Doctor of Philosophy

dissertationPublic health surveillance systems are crucial for the timely detection and response to public health threats. Since the terrorist attacks of September 11, 2001, and the release of anthrax in the following month, there has been a heightened interest in public health surveillance. The years immediately following these attacks were met with increased awareness and funding from the federal government which has significantly strengthened the United States surveillance capabilities; however, despite these improvements, there are substantial challenges faced by today's public health surveillance systems. Problems with the current surveillance systems include: a) lack of leveraging unstructured public health data for surveillance purposes; and b) lack of information integration and the ability to leverage resources, applications or other surveillance efforts due to systems being built on a centralized model. This research addresses these problems by focusing on the development and evaluation of new informatics methods to improve the public health surveillance. To address the problems above, we first identified a current public surveillance workflow which is affected by the problems described and has the opportunity for enhancement through current informatics techniques. The 122 Mortality Surveillance for Pneumonia and Influenza was chosen as the primary use case for this dissertation work. The second step involved demonstrating the feasibility of using unstructured public health data, in this case death certificates. For this we created and evaluated a pipeline iv composed of a detection rule and natural language processor, for the coding of death certificates and the identification of pneumonia and influenza cases. The second problem was addressed by presenting the rationale of creating a federated model by leveraging grid technology concepts and tools for the sharing and epidemiological analyses of public health data. As a case study of this approach, a secured virtual organization was created where users are able to access two grid data services, using death certificates from the Utah Department of Health, and two analytical grid services, MetaMap and R. A scientific workflow was created using the published services to replicate the mortality surveillance workflow. To validate these approaches, and provide proofs-of-concepts, a series of real-world scenarios were conducted

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

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

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions

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

Thinking interactively with visualization

Interaction is becoming an integral part of using visualization for analysis. When interaction is tightly and appropriately coupled with visualization, it can transform the visualization from display- ing static imagery to assisting comprehensive analysis of data at all scales. In this relationship, a deeper understanding of the role of interaction, its effects, and how visualization relates to interaction is necessary for designing systems in which the two components complement each other. This thesis approaches interaction in visualization from three different perspectives. First, it considers the cost of maintaining interaction in manipulating visualization of large datasets. Namely, large datasets often require a simplification process for the visualization to maintain interactivity, and this thesis examines how simplification affects the resulting visualization. Secondly, example interactive visual analytical systems are presented to demonstrate how interactivity could be applied in visualization. Specifically, four fully developed systems for four distinct problem domains are discussed to determine the common role of interactivity in these visualizations that make the systems successful. Lastly, this thesis presents evidence that interactions are important for analytical tasks using visualizations. Interaction logs of financial analysts using a visualization were collected, coded, and examined to determine the amount of analysis strategies contained within the interaction logs. The finding supports the benefits of high interactivity in analytical tasks when using a visualization. The example visualizations used to support these three perspectives are diverse in their goals and features. However, they all share similar design guidelines and visualization principles. Based on their characteristics, this thesis groups these visualizations into urban visualization, visual analytical systems, and interaction capturing and discusses them separately in terms of lessons learned and future directions

ClockBoard: A zoning system for urban analysis

Zones are the building blocks of urban analysis. Fields ranging from demo-graphics to transport planning routinely use zones — spatially contiguous areal units thatbreak-up continuous space into discrete chunks — as the foundation for diverse analysistechniques. Key methods such as origin-destination analysis and choropleth mapping relyon zones with appropriate sizes, shapes and coverage. However, existing zoning systemsare sub-optimal in many urban analysis contexts, for three main reasons: 1) administra-tive zoning systems are often based on somewhat arbitrary factors; 2) zoning systems thatare evidence-based (e.g., based on equal population size) are often highly variable in sizeand shape, reducing their utility for inter-city comparison; and 3) official zoning systemsin many places simply do not exist or are unavailable. We set out to develop a flexible,open and scalable solution to these problems. The result is the zonebuilder project (withR, Rust and Python implementations), which was used to create the ClockBoard zoningsystem. ClockBoard consists of 12 segments emanating from a central place and dividedby concentric rings with radii that increase in line with the triangular number sequence(1, 3, 6 km etc). ‘ClockBoards’ thus create a consistent visual frame of reference for mono-centric cities that is reminiscent of clocks and a dartboard. This paper outlines the designand potential uses of the ClockBoard zoning system in the historical context, and discussesfuture avenues for research into the design and assessment of zoning systems

From GeoVisualization to visual-analytics: methodologies and techniques for human-information discourse

2010 - 2011The objective of our research is to give support to decision makers when facing problems which require rapid solutions in spite of the complexity of scenarios under investigation. In order to achieve this goal our studies have been focused on GeoVisualization and GeoVisual Analytics research field, which play a relevant role in this scope, because they exploit results from several disciplines, such as exploratory data analysis and GIScience, to provide expert users with highly interactive tools by which they can both visually synthesize information from large datasets and perform complex analytical tasks. The research we are carrying out along this line is meant to develop software applications capable both to build an immediate overview of a scenario and to explore elements featuring it. To this aim, we are defining methodologies and techniques which embed key aspects from different disciplines, such as augmented reality and location-based services. Their integration is targeted to realize advanced tools where the geographic component role is primary and is meant to contribute to a human-information discourse... [edited by author]X n.s

An Evaluation of Computational Methods to Support the Clinical Management of Chronic Disease Populations

Innovative primary care models that deliver comprehensive primary care to address medical and social needs are an established means of improving health outcomes and reducing healthcare costs among persons living with chronic disease. Care management is one such approach that requires providers to monitor their respective patient panels and intervene on patients requiring care. Health information technology (IT) has been established as a critical component of care management and similar care models. While there exist a plethora of health IT systems for facilitating primary care, there is limited research on their ability to support care management and its emphasis on monitoring panels of patients with complex needs. In this dissertation, I advance the understanding of how computational methods can better support clinicians delivering care management, and use the management of human immunodeficiency virus (HIV) as an example scenario of use. The research described herein is segmented into 3 aims; the first was to understand the processes and barriers associated with care management and assess whether existing IT can support clinicians in this domain. The second and third aim focused on informing potential solutions to the technological shortcomings identified in the first aim. In the studies of the first aim, I conducted interviews and observations in two HIV primary care programs and analyzed the data generated to create a conceptual framework of population monitoring and identify challenges faced by clinicians in delivering care management. In the studies of the second aim, I used computational methods to advance the science of extracting from the patient record social and behavioral determinants of health (SBDH), which are not easily accessible to clinicians and represent an important barrier to care management. In the third aim, I conducted a controlled experimental evaluation to assess whether data visualization can improve clinician’s ability to maintain awareness of their patient panels