EHR STAR: The State‐Of‐the‐Art in Interactive EHR Visualization

Since the inception of electronic health records (EHR) and population health records (PopHR), the volume of archived digital health records is growing rapidly. Large volumes of heterogeneous health records require advanced visualization and visual analytics systems to uncover valuable insight buried in complex databases. As a vibrant sub-field of information visualization and visual analytics, many interactive EHR and PopHR visualization (EHR Vis) systems have been proposed, developed, and evaluated by clinicians to support effective clinical analysis and decision making. We present the state-of-the-art (STAR) of EHR Vis literature and open access healthcare data sources and provide an up-to-date overview on this important topic. We identify trends and challenges in the field, introduce novel literature and data classifications, and incorporate a popular medical terminology standard called the Unified Medical Language System (UMLS). We provide a curated list of electronic and population healthcare data sources and open access datasets as a resource for potential researchers, in order to address one of the main challenges in this field. We classify the literature based on multidisciplinary research themes stemming from reoccurring topics. The survey provides a valuable overview of EHR Vis revealing both mature areas and potential future multidisciplinary research directions

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Isomorphic effects of managerial and directoral career paths

The extensive array of interlocking directorate research remains near-exclusively cross-sectional or comparative cross-sectional in nature. While this has been fruitful in identifying persistent structures of inter-organisational relationships evidence of the impact of these structures on organisational performance or activity has been more limited. This should not be surprising because, by their nature, relationships have strong longitudinal and dynamic qualities that are likely to be difficult to isolate through cross-sectional approaches. Clearly, managerial practice is inevitably strongly conditioned by the specific contingencies of the time and the information available through networks of colleagues and advisers (particularly at board level) at the time. But managerial and directoral capabilities and mental sets are also developed over time, particularly through previous experiences in these roles and the formation of long-lasting 'strong' and 'weak' relationships. This paper tests the influence of three longitudinal dimensions of managers and directors' relationships on a set of indicators of financial performance, drawing from a large dataset of detailing historic board membership of UK firms. It finds evidence of isomorphic processes through these channels and establishes that the longitudinal design considerably enhances the detection of performance effects from directorate interlocks. More broadly, the research has implications for the conception of collective action and the constitution of 'community'

Toward Precision Medicine in Intensive Care: Leveraging Electronic Health Records and Patient Similarity

The growing adoption of Electronic Health Record (EHR) systems has resulted in an unprecedented amount of data. This availability of data has also opened up the opportunity to utilize EHRs for providing more customized care for each patient by considering individual variability, which is the goal of precision medicine. In this context, patient similarity (PS) analytics have been introduced to facilitate data analysis through investigating the similarities in patients’ data, and, ultimately, to help improve the healthcare system. This dissertation is presented in six chapters and focuses on employing PS analytics in data-rich intensive care units. Chapter 1 provides a review of the literature and summarizes studies describing approaches for predicting patients’ future health status based on EHR and PS. Chapter 2 demonstrates the informativeness of missing data in patient profiles and introduces missing data indicators to use this information in mortality prediction. The results demonstrate that including indicators with observed measurements in a set of well-known prediction models (logistic regression, decision tree, and random forest) can improve the predictive accuracy. Chapter 3 builds upon the previous results and utilizes these missing indicators to reveal patient subpopulations based on their similarity in laboratory test ordering being used for them. In this chapter, the Density-based Spatial Clustering of Applications with Noise method, was employed to group the patients into clusters using the indicators generated in the previous study. Results confirmed that missing indicators capture the laboratory-test-ordering patterns that are informative and can be used to identify similar patient subpopulations. Chapter 4 investigates the performance of a multifaceted PS metric constructed by utilizing appropriate similarity metrics for specific clinical variables (e.g. vital signs, ICD-9, etc.). The proposed PS metric was evaluated in a 30-day post-discharge mortality prediction problem. Results demonstrate that PS-based prediction models with the new PS metric outperformed population-based prediction models. Moreover, the multifaceted PS metric significantly outperformed cosine and Euclidean PS metric in k-nearest neighbors setting. Chapter 5 takes the previous results into consideration and looks for potential subpopulations among septic patients. Sepsis is one of the most common causes of death in Canada. The focus of this chapter is on longitudinal EHR data which are a collection of observations of measurements made chronologically for each patient. This chapter employs Functional Principal Component Analysis to derive the dominant modes of variation in septic patients’ EHR's. Results confirm that including temporal data in the analysis can help in identifying subgroups of septic patients. Finally, Chapter 6 provides a discussion of results from previous chapters. The results indicate the informativeness of missing data and how PS can help in improving the performance of predictive modeling. Moreover, results show that utilizing the temporal information in PS calculation improves patient stratification. Finally, the discussion identifies limitations and directions for future research

Papel de los mundos metaversos como alternativa de generación de condiciones clave en la construcción de ambientes personales de aprendizaje

367 Páginas.El presente informe describe el proceso y los resultados de una investigación de naturaleza cualitativa, a manera de estudio de diferentes múltiples, acerca del papel que juegan los Mundos Metaversos en la construcción de Ambientes Personales de Aprendizaje. Luego de realizar una exploración teórica se encontraron cuatro condiciones clave que permiten su construcción y que se tomaron como las categorías de análisis de la investigación: la identidad, el flujo de información, las herramientas y la emoción

Syndromic surveillance: reports from a national conference, 2004

Overview, Policy, and Systems -- Federal Role in Early Detection Preparedness Systems -- BioSense: Implementation of a National Early Event Detection and Situational Awareness System -- Guidelines for Constructing a Statewide Hospital Syndromic Surveillance Network -- -- Data Sources -- Implementation of Laboratory Order Data in BioSense Early Event Detection and Situation Awareness System -- Use of Medicaid Prescription Data for Syndromic Surveillance ? New York -- Poison Control Center?Based Syndromic Surveillance for Foodborne Illness -- Monitoring Over-The-Counter Medication Sales for Early Detection of Disease Outbreaks ? New York City -- Experimental Surveillance Using Data on Sales of Over-the-Counter Medications ? Japan, November 2003?April 2004 -- -- Analytic Methods -- Public Health Monitoring Tools for Multiple Data Streams -- Use of Multiple Data Streams to Conduct Bayesian Biologic Surveillance -- Space-Time Clusters with Flexible Shapes -- INFERNO: A System for Early Outbreak Detection and Signature Forecasting -- High-Fidelity Injection Detectability Experiments: a Tool for Evaluating Syndromic Surveillance Systems -- Linked Analysis for Definition of Nurse Advice Line Syndrome Groups, and Comparison to Encounters -- -- Simulation and Other Evaluation Approaches -- Simulation for Assessing Statistical Methods of Biologic Terrorism Surveillance -- An Evaluation Model for Syndromic Surveillance: Assessing the Performance of a Temporal Algorithm -- Evaluation of Syndromic Surveillance Based on National Health Service Direct Derived Data ? England and Wales -- Initial Evaluation of the Early Aberration Reporting System ? Florida -- -- Practice and Experience -- Deciphering Data Anomalies in BioSense -- Syndromic Surveillance on the Epidemiologist?s Desktop: Making Sense of Much Data -- Connecting Health Departments and Providers: Syndromic Surveillance?s Last Mile -- Comparison of Syndromic Surveillance and a Sentinel Provider System in Detecting an Influenza Outbreak ? Denver, Colorado, 2003 -- Ambulatory-Care Diagnoses as Potential Indicators of Outbreaks of Gastrointestinal Illness ? Minnesota -- Emergency Department Visits for Concern Regarding Anthrax ? New Jersey, 2001 -- Hospital Admissions Syndromic Surveillance ? Connecticut, October 2001?June 2004 -- Three Years of Emergency Department Gastrointestinal Syndromic Surveillance in New York City: What Have we Found?"August 26, 2005."Papers from the National Syndromic Surveillance Conference sponsored by the Centers for Disease Control and Prevention, the Tufts Health Care Institute, the Alfred P. Sloan Foundation, held Nov. 3-4, 2004 in Boston, MA."Public health surveillance continues to broaden in scope and intensity. Public health professionals responsible for conducting such surveillance must keep pace with evolving methodologies, models, business rules, policies, roles, and procedures. The third annual Syndromic Surveillance Conference was held in Boston, Massachusetts, during November 3-4, 2004. The conference was attended by 440 persons representing the public health, academic, and private-sector communities from 10 countries and provided a forum for scientific discourse and interaction regarding multiple aspects of public health surveillance." - p. 3Also vailable via the World Wide Web