Graduated Students


PhD Students

Nicholas Anderson
2007

Thesis/Dissertation Title

Evaluating Experimental Information Management in Biomedical Research: A Case-Study Approach

Data intensive biomedical research is increasingly integrative; knowledge gained from a spectrum of disciplines and tools is generated, collected and applied to aid in the analysis and description of biological phenomena. Though there are many evolving approaches and much effort to bring a synthesis of disciplines to biological research, we have little understanding of how researchers are coping with the longitudinal experimental data management challenges involved with day-to-day experimental work.
This research uses multiple theoretical frameworks and data collection methods to identify issues involved in the use of information-rich research tools and techniques by academic laboratory research groups. Experience from a broad evaluation of common information management issues affecting the local biomedical research community was used to inform a case study protocol for the study of information technology in laboratory settings. This protocol was then used to design a focused case study of microarray gene expression analysis (MGEA) information use and workflow in academic laboratories. MGEA is a methodology that due to its large generated raw data sets, expensive measurement equipment and complex analysis procedures requires collaboration with specialists in biostatistics and bioinformatics to aid researchers in effective inquiry. As such, the academic use of MGEA methodologies is a representative example of information management challenges that are necessary to provide integrative biomedical research support. This case study approach was then used to evaluate the utility and transferability of the protocol to other laboratory information management issues.
This work seeks to explore two fundamental issues: The first is the development of methods to capture the full complexity and cost of planning, collaboration and analysis needed to complete data-rich academic biomedical experiments. The second is to use these methods to explore the use of a representative technology, and to assess the degree to which an exploratory case-study approach can serve to inform bioinformatics design, implementation and support.

Last Known Position

Associate Professor and Division Head, Health Informatics, UC Davis


Alan Au
2013

Thesis/Dissertation Title

What Difference Does a Form Make: Redesign and Evaluation of a Form for Documenting In-Hospital Cardiac Arrest

The real-time documentation of medications and procedures is an essential part of managing patient care during in-hospital "code blue" cardiac arrest emergencies. Care providers have voiced dissatisfaction with the existing code blue documentation form. To address this problem, a mixed-methods needs assessment was used to describe the problems of usability and completeness. Based on the results, the documentation form was redesigned and then assessed through an evaluation study.

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Last Known Position

Senior Fellow, University of Washington


Noah Benson
2010

Thesis/Dissertation Title

Computational Methods for the Analysis of Molecular Dynamics Simulations

Proteins are macromolecules that are involved in virtually every biological process and structure. The three-dimensional structure of these molecules is extremely important as a window into how they work but is extremely difficult to predict, as direct observation of their motion and the folding pathway is possible only through very limited experimental techniques. Nonetheless, observing protein structure alone has proven insufficient for understanding how proteins fold or behave natively. Molecular dynamics (MD) is a computational technique by which protein dynamics can be examined at resolutions well beyond the capabilities of experiment. The decrease in cost of computer resources have lead biologists to turn to MD more frequently in recent years, yet MD simulations produce data in quantity and complexity well beyond the capabilities of conventional biological analysis techniques. We have curated a database of protein native-state and thermal unfolding simulations, which is the largest database of unfolding simulations to date. We examine this database using two existing and three novel analysis methods and demonstrate the utility of each for high throughput analysis. Finally, we demonstrate that these methods can be used to generate and support novel hypotheses concerning protein motion.

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Last Known Position

Postdoctoral Fellow, University of Pennslyvania


Wynona Black
2013

Thesis/Dissertation Title

Temporal Data Mining in Electronic Medical Records from Patients with Acute Coronary Syndrome

Every 25 seconds someone in the US has cardiac event and one person per minute will die from it. ST-elevated myocardial infarction (STEMI), non ST-elevated myocardial infarction and unstable angina are caused by ischemia and referred to as acute coronary syndrome (ACS). STEMI is the most severe and accounts a quarter of ACS cases. There is substantial research in STEMI treatment that focuses on a single event and the risks/benefits thereof. The interaction between events during an encounter is especially important in STEMI, where the timing of treatments is crucial for positive patient outcomes. However there is a dearth of research into the relationship between events.
To explore the temporal relationships, I created a sequential pattern mining algorithm (SPM) and a temporal association rule mining algorithm (TARM) to mine the Acute Coronary Syndrome Patient Database (ACSPD). The ACSPD is a very large, 9-year EMR database derived from 128 health care institutions across the US. The SPM is well-suited to extract patterns from noisy data. The TARM is designed to discover rules comprised of 3 temporally ordered events, i.e. clinical practice patterns (CPP).
Using the SPM in the ACSPD, I discovered 39 order sets. Not all order sets are present for the 9 year span and overall order set use drops precipitously in 2004. I postulate that this denotes a shift in medical practice. The cause is unknown, but in late 2004, the American Heart Association (AHA) published new STEMI treatment guidelines. I condensed the ACSPD sequences using the order sets then applied the TARM. Using support, confidence, lift, likelihood, and Zhang’s, I found substantial variation, rarity and weak antecedent-consequent pairing in the CPPs. To explore the interaction between clinical decisions and patient outcomes, I compared the CPPs with AHA STEMI performance measures for compliance and analyzed the risk of bleeding and mortality. CPP compliance with performance measures decreases mortality and bleeding risk, but there is evidence of complex interactions between measures that augments or masks the effect. The contributions of this work are 1) exploring CPPs and their effect on patient outcomes and 2) the novel combination of sequential and temporal association rule mining in EMR data.

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Last Known Position

Senior Analyst, Oracle


Richard Boyce
2008

Thesis/Dissertation Title

An Evidential Knowledge Representation for Drug-mechanisms and its Application to Drug Safety

A major challenge to designers of informatics tools that help alert clinicians to potential drug-drug interactions (DDIs) is how to best assist clinicians when they must infer the potential risk of an adverse event between medication combinations that have not been studied together in a clinical trial. The central thesis of this dissertation is that DDI prediction using drug mechanism knowledge can help drug-interaction knowledge bases expand their coverage beyond what has been tested in clinical trials while avoiding prediction errors that occur when individual drug differences are not recognized. This dissertation describes a knowledge representation system, called the Drug Interaction Knowledge Base (DIKB), that uses a novel approach to linking and assessing evidence support for drug-mechanism assertions.

The DIKB is the first knowledge-representation system we are aware of to use a computable model of evidence and a Truth Maintenance System to manage assertions in its knowledge-base. The novel approach to evidence management implemented in the DIKB enables its prediction accuracy and coverage to be optimized to a particular body of evidence; a feature that is very desirable for clinical decision support. The DIKB is also novel for its computable representation of the conjectures behind a specific application of evidence. These evidence-use assumptions enable the system to flag when a conjecture has become invalid and alert knowledge-base maintainers to the need to reassess their original interpretation of what assertions a piece of evidence supports. They are also used as evidence is input into the system to help identify a pattern, called a circular line of evidence support, that is indicative of fallacious reasoning by evidence-base curators. The DIKB has been shown capable of accurately predicting clinically-relevant DDIs using only pharmacokinetic drug-mechanism knowledge and development of the system has helped to identify and evaluate potential informatics solutions to the challenges of representing, synthesizing, and maintaining drug mechanism knowledge.

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Last Known Position

Associate Professor, Biomedical Informatics, University of Pittsburgh


Dennis Bromley
2014

Thesis/Dissertation Title

Visual Analytics Methods for Analyzing Molecular Dynamics Simulations of Mutant Proteins

The structural dynamics of proteins are integral to protein function; if these structural dynamics are altered by mutation, the function of the protein can be altered as well, potentially resulting in disease. Experimental structure-determination with x-ray crystallography and Nuclear Magnetic Resonance (NMR) can be useful in determining mutant protein structures, but detailed, high-resolution dynamics data can be difficult to ascertain. Molecular Dynamics (MD) simulation is a high temporal- and spatial-resolution in silico method for dynamic protein structure determination. Unfortunately, the data generated by MD simulations can be too large for standard analysis tools. Here I describe a novel visual-analytics tool called DIVE that was specifically created to handle large, structured datasets like those generated by MD simulations. Using DIVE, I analyzed MD simulation-data of disease-associated mutations to the α-Tocopherol Transfer Protein (α-TTP) and to the p53 tumor suppressor protein. In addition to mutant structural-analysis and characterization, I also used DIVE to develop an algorithm for identifying regions of mutant proteins that are amenable to ‘rescue’, or ligand-mediated stabilization that can suppress the destabilizing effect of mutations. The results of these investigations highlight the utility of big-data, visual-analytics approaches to exploring MD simulation data.

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Eithon Cadag
2009

Thesis/Dissertation Title

Automated Learning of Protein Involvement in Pathogenesis Using Integrated Queries

Methods of weakening and attenuating pathogens' abilities to infect and propagate in a host, and thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories. A lightweight method for data integration is introduced, which links information regarding a protein via a path-based query graph and supports both exploratory and logical queries; data gathered in this way is characterized with experiments on retrieving high-quality annotation data. A system and method of weighting is then applied to query graphs that can serve as input to various statistical classification methods for discrimination, and the combined usage of both data integration and learning methods are leveraged against the problem of generalized and specific virulence function prediction. This approach improves coverage of functional data over a protein, outperforms other recent approaches to identification of virulence factors, is robust to different weighting schemes of varying complexity and is found to generalize well to traditional function prediction.

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Last Known Position

Principal Data Scientist, Ayasdi Inc.


Daniel Capurro
2012

Thesis/Dissertation Title

Secondary Use of Clinical Data: Barriers, Facilitators and a Proposed Solution

The increasing adoption of electronic medical records is producing a massive accumulation of routinely collected electronic clinical data (ECD). This data can be used not only for direct patient care but for secondary purposes such as clinical research, quality improvement and public health. However, using clinical data collected for one purpose does not render it usable for secondary purposes. This dissertation seeks to explore (1) whether ECD is fit for use for research purposes, (2) the barriers and facilitators to secondary use faced by clinical researchers, and (3) to propose a solution to help address one of the barriers identified. To do this, this dissertation is composed of three different but interrelated studies. The first one consists of a Delphi process to develop a tool to systematically assess the fitness for use of ECD for research and its subsequent application on a set of clinical data requests. The second study is a qualitative inquiry into the barriers and facilitators to secondary use of clinical data experienced by researchers at the University of Washington, Group Health Research Institute and the Veterans Affairs' Northwest Center for Outcomes Research in Older Adults. The third study describes the development of a system to query clinical relational databased based on temporal abstractions and patterns, which should enable researchers to identify high-level concepts from clinical databases. The results of this dissertation should allow us to improve the reutilization of ECD for research purposes.

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Last Known Position

Assistant Professor, Department of Internal Medicine, Pontificia Universidad Católica de Chile; Affiliate Assistant Professor UW Department of Biomedical Informatics and Medical Education


Shomir Chaudhuri
2015

Thesis/Dissertation Title

Examining the Feasibility and Acceptability of a Fall Detection Device

Falls are a very complex challenge for older adults and our health care system. They are especially dangerous when the fallen individual is unable to get up from a fall independently. This “long lie” has been shown to be almost as damaging as the fall itself and has the ability to affect not only the fallen individual’s physical health but also their mental health. Current technology designed to detect these falls are often inappropriately designed for the older adult population and are improperly used if at all.

This dissertation includes three studies that cover various aspects of older adults’ use of fall detection technology. The first study is a systematic review which assesses the current state of design and implementation of fall detection devices. The second study seeks to more clearly understand older adults’ perceptions of fall detection technology using focus groups. The third study is a feasibility study investigating the usability of a wearable fall detection device that employs innovative GPS and automatic detection technologies. I will go over the results of these studies and identify challenges associated with these devices and provide design recommendations for improving these devices.

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Melissa Clarkson
2014

Thesis/Dissertation Title

Visual Analytics Methods for Analyzing Molecular Dynamics Simulations of Mutant Proteins

Ontologies have become increasingly important for both representation of biomedical knowledge and for using that knowledge to facilitate data integration. However, ontologies are generally not presented in ways that are easy for users to comprehend, which limits their use. In this work I address this problem within the context of two spatially-oriented ontologies: the Foundational Model of Anatomy (FMA) and the Ontology of Craniofacial Development and Malformation (OCDM). I describe an approach to communicating these ontologies that involves (1) identifying content patterns within an ontology, (2) creating a simplified tutorial to explain basic concepts within the ontology, (3) involving potential users in the design of an ontology browser interface, and (4) creating graphics to support the process of building and communicating the ontology. This approach should be applicable to any spatially-oriented ontology, and should result in visualizations that will enhance understanding of ontologies.

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Last Known Position

Assistant Professor, University of Kentucky, Division of Biomedical Informatics


Walter H. Curioso
2012

Thesis/Dissertation Title

Evaluation of a Computer-Based System using Cell Phones for HIV positive people in Peru

HIV is one of the biggest infectious killers worldwide. To prevent disease progression and avoid development of resistant strains to HIV, people living with HIV must adhere to complicated antiretroviral therapy (ART). Yet, in Peru, where ART has recently been introduced, adherence to HIV treatment has not yet been addressed properly, and no systematic approaches to evaluate or promote adherence to ART exist. For people living with HIV, innovative approaches using information technologies, such as mobile phones, are needed to increase adherence to ART. In my thesis, I proposed the following specific aims: (1) To conduct formative research to assess culturally-specific behavioral messages to be included in the computer-based system; (2) To develop and test an interactive computer-based system using cell phones both to enhance adherence to ART and to deliver HIV transmission risk reduction messages; and (3) To evaluate the impact of the system on ART adherence. To achieve these aims, I conducted a randomized controlled trial of a 12-month intervention, comparing (1) standard-of-care with (2) standard-of-care plus my mobile phone-based system among patients receiving ART at Via Libre, a non-governmental organization established to help people with HIV, and Hospital Nacional Cayetano Heredia, a governmental hospital; both in Lima, Peru. This novel trial adds important evidence to the field of mHealth—the provision of health-related services via mobile communications. The trial is potentially scalable as a prevention strategy by the Ministry of Health, and the results could be applied in other settings, not only for ART, but also to encourage patients to follow long-term treatment plans for other chronic diseases. Furthermore, because the intervention is automated using available information and communication technology, it can be scaled up widely without requiring proportionate and expensive staff resources.

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Last Known Position

Assistant Professor, Department of Internal Medicine, Pontificia Universidad Católica de Chile; Affiliate Assistant Professor UW Department of Biomedical Informatics and Medical Education


Jon DeShazo
2009

Thesis/Dissertation Title

Automated Analysis and Surveillance for STI / HIV Online Behavior

An automated algorithm has been developed to extract health related information from free text. This interactive expert system is one of the first to automatically extract characteristics related to STI/HIV prevention from online sex-seeking discourse. In addition to the design of a novel fully supervised expert system, this research is focused on the contextual evaluation of a new type of information for STI/HIV prevention activities.

Last Known Position

Assistant Professor, Department of Health Administration, Virginia Commonwealth University


Paul Fearn
2016

Thesis/Dissertation Title

Last Known Position

Chief of the Surveillance Informatics Branch, Surveillance Research Program (SRP) at the National Cancer Institute, Division of Cancer Control and Population Science


Michal Galdzicki
2012

Thesis/Dissertation Title

The Synthetic Biology Open Language: A Data Exchange Standard for Biological Engineering

Synthetic biology is the emerging research and engineering field concerned with the design and construction of new biological functions and systems. Synthetic biologists are engineering organisms to solve outstanding problems in medicine, bio-energy, environmental health, and nutrition. Their goal is to improve the biological engineering process by applying standardization, decoupling, and abstraction. To more efficiently engineer gene circuits synthetic biologists need software tools that support standardized data exchange.

For my dissertation research I led the development and deployment of the Synthetic Biology Open Language (SBOL). In this dissertation, I present the SBOL community, the specification, and demonstrations of its use. The SBOL community is supported by stakeholders from the synthetic biology software community. The SBOL Core specifies the vocabulary, data model, and format to define the standard. I describe SBOL Core as a common representation for synthetic biology designs capable of describing theoretical DNA component designs; annotated DNA sequence; and collections of components. To aid the exchange synthetic biological designs among software tools I explain the software libraries which support the implementation of SBOL. Then, I illustrate the recognition of its value and acceptance by the stakeholders through the deployment of the technology at collaborating sites. Finally, I show how the choice of Semantic Web technology to facilitate the information exchange between software can also be used for information retrieval to improve the selection of DNA components in new designs. Through this work I contribute to the development of informatics standards a computational infrastructure to enable a rapid biological engineering process for biotechnology.

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Last Known Position

Scientist, Arzeda Corporation; Chief Science Officer, HiveBio Community Lab


Nikhil Gopal
2017

Thesis/Dissertation Title

On Biological Network Visualization: Understanding Challenges, Measuring the Status Quo, and Estimating Saliency of Visual Attributes

Biomedical research increasingly relies on the analysis and visualization of a wide range of collected data. However, for certain research questions, such as those based on the interconnectedness of biological elements, the sheer quantity, complexity, and variety of data may result in rather large and dense networks, rendering them visually uninterpretable. Since networks are important models in biomedicine, and since visualization is a valuable form of analysis, it stands to reason that the biomedical community may benefit from improvements to network visualization.

My dissertation focuses on the following three studies. First, I cover a semi-structured interview study aimed at uncovering the challenges researchers face while analyzing and visualizing biological networks. Second, I describe a systematic review aimed at characterizing visual attributes and assessing the ability to complete selected graph tasks in figures containing node-link diagrams obtained from peer-reviewed bioinformatics literature. Furthermore, I explain the Information Triad, a small conceptual framework I developed to reason about network visualization research questions, followed by a description of visual encoding exploration software I implemented based on the framework. Finally, I detail the design and execution of a task-centered perception study, where the saliency of several visual attributes were estimated as functions for the task of visually scanning a network.

Through these studies, I contributed to the understanding of network-related visualization challenges encountered by researchers, showed that graph figures in bioinformatics literature may be designed for varying purposes, developed a conceptual framework for reasoning about network visualization, built visual encoding software that supports systematic and reproducible explorations of the visual encoding set space, and finally, obtained an estimate of how numerous visual encodings are related to one’s ability to visually scan a network.

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Last Known Position

Insight Data Science Fellow


Alicia Guidry
2013

Thesis/Dissertation Title

Ontology Based Data Integration of Open Source Electronic Medical Record and Data Capture Systems

n low-resource settings, the prioritization of clinical care funding is often determined by immediate health priorities. As a result, investment directed towards the development of standards for clinical data representation and exchange are rare and accordingly, data management systems are often redundant. Open-source systems such as OpenMRS and OpenClinica provide an opportunity to leverage available systems to improve standards and increase interoperability. Nevertheless, continuity of care and data sharing between these systems remains a challenge, particularly in populations with changing health needs, and inconsistent access to health resources.
The overarching goal of this project is to enable sharing of data across low cost systems like OpenMRS and OpenClinica using ontologies. The project consists of three aims: 1) describing clinical research and visit data related to the treatment and care of HIV/AIDS patients, 2) developing a prototype data integration system between electronic medical record and electronic data capture systems, and 3) evaluating the utility of the prototype system using simulated and real-world data. In the first aim, I developed a patient identifier and a HIV/AIDS treatment and care ontology to represent the types of data and information created and used by clinicians. This was achieved by gathering data forms used in HIV/AIDS clinics in low-resource settings. From these forms, the patient identifier and HIV/AIDS variables were extracted and used to create the ontologies. In aim 2, the ontologies from aim 1, along with simulated data, were used to develop a prototype data integration system that improves the ability of developers to implement integration systems that meet the needs of users, based on previously created use cases. In the third aim, I evaluated whether the matching algorithm used in the prototype can correctly identify matching patients, and whether the prototype is generalizable to clinical care and research data collected in a real world setting.
This work contributes two ontologies to the medical and public health fields that are useful in providing standardization of data elements. Additionally, I provide a prototype data integration system that is useful in facilitating access to previously siloed data and helps reduce the burden of integrating future systems.

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Last Known Position

Software Developer, University of Louisiana at Lafayette and the Louisiana Department of Health and Hospitals


Andrea Hartzler Civan
2009

Thesis/Dissertation Title

Understanding and Facilitating Patient Expertise Sharing

A fundamental part of becoming an empowered patient is learning to engage in the day-to-day management of personal health. Yet learning to manage personal health can take substantial time and effort when patients do so through trial and error on their own. Although health informatics support has the potential to help patients overcome this challenge by facilitating patient expertise sharing, we lack the knowledge necessary to meet this potential. Prior work provides little clarity about the nature of patients' personal health expertise and has not explored the practices patients use to leverage this experiential knowledge offered by other patients in similar situations. This dissertation contributes foundational knowledge about what patient expertise is and how patients share this valuable resource. Within the context of breast cancer, I (1) describe the characteristics of patient expertise through a comparative content analysis that demonstrates how this unique form of knowledge significantly differs from the expertise obtained from health professionals in topic, form, and style, (2) describe practices patients use to share their expertise in their everyday lives during cancer treatment through a naturalistic field study, and (3) employ a user-centered approach, informed by specific design recommendations I propose for enhancing health-related social software, to design a patient expertise locator to facilitate patient expertise sharing. This work provides substantial guidance on new ways to think about the design of supportive tools for patients. Patients need help from peers and this work provides the understanding and guidance necessary to empower patients by facilitating patient expertise sharing.

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Last Known Position

Assistant Investigator Group Health Research Institute


Rebecca Hazen
2016

Thesis/Dissertation Title

Designing and Evaluating a Patient-Driven Application for Patients with Primary Brain Tumors

Primary brain tumors are a complex and challenging disease. These tumors are rare and difficult to treat, and result in a significant burden on patients and their families. These patients will experience a wide range of neurological symptoms, as well as deficits and declines in cognitive and functional abilities as they progress through the disease and treatment process. For these patients, prognosis is often poor, as recurrence is common and complete cure for most malignant brain tumors is typically not possible.

From the time of diagnosis through treatment and follow-up, patients with primary brain tumors and their caregivers face many challenges and uncertainties as they navigate the healthcare environment and take on new roles and responsibilities in the care process. Despite a recent increase in the use of personal technologies to support health-related activities, there are very few tools and technologies currently available to support the unique needs of this patient population. There has been little research conducted to study the role of technology in health and daily life for these individuals, and to explore the potential for future design and development to reflect the needs and abilities of this small and challenging patient population. These gaps represent an opportunity for research and design, leveraging the insights and experiences of current patients and caregivers in informing the design of tools and technologies to support future patients and caregivers.

In this dissertation, I investigated the experiences, challenges, and needs of patients with primary brain tumors and their caregivers and in working towards designing and developing tools and technologies to address needs surrounding tracking, understanding, managing, and communicating symptom, side effect, and other health information. I engaged patients, caregivers, and clinicians in semi-structured interviews to build an in-depth understanding of the current situation, and worked alongside patients and caregivers as partners in designing a prototype of a brain tumor specific smartphone and tablet application. I then evaluated the resulting high-fidelity prototype with patients, caregivers, and clinicians to explore functionality and usability, and further understanding of how this tool could be implemented and used to support these and future users throughout treatment and follow-up.

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Rebecca Hills
2011

Thesis/Dissertation Title

Information Needs and the Characteristics of Population Data Sources: An Immunization Information System Case Study

Data and information are vital to the daily work of public health practitioners and the data they use come from a variety of sources. Examples of these data sources are vital statistics databases, surveillance data, morbidity data, and Immunization Information Systems (IISs). These IISs are of particular interest because of their near ubiquity in the Unites States, their importance for public health practice, and their most basic function of providing cross-organizational access to immunization-related clinical data for both public and private health care providers. As the infrastructure to connect electronic health record (EHR) systems and public health systems expands, public health practitioners will have the opportunity to access an unprecedented volume of patient level clinical information. The flood of information and data will have the greatest public health impact if understood and organized within the framework of public health practitioners' data and information needs. This work uses qualitative methods to identify and understand the information needs of public health practitioners related to immunization work and the data and information source characteristics that are important in meeting those needs. This study also uses quantitative methods to describe two important data source characteristics in Washington’s IIS: timeliness and data element completeness. Results point to three main types of information needs of public health practitioners: individual level, population level and context-specific information (vaccine-specific information in this case). These results further the understanding of information work in public health across local and state public health organizations. These results also provide solid evidence related to the effect of different methods of data transfer on data quality. In addition, synthesis of the qualitative and quantitative components provides evidence to support a set of recommendations presented to state level stakeholders in Washington. This research will help inform the development of technical and non-technical infrastructure to support data sharing between healthcare providers, health information exchanges, and public health organizations.

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Last Known Position

Clinical Assistant Professor, Biobehavioral Nursing and Health Systems, University of Washington; Research Coordinator, Northwest Center for Public Health Practice


Jonathan Joe
2015

Thesis/Dissertation Title

Designing Wellness Tools for and with Older Adults

ver the past few decades, the use of new technologies such as computing and internet technology, has expanded rapidly. The emergence of these new technologies has created opportunities for health related uses. With the growing older adult population, there has been increased interest in using tools to support aging, health, and wellness of the older adult population. While technologies have been used with older adults for purposes such as symptom management and cognitive training, many technologies are not designed with older adults in mind. While there have been some studies that look at the usability of a single component, there have been few studies looking at a technology platform that integrates several features together. Designing specifically for older adults is important since this population has its own unique health and information needs.

In my talk, I will present my work in exploring the wants and needs of older adults for integrated health and wellness tools. I will discuss the three phases of my dissertation work including the results of focus groups seeking to understand the attitudes and preferences towards a multifunctional wellness tool, the usability issues of a popular, commercially available wellness tool, and the reactions and feedback of older adults to scenarios and storyboards showing design ideas generated after the first two phases. Results from these studies help to better understand older adults’ perceptions, attitudes and issues with potential wellness tools and inform the design of new effective and efficient systems for older adults.

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Alan Kalet
2015

Thesis/Dissertation Title

Bayesian Networks from Ontological Formalisms in Radiation Oncology

Bayesian networks (BNs) are compact, powerful representations of probabilistic knowledge well suited to applications of reasoning under uncertainty in medical domains. Traditional development of BN topology requires that modeling experts establish relevant dependency links between domain concepts by searching and translating published literature, querying domain experts, or applying machine learning algorithms on data. For initial network development, these methods are time-intensive, and this cost hinders the growth of BN applications in medical decision making. In addition, they result in networks with inconsistent and incompatible topologies, and these characteristics make it difficult for researchers to update old BNs with new knowledge, to merge BNs that share concepts, or to explore the space of possible BN models in any simple intuitive way.

My research alleviates the challenges surrounding BN modeling by leveraging a hub and spoke system for BN construction. I implement the hub and spoke system by developing 1) an ontology of knowledge in radiation oncology (the hub) which includes dependency semantics similar to BN relations and 2) a software tool that operates on ontological semantics using deductive reasoning to create BN topologies. I demonstrate that network topologies built using my software are terminologically consistent and topologically compatible by updating a BN model for prostate cancer prediction with new knowledge, exploring the space of other dependent concepts surrounding prostate cancer radiotherapy, and merging the updated BN with a different prostate cancer BN containing cross terms with the original model. I also produce a BN to aid in error detection in radiation oncology, showing the extent to which Bayes nets are clinically impactful. Moreover, I show that the methodology developed in this research is applicable to medical domains outside radiation oncology by extracting a BN from a description logic version of the Disease Ontology.

By translating medical domain literature into ontological formalisms and developing a software tool to operate on those formalisms, I establish a novel, feasible, and useful methodology that advances and improves the creation of clinically viable Bayesian network models. In sum, my research represents a foundational component of a larger framework of automation and innovation that contributes to further application of BNs in medical decision support roles.

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Last Known Position

Assistant Professor, Department of Radiation Oncology, University of Washington


Irma Lam
2014

Thesis/Dissertation Title

Feature Engineering for 3D Medical Image Applications

eature engineering, including input representation, feature design, evaluation, and optimization, is essential to success in machine learning. For unstructured data like images and texts, feature engineering can often become the bottleneck in learning related tasks. Selecting the most effective and descriptive features can improve performance, proficiency, and precision in quantification applications, or enhance a good classifier in classification. Features are domain-specific, therefore, deciding what features to use and optimizing the design so that features can express input explicitly, automatically, fully, yet intuitively often require substantial knowledge of the applications and the nature of the input. This thesis introduces a new set of feature engineering algorithms for medical research of 3D CT skull images in understanding craniosynostosis disorder. Three related tasks: 1) classification, 2) severity assessment and class ranking, and 3) pre-post surgery change are used to demonstrate the effectiveness of the features and the algorithms that produce them.

Craniosynostosis, a disorder in which one or more fibrous joints of the skull fuse prematurely, causes skull deformity and is associated with increased intracranial pressure and developmental delays. In order to perform medical research studies that relate phenotypic abnormalities to outcomes such as cognitive ability or results of surgery, biomedical researchers need an automated methodology for quantifying the degree of abnormality of the disorder. While several papers have attempted this quantification through statistical models, the methods have not been intuitive to biomedical researchers and clinicians who want to use them. The goal of this work was to develop a general set of features upon which new quantification measures could be developed and tested. The features reported in this study were developed as basic shape measures, both single-valued and vector-valued, that are extracted from a projection-based plane of the 3D skull. This technique allows us to process images that would otherwise be eliminated in previous systems due to poor resolution, noise or imperfections on their original older CT scans.
We test our new features on classification tasks and also compare their performance to previous research. In spite of their simplicity, the classification accuracy of our new features is significantly higher than previous results on head CT scan data from the same research studies.
We propose a set of features derived from CT scans of the skull that can be used to quantify the degree of abnormality of the disorder. A thorough set of experiments is used to evaluate the features as compared to two human craniofacial experts in a ranking evaluation.
We study pre-post surgery change based on selected features we use in quantifying the severity of deformity of the disorder. Using the same selected features, we also compare and contrast post-surgery craniosynostosis skulls to the unaffected class.

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Amanda Lazar
2015

Thesis/Dissertation Title

Using Technology to Engage People with Dementia in Recreational Activities

Dementia is estimated to currently affect almost 15% of US adults over the age of 70. As the population ages, the prevalence of dementia will increase proportionally. The increase in the number of people with dementia will create a corresponding increase in health services required. Structured activities are extremely important in this population, leading to greater well-being and greater positive affect during activities and long term effects such as delayed progression of cognitive impairments. Despite the importance of activities in dementia care, many people with dementia living outside of the community are lacking opportunities for sustained social interactions and stimulating activities. There is a clear unmet need for stimulating activities that do not place an additional financial or time burden on staff or families. Technology is a promising venue to engage people with dementia in activities. For example, technology can be used to deliver rich multimedia and standardized interventions, utilize digital archives increasing their accessibility to many, engage people in remote care or contact with loved ones, and monitor and log changes in use of the system.

In my dissertation, I examine the ways technology can support older adults with dementia in engaging in activities in a memory care unit. I discuss existing technologies that support this population in engaging in activities, a six month field deployment of an existing technology, and recommendations that have been validated with experts in the field of gerontology and human computer interaction. My dissertation furthers our understanding of how to design engaging technologies for older adults with dementia to promote meaningful participation in recreational and leisure activities.

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Thai Le
2014

Thesis/Dissertation Title

Design and Evaluation of Health Visualizations for Older Adults

The older adult population is one of the fastest growing demographic groups in the United States. Associated with this aging population are changes in health and wellness. Smart home technologies can be a valuable resource to support older adults in maintaining independence while encouraging engagement in care. To present data collected from home based monitoring including telehealth, smart homes, and other informatics tools in a meaningful manner, I describe work in the development of health visualizations for older adults. Though a body of work has shown that older adults find utility in technology to support their health and wellness, there has been limited research examining how this would translate to data visualizations. I start by looking at potential differences in how older adults process graphical information compared to the general population through a set psychophysics experiments. I then apply a user-centered design approach to iterate on health visualizations from early mockups to fully interactive prototypes. I describe different approaches for evaluating visualizations with older adults, and report on the findings of the evaluations. This work highlights key issues for how older adults use health visualizations. Based on these evaluations, I also provide a set of design guidelines when designing health visualizations for older adults.

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Eunjung Sally Lee
2008

Thesis/Dissertation Title

Supporting Multi-institutional, Interdisciplinary Biomedical Collaboration (MIBC): A Biomedical Informatics Approach

The modern biomedical research community is facing ever more challenging research questions. Out of necessity, biomedical research has become increasingly interdisciplinary and large-scale in nature. Yet large-scale interdisciplinary biomedical collaborations are not easily established or maintained. Many funding agencies identify biomedical informatics as an important foundation to support biomedical collaboration to alleviate some of the challenges large-scale interdisciplinary collaborations face. However, biomedical informatics has yet to understand in detail how large-scale interdisciplinary biomedical collaborations operate and deal with day-to-day challenges associated with collaboration.
This research used contextual field study to describe the characteristics of large-scale interdisciplinary biomedical collaboration in-depth and to identify barriers, existing facilitators, and needs associated with various collaborative processes. The study result was synthesized to develop a context-specific informatics framework to support large-scale interdisciplinary biomedical collaboration that extends prior research of collaboration in other fields. In the future, the framework can be used as a guide for design and evaluation of collaborative infrastructure.

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Last Known Position

Biomedical Informatics Consultant, Institute of Translational Health Sciences (ITHS), University of Washington


Chia-Ju Lee
2016

Thesis/Dissertation Title

A Knowledge-based System for Intelligent Support in Pharmacogenomics Evidence Assessment: Ontology-driven Evidence Representation, Retrieval, Classification and Interpretation

A Knowledge-based System for Intelligent Support in Pharmacogenomics Evidence Assessment: Ontology-driven Evidence Representation, Retrieval, Classification and Interpretation
Abstract: Pharmacogenomics is the study of how genetic variants affect a person’s response to a drug. With great advances to date, pharmacogenomics holds promise as one of the approaches to precision medicine. Yet, the use of pharmacogenomics in routine clinical care is minimal, partly due to the misperception that there is insufficient evidence to determine the value of pharmacogenomics and the lack of efficient and effective use of already existing evidence. Enormous efforts have been directed to develop pharmacogenomics knowledge bases; however, none of them fulfills the functionality of providing effective and efficient evidence assessment that supports decisions on adoption of pharmacogenomics in clinical care.

In this context, my overall hypothesis was that a knowledge-based system that fulfills three critical features, including clinically relevant evidence, providing an evidence-based approach, and using semantically computable formalism, could facilitate effective and efficient evidence assessment to support decisions on adoption of pharmacogenomics in clinical care. My overarching research question has been: How can we exploit state-of-the-art knowledge representation and reasoning in developing a knowledge-based system with the intended features and applications as specified above.

The first aim of this research was to develop a conceptual model to address the information needs and heterogeneity problem for the domain of pharmacogenomics evidence assessment. Faceted analysis and fine-grained characterization of clinically relevant evidence acquired from empirical pharmacogenomics studies were deployed to identify 3 information entities, 9 information components, 30 concepts, 49 relations and approximately 250 terms as building blocks of the conceptual model. These building blocks were then organized into a model, which features a layered and modular structure so that heterogeneous information content of pharmacogenomics evidence could be expressed to reflect its intended meaning. The developed conceptual model was validated against a general ontology of clinical research (OCRe) to show its strength in modeling pharmacogenomics publications, studies and evidence in an extensible and easy-to-understand way.

The second aim of this research was to exploit OWL 2 DL to build a knowledge-based system that enables formal representation and automatic retrieval of pharmacogenomics evidence for systematic review with meta-analysis. The conceptual model developed in Aim 1 was encoded into an OWL 2 DL ontology using Protégé. The constructed ontology provides approximately 400 formalized vocabularies, which were used in turn to formally represent 73 individual publications, 82 individual studies and 445 individual pieces of evidence, and thereafter formed a knowledge base. After a series of subsumption checking and instance checking using HermiT reasoner, the implemented knowledge-based system was verified as consistent and correct.

The third aim of this research was to use the implemented knowledge-based system to provide four applications in pharmacogenomics evidence assessment. The first application focused on the ontology-driven evidence retrieval for meta-analysis. A total of 33 meta-analyses selected from 9 existing systematic reviews were used as test cases. The results showed that the ontology-based approach achieved a 100% precision of evidence retrieval in a very short time, ranged from 9 to 23 seconds. The second application addressed the evidence assessment of the clinical validity of CYP2C19 loss-of-function variants in predicting efficacy of clopidogrel therapy. The third application addressed the evidence assessment of the comparative effectiveness of genotype-guided versus non-genotype-guided warfarin therapy. These two applications focused on ontology-driven evidence classification to provide useful information to assist in the planning, execution, and reporting of a multitude of meta-analyses. The fourth application focused on ontology-driven interpretation of a multitude of synthesized evidence that was enabled by formal representation of synthesized evidence and typology of clinical significance in the context of assessing clinical validity and clinical utility of pharmacogenomics.

In conclusion, the major contributions of this research include: deriving an extensible conceptual model that expresses heterogeneous information content, constructing an ontology that exploits the advanced features of OWL 2 DL, and implementing a knowledge-based system that supports ontology-driven evidence retrieval, classification and interpretation. Future research would focus on (1) enhancing the system’s applicability in pharmacogenomics evidence assessment by representing evidence of other sub-domains of pharmacogenomics such as cancer drugs, and (2) expanding the system’s capability beyond pharmacogenomics evidence assessment by representing individuals’ genomic profiles and providing evidence-based interpretation based on their individual genomic profiles. With the enhanced applicability, the pharmacogenomics knowledge-based system might improve pharmacogenomics evidence assessment as well as evidence-based interpretation of pharmacogenomics at the point of care, and ultimately increase the adoption of pharmacogenomics in routine clinical care

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Hao (Maya) Li
2011

Thesis/Dissertation Title

A Model Driven Laboratory Information Management System

Biomedical research scientists need more robust tools than spreadsheets to manage their data. However, no suitable laboratory information management systems (LIMS) are readily available; they are either too costly to build or too complex to adapt. This thesis presents the architecture, design, implementation, and a prototype of a model driven LIMS, called Seedpod. Scientists, with the help of biomedical informaticists, develop a knowledge model of their data and data management needs in Protégé. Seedpod then automatically produces a relational database from the model, and dynamically generates a web-based graphical user interface. Seedpod can be used for multiple scientific research domains since only its knowledge model contains domain-specific content. It decreases development time and cost, thereby allowing scientists to focus on producing and collecting data.

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Last Known Position

Software Developer, Practice Fusion


Ching-Ping Lin
2010

Thesis/Dissertation Title

A Cognitive Work Analysis of Physician Order Entry in Pediatric Inpatient Medicine Teams

Computer Physician Order Entry (CPOE) systems have been shown to save time, streamline processes and reduce medication prescribing errors and adverse drug events. However, CPOE remains a poorly adopted technology in most United States hospitals. Clinical work is known to be interruptive, multitasking, collaborative and distributed yet current CPOE systems emphasize linear, normative and solitary work. To study this work-technology disconnect, I performed a qualitative field study that included document collection, observations and interviews of pediatric inpatient physicians working in teams. I identified emerging physician work themes through inductive analysis. I systematically characterized the larger contexts in which ordering occurs by deductively analyzing these data using Cognitive Work Analysis (CWA) - a holistic systems analysis framework that characterizes work by identifying constraints on work at multiple levels from the work environment to the worker. Through these combined results, I identified and will present design implications for future CPOE systems that can support flexibility, cooperation and adaptation to unanticipated work situations before they become sources of medical errors.

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Last Known Position

Visiting Fellow, George Institute for Global Health, Peking University Health Science Center


Hen-Tzy (Jill) Lin
2006

Thesis/Dissertation Title

A Shape-based Image Retrieval System for Assisting Intervention Planning

Craniosynostosis is a serious and common pediatric disease caused by the early fusion of the sutures of the skull. Premature suture fusion results in severe malformation in calvarial shapes. A single surgery, i.e. cranioplasty, is required to release the fused suture and reshape the deformed calvaria in order to prevent further deformation in skull shapes and impairment in neuropsychological development. Even though no concrete evidence suggests whether or not surgical complications and neurobehavioral developments are directly affected by different calvarial shapes, radiologists and surgeons often use cases of similar shapes that were previously resolved as guidelines to prepare for pre-surgical planning and post-surgical evaluation. With the increasing amount of imaging data, a systematic and quantitative approach is required to help physicians capture information embedded in images and define image similarities between cases. We have designed and implemented a shape-based image retrieval system that will objectively and quantitatively retrieve cases of similar shapes that were previous treated or established to help physicians in the decision making process of the reconstruction of the skull.

Currently, most imaging studies in patients with craniosynostosis emphasize the description of qualitative features and relegate quantitative assessments to the measurement of a ratio or an angle between anthropometric landmarks. In order to objectively detect inter- and intra-class differences between shapes in the image retrieval system, we have developed a novel shape measurement called the symbolic shape descriptor (SSD) to refine and establish quantitative definitions of skull phenotype. Our experiments show that the SSD has classification performance that is better than or comparable to other shape descriptors, uses less space, and is much faster than competitors. We have also conducted a regression analysis to determine the correlation between skull shapes and neuropsychological development in children with isolated sagittal synostosis. The result of this study is incorporated in the retrieval system for prediction of mental and psychomotor scores in order to help psychologists decide whether to initiate intervention on affected children.

Last Known Position

Senior Analyst, Beghou Consulting


Leslie Liu
2015

Thesis/Dissertation Title

Appropriating Artifacts: Understanding and Designing for Patients with a Chronic Illness

From taking medications at the right time to emotionally dealing with their symptoms, patients who have a chronic illness must manage many facets of their illness. Today, patients often utilize different types of general-purpose technologies (e.g., Facebook) to manage their chronic illness. However, many of these technologies were designed with a general user in mind—a user who does not necessarily have the same needs as one who has a chronic illness.

In this dissertation, I discuss how people from three distinct populations–health vloggers with a chronic illness, older adults who have diabetes, and children with a chronic illness–reconfigure the “everyday things” that surround them. In other words, I unpack how artifacts, relationships, roles, and technologies—the things of our daily lives—are deftly reconfigured to support chronic illness management. Drawing from these discussions, I will detail how researchers and other interested parties can design technologies that leverage this appropriation of everyday things for patients’ chronic illness management. Lastly, I expand on how we can further improve current design methodologies by designing for reappropriation when designing for and with patients who have a chronic illness. By supporting appropriation in existing general technologies in addition to newly designed technologies, we can build upon and embrace the world that those with chronic illnesses have already reconfigured.

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Brent Louie
2008

Thesis/Dissertation Title

Modeling Uncertainty in Data Integration for Improving Protein Function Assignment

n this work we describe the development and evaluation of the BioMiner system for protein functional annotation. BioMiner is the implementation of a novel uncertainty model for annotation and is based on the Uncertainty in Information Integration (UII) system, a general-purpose data integration system with extended functionality to handle uncertainty in data. The informatics contributions of our work are as follows: 1) we develop and implement a first-in-class uncertainty model for annotation and illustrate the validity of the model, 2) we show that the uncertainty model is reliable by evaluating its robustness through a principled methodology, and 3) we demonstrate that the uncertainty model performs better than existing, commonly utilized, approaches through a rigorous performance evaluation.
The application of BioMiner also contributes to the expansion of domain knowledge by accurately identifying functions for proteins of unknown function, a problem of utmost importance to biology.

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Last Known Position

Computational Scientist, Nodality


Maxwell Neal
2010

Thesis/Dissertation Title

Modular, Semantics-based Composition of Biosimulation Models

Biosimulation models are valuable, versatile tools used for hypothesis generation and testing, codification of biological theory, education, and patient-specific modeling. Driven by recent advances in computational power and the accumulation of systems-level experimental data, modelers today are creating models with an unprecedented level of complexity. These researchers need tools that manage this complexity and scale across biological levels of organization and physical domain. Historically, many industries have addressed the issue of complexity by adopting a modular product design. In order to apply this approach to the field of biosimulation, existing models must be cast as interoperable components. However, modelers today use a variety of simulation languages so that interoperability is the exception rather than the rule.

For my dissertation research I have worked on the challenges of modularity and interoperability within biosimulation. I helped develop a modular, multi-scale, multi-domain modeling approach called SemSim that provides broad model interoperability. The SemSim approach includes a declarative model description format that can capture the computational and semantic information in existing legacy models, thereby converting them into interoperable, reusable components. Because they interoperate at the semantic level, SemSim models offer opportunities to automate common composition and decomposition tasks beyond currently available methods. For my dissertation project I created and tested a software tool called SemGen that helps automate the modular composition and decomposition of SemSim models. With this tool, users can 1) convert legacy models into the SemSim format and annotate them with semantic data, 2) automatically decompose SemSim models into interoperable sub-models, 3) semi-automatically merge SemSim models into larger systems, and 4) encode SemSim models in an executable simulation format. As a proof-of-concept demonstration of modular modeling, I used SemGen to perform a set of model composition and decomposition tasks using models of hemodynamics, neural signaling, molecular diffusion, and chemical pathway kinetics. This demonstration establishes SemGen’s capabilities for automating the modular composition and decomposition of biosimulation models across physical scales and physical domains. Thus, SemGen has the potential to advance the entire field of biosimulation by spurring the development of complex models for biological research, drug target identification, and patient-specific modeling.

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Last Known Position

Computational Biologist, Center for Infectious Disease Research (CIDR()


B. Nolan Nichols
2014

Thesis/Dissertation Title

Reproducibility in Human Cognitive Neuroimaging: A Community-Driven Data Sharing Framework for Provenance Information Integration and Interoperability

ccess to primary data and the provenance of derived data are increasingly recognized as an essential aspect of reproducibility in biomedical research. While productive data sharing has become the norm in some biomedical communities, human brain imaging has lagged in open data and descriptions of provenance. The overarching goal of my dissertation was to identify barriers to neuroimaging data sharing and to develop a fundamentally new, granular data exchange standard that incorporates provenance as a primitive to document cognitive neuroimaging workflow.

For my dissertation research, I led the development of the Neuroimaging Data Model (NIDM), an extension to the W3C PROV standard for the domain of human brain imaging. NIDM provides a language to communicate provenance by representing primary data, computational workflow, and derived data as bundles of linked Agents, Activities, and Entities. Similar to the way a sentence conveys a standalone thought, a bundle contains provenance statements that parsimoniously express the way a given piece of data was produced. To demonstrate a system that implements NIDM, I developed a modern, semantic Web application platform that provides neuroimaging workflow as a service and captures provenance statements as NIDM bundles. The course of this work necessitated interaction with an international community, which adopted and extended central elements of this work into prevailing brain imaging software. My dissertation contributes neuroinformatics standards to advance the current state of computational infrastructure available to the cognitive neuroimaging community.

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Last Known Position

Postdoctoral Fellow,Psychiatry and Behavioral Sciences, Stanford University School of Medicine


Casey Overby
2011

Thesis/Dissertation Title

A Clinical Decision Support Model for Incorporating Pharmacogenomics Knowledge into Electronic Health Records for Drug Therapy Individualization: A Microcosm of Personalized Medicine

Personalized medicine, where treatment may be tailored to individual characteristics, has the potential to improve patient outcomes. As a microcosm of personalized medicine, findings from pharmacogenomics (PGx) studies have the potential to be applied to individualize drug therapy such that the efficacy is improved and the occurrence of adverse drug events are reduced. In this context, the overarching research question this research project aimed to address was: what needs to be done to incorporate PGx knowledge into an electronic health record (EHR) in a useful way that facilitates drug therapy individualization? Clinical decision support (CDS) imbedded in the EHR was investigated as a model for providing access to PGx knowledge to support accurately using and interpreting patient genetic data to individualize drug therapy. The aims of this research were: (1) characterizing PGx knowledge resources; (2) determining capabilities of current CDS systems; (3) developing a prototype implementation of a model for PGx CDS; and (4) evaluating the utility of the PGx CDS model implementation. Findings from this work enhances our understanding of how PGx knowledge should be made accessible via CDS in the EHR given characteristics of PGx knowledge, technical capabilities of current clinical systems and characteristics of clinicians. More generally, the results of this study contribute a model that is directly applicable to the incorporation of genetic and molecular data into EHRs and its usability by healthcare providers.

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Last Known Position

Assistant Professor, University of Maryland School of Medicine


Albert Park
2015

Thesis/Dissertation Title

Enhancing Health Information Gathering Experience in Online Health Communities

Online health communities can offer a range of diverse personal health expertise and experiences, yet gathering relevant health information is a significant challenge for members and researchers as each party faces different obstacles.

In my dissertation, I examine the challenges of gathering health information from online health communities in two parts according to the respective stakeholders. I first address the challenge that patient members face during their time of interaction with the online community to gather information. Within the context of computer-mediated communication in online health communities, I focus on issues associated with topic drift (i.e., topic changes) and sustainment of active participation (i.e. posting messages to participate in the communities). I also address the challenge of processing and making sense of a large amount of collective knowledge shared in online health communities. Within the context of patient-generated text in online cancer communities, I focus on the challenges of automatically understanding patient-generated text using existing natural language processing (NLP) tools.

Many members of online communities are willing to go the extra mile to help others in similar situations. Yet, many challenges hinder the experience of gathering health information from these communities.Though these efforts leave a digital trace that is embedded with diverse personal health expertise and experiences, we still lack the capability to automatically utilize this invaluable information. By expanding on existing knowledge on topic drift, sustainment of active participation, and processing patient-generated text, we can maximize the benefits of online health communities and improve patient members’experience of gathering health information.

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Last Known Position

Postdoctoral Research Fellow, Biomedical Informatics, University of Utah


Rupa A. Patel
2013

Thesis/Dissertation Title

Designing for Use and Acceptance of Tracking Tools in Healthcare

Patients with cancer experience many unanticipated symptoms and struggle to communicate them to clinicians during treatment. They contend with a variety of symptoms at home—issues stemming from cancer progression, treatment regimens, and co-morbidities. Although many patients rely on clinic visits to get help with managing these symptoms, clinicians often underestimate the intensity of patients' symptoms or miss them altogether. A proliferation of mobile and sensor-based tools, which enable self-tracking, leads us to consider how to approach their design to support cancer symptom management.

However, tracking tools are not widely used and accepted in cancer care. To further study use of tracking tools, I analyzed the use of two different types of manual tracking tools: (1) ESRA-C2, an electronic Patient-Reported Outcome (ePRO) tool deployed to 372 people with cancer; and (2) HealthWeaver, a personal informatics tool deployed as a technology probe to 10 women with breast cancer. Also, I analyzed the “in-the-wild” self-tracking practices of the 10 women before they used HealthWeaver, as well as 15 other women with breast cancer. Results showed that patients who voluntarily used the ePRO tool the most frequently had relatively low symptom distress. In addition, although patients’ tracking behaviors “in the wild” were fragmented and sporadic, these behaviors with a personal informatics tool were more consistent. Participants also used tracked data to see patterns among symptoms, feel psychosocial comfort, and improve symptom communication with clinicians. Given these considerations, I describe a new conceptual model that has implications for patients, clinicians, and tool developers. If patients and clinicians accept and integrate tracking tools into cancer symptom management away from the clinic, we can move closer to continuous healing relationships that are the cornerstone of effective care.

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Last Known Position

User Experience Researcher, GoDaddy


Deanna Petrochilos
2013

Thesis/Dissertation Title

A Graph-Theoretic Approach to Model Genomic Data and Identify Biological Modules Associated with Cancer Outcomes

Studies of the genetic basis of complex diseases present statistical and methodological challenges to discover reliable and high-confidence genes that reveal biological phenomena underlying the etiology of the disease or gene signatures prognostic of disease outcomes. This thesis examines the capacity of graph-theoretical methods to integrate and analyze genomic information and thus facilitate using prior knowledge to create a more discrete and functionally-relevant feature space. To assess the statistical and computational value of graph-based algorithms in genomic studies of cancer onset and progression I apply an instance of a random walk graph algorithm in a weighted interaction network. I merge high-throughput co-expression and curated interaction data to search for biological modules associated with key cancer processes and evaluate significant modules by their predictive value and functional relevance. This approach identifies interactions among genes involved in proliferation, apoptosis, angiogenesis, immune evasion, metastasis, and energy metabolism pathways that generate hypotheses for further cancer biology studies. Results from this analysis show that graph-based approaches are a powerful tool to integrate and analyze complex molecular relationships and to reveal coordinated activity of significant genomic features where previous statistical and analytical methods focusing on individual effects are limited.

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Jamie Pina
2011

Thesis/Dissertation Title

Notifiable Conditions Information Systems in Local Public Health Practice: Applied Informatics Research

Notifiable conditions reporting is an essential component of public health surveillance. Through this process local public health jurisdictions (LHJ) collect information about health events of interest and share this information with state-level public health departments. Many LHJs make use of electronic information systems to manage, process, and analyze the notifiable conditions data. In the midst of state and national-level efforts to standardize notifiable conditions reporting processes, there has been a nation-wide push for LHJs to adopt new notifiable conditions information systems that are capable of online reporting. Offering the benefit of faster reporting to state public health departments, and compliance with new standardization efforts, these systems may not be designed to accommodate the specific work practices that are unique to each local public health jurisdiction. The implementation of a new information system in an LHJ may disrupt the work that is required to properly address the health issues that are unique to the region. This could have serious effects on local public health practice.

This study aims to improve the development and evaluation of notifiable conditions information systems that support the work of local public health jurisdictions through three main efforts. 1) To describe the use of information systems in local public health practice, communicable disease information management activities were observed at a large municipal public health agency. Participant observation and task analysis were used to describe the work of local public health practitioners. 2) An online survey was developed and distributed to local public health practitioners in Washington State. Employees were asked about their work practices and interactions with information management systems. Descriptive statistics were used to compare the usage of information systems across LHJs of differing size. 3) An evaluation strategy for local public health agencies was developed to assess the usefullness of information systems within their working environment. A guidebook describing the strategy was written and shared with local public health practitioners.

The findings from this study provide new knowledge which can be used to inform the design and evaluation of notifiable conditions, communicable disease, and outbreak management software.

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Last Known Position

Research Scientist, RTI International; Adjunct Professor, Emory University


Mabel Raza Garcia
2013

Thesis/Dissertation Title

A Proof of Concept System for Automated Cervical Cancer Screening in Peru

Cervical cancer is the second most frequent cancer in women around the world and affects half a million women per year. The World Health Organization (WHO) estimates that 275,000 women die every year, and 80% to 85% of these deaths occur in low-resource countries in Africa and South America. In Peru, cervical cancer has the highest incidence and the second highest mortality rate of cancers among women. Currently, the screening techniques such as the Papanicolau (Pap) test, in which some cells from the cervix are examined under a microscope to detect potentially pre-cancerous and cancerous cells, and the Visual Inspection with Acetic Acid (VIA), in which the surface layer of the cervix is examined through visual inspection after washing it with 3% to 5% acetic acid (vinegar) for one minute, are part of the national health policy in Peru. The Pap test is mainly used in urban areas in Peru. However, there are some challenges related to spreading the Pap test throughout the whole country: lack of quality and standardization of the readings of Pap smears, shortage of trained personnel, uneven processing of samples resulting in diagnosis and treatment delays, and lack of even basic laboratory infrastructure, all of which impacts greatly the sustainability of this procedure in remote and/or poor settings.

Extensive research has shown that computational solutions are a viable and suitable aid for overcoming these barriers. However, the majority of these solutions are commercial products that are not affordable for developing countries, such as Peru. In this context, developing a strategy, algorithm and open source computational implementation that recognizes normal vs. abnormal Pap smears can ultimately provide a cost-effective alternative for developing countries. The dissertation-specific objectives are to: 1) determine the characteristics of normal vs. abnormal Pap smears through expert consultation and relevant literature, 2) collect data sets and run preliminary experiments to compare two possible approaches, and 3) assess the accuracy, sensitivity and specificity of the proposed cervical cancer screening approach for classifying normal vs. abnormal Pap smears compared to experts’ review.

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Last Known Position

Research Assistant, Bioinformatics, Universidad Peruana Cayetano Heredia


Blaine Reeder
2010

Thesis/Dissertation Title

Characterizing Information Needs for Public Health Continuity of Operations: A Scenario-Based Design Approach

Public health field nurses play a critical role in the community during disasters and emergencies. Continuity of operations planning (COOP) is a recognized part of any emergency management strategy and technology should support the elements of public health COOP through support of routine work activities.However, the work of public health field nurses is characterized by multiple, disparate digital and paper-based information systems that require duplicate data entry, reduce efficiencies in the performance of daily work and create issues during emergencies.

This research project characterized the information needs of public health nurses and nurse supervisors through three specific aims. The first aim consisted of an information needs assessment through a systematic literature review for technology support of public health continuity of operations planning and semi-structured interviews with public health practitioners in two local health jurisdictions. The second aim used scenario-based design and persona creation to develop a conceptual design of an integrated information system that supports the work of public health nurses and nurse supervisors. The third aim used focus groups with public health nurses and other public health staff to validate the information system design in both local health jurisdictions.

Focus group participants validated the conceptual information system design in the following thematic areas: The need for a dynamic, flexible system, support for client service and documentation, workload tracking, staff management, one-time data entry, real-time documentation, communication and data exchange between divisions, integrated scheduling and communication with external providers. Focus group participants corrected perceived errors in design and made additional design recommendations.

The results of this research highlight the importance of involving public health practitioners in the design process for technology that supports their information needs and work activities and can support them during emergencies. In addition, this research shows it is possible to validate and reuse design concepts across local health jurisdictions that have different organizational structures. Reusable design knowledge is an important goal for public health informatics efforts to increase efficiencies through support of standard work practices and reduce the costs of information system projects.

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Last Known Position

Assistant Professor, University of Colorado College of Nursing


Steve Rysavy
2014

Thesis/Dissertation Title

Data-driven Methods and Models for Predicting Protein Structure using Dynamic Fragments and Rotamers

Proteins play critical roles in cellular processes. A protein’s conformation directly relates to its biological function and, consequently, determination of such structure can provide great insight into a protein’s function. Using a computational technique called molecular dynamics (MD), we are able to simulate and observe protein dynamics at a much higher temporal and spatial resolution than allowed by experimental methods. Dynameomics is a research endeavor that uses MD to produce thousands of protein simulations, resulting in hundreds of terabytes of data. Using novel visual analytics techniques, we have mined the Dynameomics data warehouse for data on protein backbone segments and side-chain behavior, called fragments and rotamers, respectively. Knowledge derived from these dynamic fragments and rotamers was used to improve the quality of protein loop structure predictions. We have created novel data models to store, analyze and compare fragments and side-chain rotamers, then developed methods to predict loop structures with information inferred from these data models. Protein loop regions predicted from these fragments and rotamers produce biologically relevant structures that improve upon current protein loop prediction methods. In conjunction with the fragment and rotamer research, we produced a novel visual analytics framework called DIVE, a Data Intensive Visualization Engine. This software has been instrumental in advancing our bioinformatics research, but it is a general-purpose framework applicable to a wide range of big data problems.

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Francisco Saavedra
2012

Thesis/Dissertation Title

Development and Evaluation of a Web-based Electronic Medical Record System Without Borders

Despite implementation of electronic medical record (EMR) systems in the United States and other countries, EMRs often lack global access, standardization, an efficient interface, and effective knowledge-based tools at the point of care. Consequently, the information needs of patients, practitioners, administrators, researchers, and policymakers often go unmet, leaving providers especially dissatisfied. To address this multifaceted problem, a novel EMR design referred to as “Electronic Medical Records Without Borders” (EMR WB) was created to ensure that the most vital pieces of patient clinical records are available to make health care decisions. A web-based, standardized, family medicine, clinical history model was developed and evaluated as an EMR clinical core that integrates state-of-the-art terminology, peer-reviewed, evidence-based protocols with real-time access to diagnostic decision support systems and the biomedical literature, using a unified, navigable, intuitive computer interface. This project aimed to facilitate structured clinical documentation, usability, global access, and decision-making processes to better address not only local, clinical, and psychosocial primary care problems in targeted underserved global communities, but also to mitigate transnational migration health issues based on information exchange among primary care settings. A survey of post-exposure EMR WB use indicated a measurable, positive effect was made on provider satisfaction compared with a previously used, paper-based record system. Data were analyzed using descriptive statistics.

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Last Known Position

Medical Record System Without Borders; 2012 Co-founder and Executive Director, ConstaTec Solutions, LLC


Patrick Sanger
2015

Thesis/Dissertation Title

Patient-Centered Development and Evaluation of a Mobile Wound Tracking Tool

Surgical site infections (SSI) are a common, costly and serious problem following surgery, affecting at least 500,000 people per year. Most infections now occur after hospital discharge, placing the burden of recognizing problems and seeking care on patients who are ill-prepared for that responsibility, resulting in reduced quality of life and preventable readmission. Yet, few efforts have been made to systematically engage patients in early identification of SSIs at home to reduce their impact.

I will describe a novel approach to addressing this problem: a patient-centered mobile health (mHealth) application that enables patients to serially track wound symptoms and photos, and securely communicate with their providers. To this end, I first present a needs assessment among surgical patients and clinicians. I then describe an iterative process of engagement with these stakeholders resulting in design considerations generalizable to post-acute care mHealth (of which wound tracking is a part). Finally, I assess the clinical value of serial wound data and photos.

My work enhances understanding of the challenges facing patients who develop post-discharge SSI, and begins to map the unexplored design space of post-acute care mHealth, especially around areas of patient-clinician conflict. In addition, I propose a new method to aid in design of patient-centered health IT and demonstrate the value of serial wound data and photos beyond existing data sources. In addition to these contributions to research, I am making an applied contribution to the development of mPOWEr, a wound-tracking tool that seeks to improve clinical outcomes and patients’ experience on the way to those outcomes.

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Last Known Position

Medical Student, University of Washington


Terry Hsin-Yi Shen
2009

Thesis/Dissertation Title

Determining the Feasibility and Value of Federated Data Integration Combining Logical and Probabilistic Inference for SNP Annotation

Most common and complex diseases are influenced at some level by variation in the genome. The future work of statistical geneticists, molecular biologists, and physician-scientists with interests in genetics or genomics must thus take genetics into consideration. Research done in public health genetics, specifically in the area of single nucleotide polymorphisms (SNPs), is the first step to understanding human genetic variation. Functional uncertainty, volume of information, and cost-effectiveness result in the prioritization of SNPs to be an important research question. SNP Integration Tool (SNPit) is a data integration system tool that looks at all the possible predictors of functional SNPs and provides the user with integrated information and decision making capability. Determining the feasibility and value of SNPit with rules and probabilistic inference, thus, represents challenges from both the biological and biomedical informatics standpoint concerning how to represent, integrate, and conduct inference over disparate biological data sources.

The main objective of this dissertation is to determine the feasibility and value of creating a federated integration system with combinations of logical, probabilistic, and logical combined with probabilistic inference for functional SNP annotation. Through iterative design, four versions of the SNPit system were created which consolidates information on a variety of functional annotation predictors and includes combinations of logical and probabilistic inference. Furthermore, this dissertation evaluates the feasibility of federated data integration and assesses its’ accuracy for SNP annotation, characterizing the suitability for adding logical and probabilistic inference to the federated data integration for both point and regional SNP annotation. This study also explores the feasibility of combining both logical and probabilistic inference for point and regional SNP annotation. This dissertation contributes to general knowledge in informatics as well as SNP annotation by describing the design, implementation, and evaluation of combinations of logical, probabilistic, and both logical and probabilistic inference applied to the domain of functional SNP annotation.

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Last Known Position

Faculty Research Associate, University of Maryland School of Nursing


Andrew Simms
2011

Thesis/Dissertation Title

Mining Mountains of Data: Organizing All Atom Molecular Dynamics Protein Simulation Data into SQL

A significant portion of my research has involved organizing all atom molecular dynamics protein simulation data into a form that is both manageable and is conducive to analysis. These data consist of multi-gigabyte collections of four-dimensional atomic coordinates (x, y, z and time) and secondary analyses, as well as classification data used to select and organize the proteins for simulation. The initial database design was released in 2007 and published in 2008 as the Dynameomics Data Warehouse1, and has been in continuous development to accommodate an ever increasing number and length of simulations. The Consensus Domain Dictionary2 (CDD), released in 2010, defines a rank ordered set of globular proteins that sample the most frequently occurring protein folds found in the Protein Data Bank. Andrew's defense presented the CDD database, the dimensional model at the core of the data warehouse, and a novel method for optimizing queries involving spatial data stored in relational tables.

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Last Known Position

Subject Matter Expert, Cognitive Medical Systems, Inc.


Dhileepan Sivam
2010

Thesis/Dissertation Title

A Rule-Based Strategy for Accurately Describing Gene Content Similarities and Differences Across Multiple Genomes

A fundamental tasks in genome research is that of comparing gene content between multiple genomes. In infectious disease research such comparisons are critical for determining the underlying parasite genetic factors that are responsible for disease transmission, pathogenicity and clinical outcome. Although numerous technologies exist for comparing gene sequences and grouping similar genes, the genomics field lacks structured methods for describing the complicated evolutionary dynamics that give rise to the differences between the compared species. In this dissertation I put forth novel technologies for accurately and precisely describing differences in gene content across multiple genomes.

First, I introduce a light-weight knowledge representation specification that allows us to aggregate gene annotation and sequence comparison data from heterogeneous sources. Next, I describe a new ontology for describing pairwise homology relationships between genes, as well as a rule-based system for applying those terms to sequence comparison results. I then detail a novel method for grouping genes based on the nature of their homology relationships. Finally, I present a technique for querying the gene groups in order to uncover interesting evolutionary trends across the compared genomes. These methods represent a significant advance in the clarity and detail with which large scale comparative genomics can be described; furthermore, the novel techniques that I present in this work are amenable to integration with existing sequence comparison and clustering technologies.

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Last Known Position

Assistant Director, Commercial Development Group, Intellectual Ventures


Meredith Skeels
2010

Thesis/Dissertation Title

Sharing by Design: Understanding and Supporting Personal Health Information Sharing and Collaboration within Social Networks

Friends, family, and community provide important support and help to patients who face an illness. Unfortunately, keeping a social network informed about a patient’s health status and needs takes effort, making it difficult for people who are sick and exhausted from illness. Members of a patient’s social network are often eager to help, but can be unsure of what to do; they must balance their desire to help with trying not to bother a sick friend. In this dissertation, I describe research on how people share health information within their existing social networks and present technology to create informed, helpful networks. I used a mixed methods approach of interviews and an online questionnaire to provide a detailed analysis of what health information people share, who they share with, mode of transmission, and why people share personal health information.
My research culminates in the design of new technology that enables patients to create an informed network and catalyzes helping activities within that network. I used participatory design methods with breast cancer patients and survivors to ensure that the design is based on a firm understanding of users’ goals, priorities, constraints, and current sharing practices. Together, we designed a technology that allows a patient to keep their social network up to date, solicit help from their network, field offers of help, and collaborate through discussions. The design is motivated by the insight that a more informed social network is better able to provide needed help and support. Advocating that patient-centered technology should allow users to share personal health information with others comes with the responsibility to contribute to the effort to create usable privacy interfaces. I present a method for evaluating the transparency of privacy controls and use this method to identify a transparent icon that can be embedded within interfaces to show how information is being shared.

Embracing the complex picture of how patients manage and share personal health information with others will ultimately improve the technology available to support patients. I contribute a better understanding of current sharing practices and technology to enable patients to create informed, helpful social networks.

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Anna Stolyar
2011

Thesis/Dissertation Title

A Study of Low-Income Health Care Consumers: Motivations for Using Electronic Personal Health Record Systems

Health care consumers have different motivations and needs for managing their detailed medical history as well as health information to support their healthcare-related decisions. Electronic Personal Health Record systems are a form of tool that helps health care consumers collect, manage and use their health information. Despite the fact that many types of PHR systems have become available to various groups of consumers, the motivations to utilize PHRs and the barriers to widespread adoption have proven difficult to measure. In this research, I explore and define the factors that motivate individuals’ decisions on whether to adopt a PHR system.
I chose a grounded-theory-based qualitative methodology to identify and explore these factors in a setting where a PHR had been available for one and a half to three years to a group of low-income individuals. Demographics of this group included elderly and disabled individuals, many of whom had multiple co-morbidities that result in complex health information management needs.
The end results of this work are two frameworks created from the health care consumer or patient-driven perspective. (1) The Levels of Interest in Health Information Management Framework (LIHIMF) can be used to categorize potential adopters to help create personas and tailored approaches to designing and implementing PHR systems. This framework describes three types of potential PHR adopters by their willingness to manage their health information or use a PHR. (2) The Health Information Management Motivational Factors Framework (HIMMFF) is a comprehensive framework of issues that contribute to PHR adoption. Factors that motivate or discourage adoption as described by both PHR users and non-users are grouped into seven categories. These frameworks can be used by the PHR and health information management research community to better understand and further study PHR adoption.
This work contributes an approach to understanding patient information management needs from the patient-driven perspective. Furthermore, it advances our understanding of how information systems impact health information management in underserved populations.

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Greg Strylewicz
2007

Thesis/Dissertation Title

Errors in the Clinical Laboratory: A Novel Approach to Autoverification

Clinical laboratories provide a critical service to the health care and well-being of the world's population. Estimates suggest that the clinical laboratory influences some 70 percent of health-care decisions, but requires only about 4 percent of the health-care expenditures. Given an estimated 7 billion laboratory tests per year in the United States, about 1% of the results, or 70 million laboratory errors annually, are erroneous with an estimated 6%, of those errors causing harm to the patient. Laboratory errors harm millions of patients each year and laboratory experts spend countless hours reviewing billions of laboratory results each year in the search for these rare errors. Autoverification systems, automated programs used to check laboratory results for errors, can save laboratories countless hours and be more accurate than laboratory experts, but the current generation of rule-based systems is not appropriate for the clinical laboratory domain due to its inherent uncertainty. This research demonstrates that a novel approach using a synthetic error generation system to create training datasets for a conditional Gaussian Bayesian network produces an autoverification system superior to ones trained using standard methods and superior to laboratory experts. Unlike standard approaches that require an expensive and time-consuming expert annotation process to create training datasets, the synthetic error generation method uses results that were reviewed normally.

By creating synthetic datasets, the synthetic error generation process creates customized training datasets, which maximize the Bayesian network's performance in detecting errors. In this dissertation, we review the clinical laboratory process and the many sources of errors in clinical laboratory results, Bayesian networks, and the class imbalance problem. Next, we elucidate the performance characteristics of the synthetic error generation process, which is followed by a comparison between our novel method and standard approaches to the class imbalance problem. Finally, we compare the results of a synthetic error autoverification system against laboratory experts in the identification of errors.

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Last Known Position

Informatics Manager, Northwest Lipid Metabolism and Diabetes Research Laboratories


Ravensara Travillian
2006

Thesis/Dissertation Title

Ontology Recapitulates Phylogeny: Design, Implementation and Potential for Usage of a Comparative Anatomy Information System

Building on our previous design work in the development of the Structural Difference Method (SDM) for symbolically modeling anatomical similarities and differences across species, we describe the design and implementation of the associated comparative anatomy information system (CAIS) knowledge base and query interface, and provide scenarios from the literature for its use by research scientists. Our work includes several relevant informatics contributions. The first one is the application of the structural difference method (SDM), a formalism for symbolically representing anatomical similarities and differences across species. We also present the design of the structure of a mapping between the anatomical models of two different species, and its application to information about specific structures in humans, mice, and rats. The design of the internal syntax and semantics of the query language underlies the development of a working system that allows users to submit queries about the similarities and differences between mouse, rat, and human anatomy; delivers result sets that describe those similarities and differences in symbolic terms; and serves as a prototype for the extension of the knowledge base to any number of species. We also contributed to the expansion of the domain knowledge by identifying medically-relevant structural questions for humans, mice, and rats. Finally, we carried out a preliminary validation of the application and its content by means of user questionnaires, software testing, and other feedback.

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Last Known Position

Research Scientist


Jim Tufano
2009

Thesis/Dissertation Title

Information and Communication Technologies in Patient-Centered Healthcare Redesign: Qualitative Studies of Provider Experience

Promoting widespread availability and provider adoption of electronic medical records is a core component of current efforts to reform healthcare in the United States. Initiatives to redesign healthcare to achieve quality improvement, patient access, economic sustainability, and other reforms often seek to leverage the potential of electronic medical records and other information and communication technologies. However, the evidence pertaining to the effectiveness of these technologies in supporting and promoting these objectives is limited, and their adoption among healthcare providers remains low – particularly in primary care and other ambulatory care settings. Given both the questionable sustainability of primary care and its central role in current healthcare reform initiatives, there is a critical need to inform these endeavors with empirically - derived knowledge of how information and communication technologies affect healthcare providers and their efforts to redesign care to better meet the needs of their patients and communities. This dissertation explores provider perspectives on the roles, importance, and effects (both positive and negative) of healthcare information and communication technologies in the context of patient-centered healthcare redesign. Three qualitative observational studies were conducted at Group Health Cooperative, a large integrated healthcare delivery system serving patients throughout the Pacific Northwest. These studies were informed by Donabedian’s framework for evaluating healthcare quality, Rogers’ Diffusion of Innovations Theory, and the Tavistock Institute’s Sociotechnical Systems Theory.

Findings revealed provider and organizational perspectives on their experiences with implementing and using a commercial clinical information system (EpicCare Ambulatory EMR) with an integrated patient Web portal, patient-provider email, internal clinical messaging, an internally-developed online health risk assessment application, and other information and communication technologies. Participants expressed sharply contrasting perspectives on the same technologies viewed as components of two unique practice redesign initiatives – an organization-wide redesign of operations to implement Patient-Centered Access, and a single clinic redesign to implement the Patient-Centered Medical Home model. These findings suggested that contextual factors such as the care redesign methods and the care models used to guide care redesign are key determinants of the effects associated with the implementation and use of these technologies. This dissertation contributes to the literature on sociotechnical approaches to technology-enabled healthcare redesign and evaluation by describing how instances of these different care redesign models incorporated the various technologies, and by evaluating providers’ perspectives on their roles, importance, and effects.

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Last Known Position

Patient Portal Product Manager, NextGen Healthcare


Wayne Warren
2012

Thesis/Dissertation Title

Towards an Extensible Atlas-Based 3D Visualization Framework for Biomedicine: Biolucida

Today’s biomedical research endeavors often entail exposure to a daunting amount of expressive data which must be effectively comprehended and communicated. It has been well established that visualization is a powerful conduit for analyzing, representing, and communicating such information. Since these data are often associated with structures of anatomy that can be represented as computer-generated 3D models, the biomedical atlas can be leveraged as an effective visualization motif. This style of presentation not only conveys spatial relationships among the data, but also provides a natural representation which is easily understood by a wide audience. Many systems have been produced which can be considered computer-based biomedical atlases, some of which have been used to visualize data and to illustrate spatially complex concepts. However, the development of these systems has been costly due to the fact that many, while similar in features and design, have been built from the ground up as single-purpose applications. A next-generation visualization system, Biolucida, has been developed as a generalizable framework which is designed to meet the visualization needs of the biomedical community through its comprehensive feature set and its extensible architecture.

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Chunhua Weng
2005

Thesis/Dissertation Title

Supporting Collaborative Clinical Trial Protocol Writing through Annotation Design

Clinical trial protocols are important documents that guide clinical research. Modern protocol development requires collective expertise from a group of Loosely-Coupled protocol writers, who work across distances and time zones. Email has been the primary communication tool for these protocol writers. Unfortunately, it inadequately supports collaborative writing tasks. Without appropriate groupware technology, these protocol writers often compromise work efficiency and the degree of collaboration to complete their tasks. This situation is exhibited at the Southwest Oncology Group (SWOG), one of the Cooperative Group Programs under the direction of NCI. While it is clear that its current work practices do not support optimal collaboration, it is unclear how to improve the collaboration and communication in such group work because the complexities of collaborative protocol development has rarely been studied. This research utilizes and extends Computer-supported Cooperative Work (CSCW) theories to identify the problems in protocol development and to design groupware technology for supporting this group work.

This dissertation consists of four parts: (1) qualitative fieldwork of the collaborative protocol writing process at SWOG; (2) a design of an annotation model that facilitates in-context communication around evolving documents during the iterative reviewing and revising process; (3) a design and an implementation of a protocol collaborative authoring tool (PCAT) that embodies the annotation model from #2 to address group work problems identified in #1; and (4) a validation of the usability of the annotation model and the PCAT prototype. In addition, this dissertation implements a grounded design process and contributes a socio-technical design of groupware technology in a healthcare setting to the literature of socio-technical approaches for system design.

Last Known Position

Associate Professor, Biomedical Informatics, Columbia University


Wen-Wai Yim
2016

Thesis/Dissertation Title

Information extraction from clinical and radiology notes for liver cancer staging

Medical practice involves an astonishing amount of variation across individual clinicians, departments, and institutions. Adding to this condition, with the exponential pace of new discoveries in pockets of biomedical literature, medical professions, often understaffed and overworked, have little time and resources to analyze or incorporate the latest research into clinical practice. The accelerated adoption of electronic medical records (EMRs) brings about great opportunities to mitigate these issues. In computable form, large volumes of medical information can now be stored and queried, so that optimization of treatments based on patient characteristics, institutional resources, and patient preferences can be data driven. Thus, instead of relying on the skillsets of patients' support network and medical teams, patient outcomes can at least have some statistical guarantees.

In this dissertation, we focus specifically on the task of hepatocellular carcinoma (HCC) liver cancer staging using natural language processing (NLP) techniques. Staging, or categorizing cancer patients by extent of diseases, is important for normalizing patient characteristics. Normalized stages, can then be used to facilitate evidence-based research to optimize for treatments and outcomes. NLP is necessary, as with other clinical tasks, a majority of staging information is trapped in free text clinical data.

This thesis proposes an approach to liver cancer stage phenotype classification using a mixture of rule-based and machine learning techniques for text extraction. Included in this approach is a careful, layered design for annotation and classification. Each constituent part of our system was characterized by detailed quantitative and qualitative analysis regarding several medical conditions.

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Last Known Position

Postdoctoral Scholar, Stanford University


MS Students




Dongyang Chen
2017

Thesis/Dissertation Title

Qualitative Assessment of Hot Debriefs for Code Teams at Seattle Children’s Hospital

Seattle Children’s Hospital recently implemented ‘hot debriefs’ for code teams that respond to cardiac or respiratory resuscitation code events. Hot debriefs are meetings immediately after the code event where the code team members are able to discuss the details of the event that just transpired. These discussions generally revolve around aspects of the code event that went well as well as those that could be improved upon. Before the implementation of these hot debriefs, no such formal meetings with the entire code team were required. This meant that if any particular code team member did want to discuss a code event, participation was minimal and the meeting would often occur at a much later time such as the following day. Hot debriefs were implemented with the intent of increasing information review and improving the quality of future code events. I assessed the status of these hot debriefs using well-established qualitative research methods and semi-structured interviews with clinicians who participated in them to understand their thoughts and feelings on the new process. I interviewed ten participants (including nurses, respiratory therapists, physicians, etc.) and qualitatively analyzed their responses. Four key themes emerged: the effectiveness of hot debriefs, process formalization, openness of communication, and dissemination of information. For the first theme, the participants unanimously approved of the hot debriefs as a process for increasing information review and improving the quality of code events. However, there were concerns revolving around the other three themes with mixed opinions. This study shows that in order to effectively implement a process such as hot debriefing, one should consider the needs and opinions of the participants themselves.

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Last Known Position

User Operations Specialist at Stripe



Marea Cobb
2015

Thesis/Dissertation Title

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Last Known Position

Analytical Software Engineer, Intel Corporation



Loren Donelson
2004

Thesis/Dissertation Title

Last Known Position

Director of Product Management, Verve Wireless


Christine Fong
2005

Thesis/Dissertation Title

Last Known Position

Biomedical Informatics Consultant, Institute of Translational Health Sciences (ITHS), University of Washington


Lynne Harris
2011


Tressa Hood
2017

Thesis/Dissertation Title

A Systems Biology Approach to Characterizing Gene Fusion Pathways in Cancer

Gene fusions have long been known to drive cancer. Initial discovery of gene fusions was opportunistic, and functional assessment was done individually and experimentally. There is no comprehensive systems biology approach to understanding the impact of gene fusions on the signaling networks within tumor cells. An integrative computational approach was taken to achieve a better understanding of gene fusions and their complex influence on pathways and interaction networks in the context of lung cancer. Using well-studied fusions and publicly available gene expression data, the effect of fusion events on the expression pattern of gene networks revealed unique differences in tumors with gene fusions, tumors without gene fusions, and normal samples. This approach identifies gene expression signatures associated with specific fusions, and provides a model for integrating experimental and pathway data to better understand the biology of a fusion genes and their roles in oncogenesis.

Last Known Position

Research Associate IV, NanoString Technologies


Christi Inman
2009

Thesis/Dissertation Title

Last Known Position

Assistant Professor, Pediatric Rheumatology, University Of Utah School of Medicine and Primary Children’s Medical Center


Sophia Jeng
2006

Thesis/Dissertation Title

Last Known Position

Informatics Research Associate, Oregon Health & Science University


Twaha Kabocho
2011


Maher Khelifi
2015

Thesis/Dissertation Title

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Last Known Position

PhD student, Biomedical and Health Informatics, University of Washington


Tung Le
2008

Thesis/Dissertation Title

Last Known Position

Owner at TungTree



Tsung-Chien (Jonathan) Lu
2011

Thesis/Dissertation Title

Cross-Correlation Networks to Identify and Visualize Disease Transmission Patterns

Influenza-like illness (ILI) has been a major threat to the public health around the world. To inform influenza response by enhancing and supporting disease surveillance, a syndromic surveillance system collects case counts that are aggregated from multiple sources and jurisdictions. Although each jurisdiction has their own planned uses of the data, most systems focus on early detection of the outbreak in regional level response and the algorithms they are using often do not point to a route of transmission. In this work, we seek to develop approaches to aid comparison of data among jurisdictions to improve detection of geographic patterns in disease spread. Using cross-correlation to assess the pairwise similarity between regional case counts, we introduce a cross-correlation network based on ILI activity to reveal potential spatio-temporal patterns in disease transmission. The resulting networks were plotted and visualized in the map with the R statistical package. To evaluate the feasibility and utility of this approach, we validate these networks against population-level variables influencing the spread of infectious disease, including flight passenger volume, census worker flow, and geographic distance. In our analysis, the spatio-temporal transmission of ILI correlated more closely with state-to-state census worker flows and distance between states than with flight passenger flows. We demonstrate how this visualization motif might enhance existing tools used for the purpose of syndromic surveillance. Finally, limitations of the approach, broader implications for disease surveillance and informatics, and future directions for this research will be discussed.

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Last Known Position

PhD student, Biomedical and Health Informatics, University of Washington


Hannah Mandel
2013

Thesis/Dissertation Title

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Last Known Position

Applied Public Health Informatics Fellow, New York City Department of Health and Mental Hygiene


Justin McReynolds
2016

Thesis/Dissertation Title

Open-source Computerized Patient-reported Outcomes: Case Studies Illustrating Fifteen Years of Evolution

Over a fifteen year period, Patient Reported Outcomes ("PRO") applications to support over forty clinical and research projects have driven the evolution of an open-source computerized PRO system ("cPRO", http://cprohealth.org). The projects varied widely in PRO content, clinical domain, and workflows. Detailed case studies of six major implementations of the cPRO system offer a framework to understand the socio-technical challenges and opportunities in collecting computerized PROs and incorporating PROs into clinical care, patient-centered tools, and research.

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Last Known Position

Senior Computer Specialist, Biobehavioral Nursing and Health Systems, University of Washington


Hao Mei
2003

Thesis/Dissertation Title

Last Known Position

Assistant Professor at Tulane University


Michele Mehaffey
2004

Thesis/Dissertation Title

Last Known Position

Research Scientist III - Bioinformatics at University of Washington


Jon Nakashima
2005

Thesis/Dissertation Title

Last Known Position

Integration Architect, Cerner Corporation


Adam Nishimura
2014

Thesis/Dissertation Title

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Last Known Position

Medical Student, University of Washington


Jesus Peinado
2003

Thesis/Dissertation Title

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Last Known Position

Affiliate Assistant Professor, Global Health, University of Washington; Head of Statistics, Informatics, Data Management and Systems (UIDES) Unit, Partners in Health Peru


Richard Phillips
2004

Thesis/Dissertation Title

Last Known Position

Chief Medical Information Officer


Jin Qu
2017

Thesis/Dissertation Title

Predicting Cancer Outcome with Multispectral Tumor Tissue Images

Tumor tissue slides have been used by clinicians to assess cancer patient’s condition and indicate prognosis. Several studies have suggested that the distribution of important immunological biomarkers on tumor tissue slides might help predict survival outcome [1] [2] [3]. These studies rely upon non-parametric Kaplan-Meier survival analysis with Log-rank test to extract statistical insights, which, however, has several disadvantages such as prediction ambiguity and inability to directly model continuous variables.

In this study, we engineered 676 features encoding cellular distribution information from multi-spectral tumor tissue images from 118 HPV-negative oral squamous cell cancer patients. We leveraged statistical methods and predictive models to explore the predictive power of these features. 18 features were identified as potential survival predictors through Kolmogorov-Smirnov test. Our best model, random forest model, has achieved 58.54% prediction accuracy rate on independent validation dataset. Although the model does not suggest strong predictive power of selected features, evaluation on large scale training data is still needed to further tune model parameters and generate more concrete results.

Last Known Position

Research Assistant, eScience Institute


Mabel Raza
2009

Thesis/Dissertation Title

Last Known Position

Research Assistant, Bioinformatics, Universidad Peruana Cayetano Heredia


Michael G. Semanik
2016

Thesis/Dissertation Title

Clinical Phenotyping in the Prediction of Acute Kidney Injury

Acute kidney injury (AKI) is an increasingly prevalent problem amongst pediatric inpatients, and is associated with high morbidity and mortality. Unfortunately, current methods of diagnosing AKI rely on “late markers” of injury, making early identification and prevention of AKI difficult. This work describes the development of an “at risk for AKI” clinical phenotype from structured electronic health record data, and its ensuing application in a predictive model. The model performs reasonably well in predicting AKI, with an F1 score of 0.67 and AUC of 0.75. Unstructured data is then added to the model via the inclusion of n-grams derived from ICU clinician notes, which improves performance (the F1 score increases to 0.76 and AUC increases to 0.77). Thus, it is possible to use clinical phenotyping to predict the onset of AKI twenty-four hours before current markers are elevated. This approach may lead to better treatments and preventative strategies for pediatric AKI.

Last Known Position

Assistant Professor, School of Medicine and Public Health, University of Wisconsin


Andrew Simms
2007



Arvind Vasan
2008


Kai Wang
2003

Thesis/Dissertation Title

Last Known Position

Senior Principal Scientist and Informatics Lead for Protein Homeostasis TCoE at Celgene


Kelan Wang
2005

Thesis/Dissertation Title

Last Known Position

Senior Software Engineer



Jesse Wiley
2003

Thesis/Dissertation Title

Last Known Position

Assistant Medical Director, GE Healthcare


Shuyang Wu
2017

Thesis/Dissertation Title

A Bayesian Network Model of Head and Neck Squamous Cell Carcinoma Incorporating Gene Expression Profiles

Radiation therapy is a treatment for metastatic Head and Neck Squamous Cell Carcinoma, which allows precision targeting of certain groups of lymph nodes. A Bayesian network predictive model was developed aiming to help achieve such precision using information on the primary site and size of the tumor, representing the current decision-making process in clinical settings. The patient’s genetic profile was added to examine its predictability of metastasis through the improvement in prediction accuracies. The model was trained with publicly available data extracted from the Cancer Genome Atlas (TCGA) and validated against the TCGA dataset as well as clinical data reported to the University of Washington Tumor Board. Results show that genetic profile data improves model accuracy and such improvement may affect clinical decision making especially for patients with more advanced metastasis. A prototype for decision support application was built based on the results to demonstrate the clinical significance of the model. However, more data is needed to show significance of the proposed effects, as well as to improve the accuracy of the overall model.

Last Known Position

Bioinformatics Associate at Genentech


Xiyao Yang
2017

Thesis/Dissertation Title

Leaf2Tableau: From Real-Time Clinical Data to Clinical Knowledge Discovery

Leaf-to-Tableau, a self-service and real-time clinical data visualization pipeline, is designed and developed to handle data visualization requests for queries developed in Leaf, a clinical data explorer developed by University of Washington Medicine Information Technology Services. It can extract and visualize any Leaf datasets into a portable format that researchers can easily explore without needing a highly technical or statistical background, providing a quick visual summary of the target population. This completes a CDW self-service model with a researcher constructing a query to identify a specific patient cohort in Leaf and subsequently developing custom visualizations for exploration or publication, as well as receiving in return data files for analysis.

Last Known Position

Web Developer at University of Washington Medical Center, Angular4 learner


Postdoctoral Trainees

Barry Aaronson
2009

Last Known Position

Hospitalist , Associate Medical Director for Clinical Informatics, Virginia Mason Medical Center


Peter Anderson
2009

Last Known Position

Assistant Professor, UW Bothell


Uba Backonja
2016

Last Known Position

Assistant Professor in Nursing, UW Tacoma


Aaron Chang
2004


Christine Dean
2008


Christoph Eick
1999

Last Known Position

Associate Professor, and Director UH Data Analysis and Intelligent Systems Lab (UH-DAIS) Department of Computer Science, University of Houston


Carol Farris
2011

Last Known Position

NuGEN Technologies


Mandi Hall
2015

Last Known Position

Sr User Researcher, Microsoft Health, AI and Research; Affiliate Assistant Professor, UW Department of Biomedical Informatics and Medical Education


Claire Jungyoun Han, PhD, MSN, RN, CCRN

Last Known Position

Postdoctoral Fellow, BCPT Cancer Fellowship (Biobehavioral Cancer Prevention and Control Training Program), Department of Health Services, University of Washington


Jina Huh
2013

Last Known Position

Assistant Professor at University of California, San Diego


Christi Inman
2009

Last Known Position

Assistant Professor in Pediatric Rheumatology at the University Of Utah School Of Medicine


Sandra Johnston
2007

Last Known Position

Data Manager, University of Washington Neuro-Oncology Research


Predrag Klasnja
2012

Last Known Position

Assistant Professor of Information, School of Information; Assistant Professor of Health Behavior and Health Education, School of Public Health, University of Michigan


David Kwan
2004


Donna Lavallie
2004


Wayne Liang, MD
2017

Thesis/Dissertation Title

User-Centered Design of a Collaborative Genetic Variant Interpretation Tool

Precision genomic medicine relies upon accurate variant knowledge. However, laboratories continue to arrive at discordant interpretations for the same genomic test. Gaps, inconsistencies, and siloing of variant knowledge may contribute to inter-rater discordance in variant interpretation. Our overall goal is to develop a novel, openly available computerized tool supporting role-based collaboration, knowledge sharing, and consensus-making in variant interpretation. In Aim 1, we use literature review and informal expert input to characterize a typical variant interpretation workflow, propose a collaborative workflow, and develop an initial design for a computerized tool supporting collaborative variant interpretation. In Aim 2, we use user-centered design methodology to further characterize the typical workflow, define project requirements and user needs, and finalize the design of a tool supporting collaborative variant interpretation.

Last Known Position

Assistant Professor in Pediatrics, University of Alabama at Birmingham


William Lober
2001

Last Known Position

Professor, Biobehavioral Nursing and Health Systems Joint Professor, BIME Joint Professor, Global Health Adjunct Professor, Health Services


David Masuda
1999

Last Known Position

Adjunct Lecturer, Health Services, School of Public Health, University of Washington


Andrew Miller
2016

Last Known Position

Asst Professor in Human-Centered Computing, IUPUI


Peter Mork
2004

Last Known Position

Chief Data Engineer for Public Health Innovation at MITRE


Carolyn Paisie, PhD
2017

Thesis/Dissertation Title

RNAseq and Ribosome Profiling Generate New Insights into Leishmania Differentiation

Leishmania donovani, an intracellular parasitic trypanosomatid, causes kala-azar, a fatal form of visceral leishmaniasis in humans. Infection occurs through a cyclical cycle whereby parasites living in the midguts of female sandflies (promastigote stage) are transferred to the host via a bite from an infected female sandfly, are phagocytosed by human macrophages, and are then transferred to phagolysosomes of human macrophages (amastigote stage). Previous studies have demonstrated that L. donovani differentiation is regulated by changes in gene expression. Thus we have performed high throughput RNA sequencing (RNA-seq) to elucidate changes in transcript abundance for all cellular mRNAs during L. donovani differentiation from promastigotes into amastigotes. Analyses revealed gene expression changes which may affect posttranscriptional and translational processes during differentiation.


Leslie (Dean) Poppe
2014

Last Known Position

Clinical Psychologist, The Everett Clinic


Francisco Saavedra
2012

Last Known Position

Physician informaticist


Imre Solti
2010


Maile Taualii
2007

Last Known Position

Professor at University of Hawaii


Lisa Taylor-Swanson, PhD
2017

Last Known Position

Assistant Professor, College of Nursing, University of Utah


Lauren Thorngate
2014

Last Known Position

Executive Director Professional Development at Care New England


Herman Tolentino
1998

Last Known Position

Informatics Health Scientist, CDC


Ravensara Travillian
2013

Last Known Position

Research Scientist, University of Washington


Lisa Trigg
2005

Last Known Position

Attending ARNP at Fairfax Behavioral Health


Kent Unruh
2005

Last Known Position

Project ECHO Administrator at University of Washington


Erik Van Eaton

Last Known Position

Chief Clinical Officer at TransformativeMed, and Trauma Surgeon & Associate Professor at Harborview Medical Center


Lisa Vizer
2015

Last Known Position

Research Assistant Professor, University of North Carolina at Chapel Hill


Kasia Wilamowska
2011

Last Known Position

Resident Physician, The University of New Mexico Health Sciences Center