Use of Quality Management Methods and Tools - a Systematic Review of the Literature

Bakalářská práce se skládá ze dvou částí: teoretické a praktické. V teoretické části práce popisujeme a charakterizujeme metody a nástroje managementu kvality. V praktické části jsme se zaměřili na shromáždění a analýzu publikací zabývajících se možnostmi využívání metod a nástrojů managementu kvality v různých ekonomických a sociálních oblastech. Pro rychlejší vyhledávání sledovaných publikací jsme využili dvě databáze: (www.webofscience.com) a IEE Xplore (https://ieeexplore.ieee.org).The Bachelor thesis consists of two parts: theoretical and practical. In the theoretical part of the work, we describe and characterize the methods and tools of quality management. In the practical part, we focused on gathering and analysing publications dealing with the possibilities of using quality management methods and tools in various economic and social areas. We used two databases for faster searches of monitored publications: (www.webofscience.com) and IEE Xplore(https://ieeexplore.ieee.org).639 - Katedra managementu kvalitydobř

Visual Analytics for Performing Complex Tasks with Electronic Health Records

Electronic health record systems (EHRs) facilitate the storage, retrieval, and sharing of patient health data; however, the availability of data does not directly translate to support for tasks that healthcare providers encounter every day. In recent years, healthcare providers employ a large volume of clinical data stored in EHRs to perform various complex data-intensive tasks. The overwhelming volume of clinical data stored in EHRs and a lack of support for the execution of EHR-driven tasks are, but a few problems healthcare providers face while working with EHR-based systems. Thus, there is a demand for computational systems that can facilitate the performance of complex tasks that involve the use and working with the vast amount of data stored in EHRs. Visual analytics (VA) offers great promise in handling such information overload challenges by integrating advanced analytics techniques with interactive visualizations. The user-controlled environment that VA systems provide allows healthcare providers to guide the analytics techniques on analyzing and managing EHR data through interactive visualizations. The goal of this research is to demonstrate how VA systems can be designed systematically to support the performance of complex EHR-driven tasks. In light of this, we present an activity and task analysis framework to analyze EHR-driven tasks in the context of interactive visualization systems. We also conduct a systematic literature review of EHR-based VA systems and identify the primary dimensions of the VA design space to evaluate these systems and identify the gaps. Two novel EHR-based VA systems (SUNRISE and VERONICA) are then designed to bridge the gaps. SUNRISE incorporates frequent itemset mining, extreme gradient boosting, and interactive visualizations to allow users to interactively explore the relationships between laboratory test results and a disease outcome. The other proposed system, VERONICA, uses a representative set of supervised machine learning techniques to find the group of features with the strongest predictive power and make the analytic results accessible through an interactive visual interface. We demonstrate the usefulness of these systems through a usage scenario with acute kidney injury using large provincial healthcare databases from Ontario, Canada, stored at ICES

Multi-scale Visualization Design for Interactively Analyzing Large Time-series Data

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 서진욱.We propose a unified visualization model, called a ripple graph, that takes the benefits of both of the bar graph and line graph with enhanced graphical integrity for not only the regularly measured but also irregularly measured time-series data. The ripple graph also unveils uncertainty of values between two temporal measurements by varying color intensity depending upon the confidence of the values. In doing so, it can effectively reveal the measurement frequency or interval while still showing the overall temporal pattern of change. We further extend the ripple graph representation into a single unified multi-scale visualization model via an interactive 2D widget to accommodate the advantages of other efficient time-series data visualization techniques while addressing the scalability issue. We have conducted a controlled user study to show the efficacy of the ripple graph by comparing it to existing representations (i.e. line graph, bar graph, and interactive horizon graph), after selecting representative tasks (i.e. Max, Same, Frequency, and Confidence task) for time-series data visualization. Results show that ripple graph is overall the best performing in terms of task time, correctness, and subjective satisfaction across all task types. Following a participatory design process with neurologists, we design an interactive visual exploration tool for time-series data, called Stroscope, based on the ripple graph representation and the widget. Stroscope provides various interactions to navigate data in temporal aspect and supports algorithmic time-series analysis methods to accomplish certain analytical tasks. We have also performed long-term case studies with two neurologists dealing with blood pressure measurements for 1600 stroke patients to show the effectiveness of Stroscope. They have could visually explore individual blood pressure values and their changes over time while maintaining the context, which could lead to save time and effort on exploratory analyses in comparison with using conventional statistical tools. In analyzing blood pressure data, Stroscope enables them to (1) find patients with anomalous patterns, (2) compare between two groups in terms of measurement values, measurement frequency, and fluctuation, (3) confirm what they already knew, and (4) formulate a new hypothesis.Abstract i Contents v List of Figures viii List of Tables xiii Chapter 1 Introduction 1 1.1 Background & Motivation 1 1.2 Main Contribution 6 1.3 Organization of the Dissertation 9 Chapter 2 Related Work 11 2.1 (Large) Time-series data visualization 11 2.2 Event sequences data visualization 16 2.3 Interaction 17 2.4 Evaluation 19 Chapter 3 Problem Analysis 21 3.1 Dataset 22 3.2 A Scenario – Status Quo 24 3.3 Design Process 25 3.4 Design Rationale 26 Chapter 4 Ripple Graph: A Multi-scale Visualization Model for time-series data 29 4.1 Visual Representation 30 4.2 Multi-scale Modeling 32 4.2.1 Dimension zooming with range of interest (ROI) 32 4.2.2 Color mapping to further distinguish bars 33 4.2.3 Moving the horizontal axis 34 4.3 Visualizing degree of certainty between masurements 35 4.4 User interface for ripple graph manipulation 37 4.4.1 Control panel 37 4.4.2 Focus lens 39 Chapter 5 Usability Study 43 5.1 Participants and materials 43 5.2 Tasks 44 5.3 Procedure 46 5.4 Results 48 5.5 Discussion 50 Chapter 6 Controlled User Study 55 6.1 Participants and materials 55 6.2 Visualization techniques 56 6.3 Tasks 57 6.4 Study design and procedure 58 6.5 Results 60 6.6 Discussion 68 Chapter 7 Stroscope 69 7.1 Layout 69 7.2 User Interaction 71 7.3 Analytical Features 74 7.4 Implementation 78 Chapter 8 Case Study 79 8.1 Procedure 79 8.2 Participant 1 (P1) 80 8.3 Participant 2 (P2) 85 8.4 Discussion 89 Chapter 9 Conclusion 93 Bibliography 95 Abstract in Korean 105Docto

Visual Analytics of Electronic Health Records with a focus on Acute Kidney Injury

The increasing use of electronic platforms in healthcare has resulted in the generation of unprecedented amounts of data in recent years. The amount of data available to clinical researchers, physicians, and healthcare administrators continues to grow, which creates an untapped resource with the ability to improve the healthcare system drastically. Despite the enthusiasm for adopting electronic health records (EHRs), some recent studies have shown that EHR-based systems hardly improve the ability of healthcare providers to make better decisions. One reason for this inefficacy is that these systems do not allow for human-data interaction in a manner that fits and supports the needs of healthcare providers. Another reason is the information overload, which makes healthcare providers often misunderstand, misinterpret, ignore, or overlook vital data. The emergence of a type of computational system known as visual analytics (VA), has the potential to reduce the complexity of EHR data by combining advanced analytics techniques with interactive visualizations to analyze, synthesize, and facilitate high-level activities while allowing users to get more involved in a discourse with the data. The purpose of this research is to demonstrate the use of sophisticated visual analytics systems to solve various EHR-related research problems. This dissertation includes a framework by which we identify gaps in existing EHR-based systems and conceptualize the data-driven activities and tasks of our proposed systems. Two novel VA systems (VISA_M3R3 and VALENCIA) and two studies are designed to bridge the gaps. VISA_M3R3 incorporates multiple regression, frequent itemset mining, and interactive visualization to assist users in the identification of nephrotoxic medications. Another proposed system, VALENCIA, brings a wide range of dimension reduction and cluster analysis techniques to analyze high-dimensional EHRs, integrate them seamlessly, and make them accessible through interactive visualizations. The studies are conducted to develop prediction models to classify patients who are at risk of developing acute kidney injury (AKI) and identify AKI-associated medication and medication combinations using EHRs. Through healthcare administrative datasets stored at the ICES-KDT (Kidney Dialysis and Transplantation program), London, Ontario, we have demonstrated how our proposed systems and prediction models can be used to solve real-world problems

Visualizing Evaluative Language in Relation to Constructing Identity in English Editorials and Op-Eds

This thesis is concerned with the problem of managing complexity in Systemic Functional Linguistic (SFL) analyses of language, particularly at the discourse semantics level. To deal with this complexity, the thesis develops AppAnn, a suite of linguistic visualization techniques that are specifically designed to provide both synoptic and dynamic views on discourse semantic patterns in text and corpus. Moreover, AppAnn visualizations are illustrated in a series of explorations of identity in a corpus of editorials and op-eds about the bin Laden killing. The findings suggest that the intriguing intricacies of discourse semantic meanings can be successfully discerned and more readily understood through linguistic visualization. The findings also provide insightful implications for discourse analysis by contributing to our understanding of a number of underdeveloped concepts of SFL, including coupling, commitment, instantiation, affiliation and individuation