The work also discusses importance of diverse and robust image datasets for training accurate learning models in food volume estimation. Practical implications- Bibliometric analysis and systematic review gives insights to researchers, dieticians and practitioners with the research trends in estimation of food portions and their accuracy. It also discusses the challenges in building food volume estimator model using deep learning and opens new research directions. Originality/value- This study represents an overview of the research in the food volume estimation methods using deep learning and 3D reconstruction methods using works from 1995 to 2020. The findings present the five different popular methods which have been used in the image based food volume estimation and also shows the research trends with the emerging 3D reconstruction and deep learning methodologies. Additionally, the work emphasizes the challenges in the use of these approaches and need of developing more diverse, benchmark image data sets for food volume estimation including raw food, cooked food in all states and served with different containers

Phasenfeldmodellierung von Brüchen mit einer räumlich variierenden Längenvariablen und adaptiver Netzverfeinerung

Phase-field models of brittle fracture introduce smeared cracks of width commensurate with a regularisation length parameter ϵ. These regularised fracture model obey a minimum energy principle. Mesh adaptivity naturally suggests itself as a means of supplying spatial resolution were needed while simultaneously keeping the computational size of the model as small as possible. Here, a variational-based spatial adaptivity is proposed for a phase-field model of fracture. An extension of the conventional phase-field model is achieved by allowing spatial variation of the regularisation length ϵ in the domain. The regularisation length ϵ is therefore treated as a variable in the energy functional. Similar to the displacement and phase fields, the regularisation length is obtained by minimising the energy functional. This extended phase-field model serves as the foundation for an adaptive mesh refinement strategy, in which the mesh size is determined locally by the regularisation length. The resulting procedure is implemented in the framework of the finite element library FEniCS. According to the selected numerical experiments, the spatially adaptive phase-field model converges marginally faster than the conventional phase-field model but with a vastly superior constant, resulting in significant computational savings.In Phasenfeldmodellen für spröde Brüche werden verschmierte Risse mit einer Breite eingeführt, die einem Regularisierungslängenparameter ϵ entspricht. Diese regulierten Bruchmodelle gehorchen dem Prinzip der minimalen Energie. Die Netzanpassung bietet sich als geeignets natürlich Mittel an, um die erforderliche räumliche Auflösung zu erreichen und gleichzeitig den Rechenaufwand des Modells so klein wie möglich zu halten. Hier wird eine variationsbasierte räumliche Adaptivität für ein Phasenfeldmodell für Brüche vorgeschlagen. Eine Erweiterung des konventionellen Phasenfeldmodells wird dadurch erreicht, dass die räumliche Variation der Regularisierungslänge ϵ im Gebiet zugelassen wird. Die Regularisierungslänge ϵ wird daher als eine Variable im Energiefunktional behandelt. Ähnlich wie bei den Verschiebungs- und Phasenfeldern wird die Regularisierungslänge durch Minimierung des Energiefunktionals ermittelt. Dieses erweiterte Phasenfeldmodell dient als Grundlage für eine adaptive Netzverfeinerungsstrategie, bei der die Netzgröße lokal durch die Regularisierungslänge bestimmt wird. Das resultierende Verfahren wird im Rahmen der Finite-Elemente-Bibliothek FEniCS implementiert. Die ausgewählte numerischen Experimente zeigten, dass das räumlich adaptive Phasenfeldmodell geringfügig schneller konvergiert als das konventionelle Phasenfeldmodell, jedoch mit einer deutlich besseren Konstanten, was zu erheblichen Rechenzeiteinsparungen führt