Nick Anderson

Subgroup: Institute of Translational Health Sciences

 This project is developing a user focused resource for cohort discovery and data research for UW patient populations. The DCDR research portal provides users authenticated views of deidentified population characteristics to support prep-to study work, and uses the i2b2 (informatics for integrated bedside and biology) open source framework and the UW Amalga clinical data repository.

This work is led by Nick Anderson, as part of the ITHC Biomedical Informatics core, and includes Peter Tarczy-Hornoch as a co-PI. 

Subgroup: Institute of Translational Health Sciences

 This project is developing a comprehensive patient identification, consent, specimen capture and distribution system to advance work cancer research. This project combines clinical informatics semantic modeling with translational informatics workflow management, and occurs in partnership with the Fred Hutchinson Cancer Research Center and Seattle Children's Research Institute. This project involves broad issues of capturing patient consent and is the foundation for the work of the Washington Phenotype Biospecimen Resource (WPBR).

This work is led by Nick Anderson, as part of the ITHC Biomedical Informatics core, and includes Peter Tarczy-Hornoch as a co-PI.  

This collaborative information exchange pilot project will adapt and extend data discovery tools and processes to enhance research design and retrospective data study capabilities for clinical translational investigators. The novel approach of this project will be to incrementally build a common technical, semantic and appropriately secure and governed distributed system in close partnership with active researchers at three large and geographically distributed academic medical centers. This collaboration will extend the Informatics for Integrating Biology and the Bedside (i2b2) software architecture developed by the Harvard based National Center for Biomedical Computing (NCBC) to support multi-institution data query capabilities.

This work is led by Nick Anderson, as part of the ITHC Biomedical Informatics core, and includes John Gennari as a co-PI. The work is being carried out collaboratively with UC-Davis, UC-San Francisco, and Harvard University.

Subgroup: Institute of Translational Health Sciences

Subgroup: Institute of Translational Health Sciences

This project is developing a state-wide research portal and analytic environment that will provide novel access to the Health Share Montana Health Information Exchange Environment.  This uses the i2b2 (informatics for integrated bedside and biology) and is a collaboration with the UW Department of Bioethics and Humanites (Edwards) and Department of Family Practice Medicine (Baldwin)

The informatics work is led by Nick Anderson, as part of the ITHC Biomedical Informatics core.

Jim Brinkley

Symbolic representations for anatomy, as exemplified by the Foundational Model of Anatomy, are combined with spatial or image-based representation in a distributed information system. Various structural information servers access the spatial and symbolic resources, and various authoring and end-user client programs implement several applications. Among these applications are a foundational model explorer (FME), a graphical query engine to the FMA called Emily, a StruQL-based query engine for the FMA called OQAFMA, a natural language front-end that composes StruQL queries, a Lisp-based FMA server, a web-based image manager, FMA-based image retrieval, interactive atlases of anatomy, and a dynamic 3-D scene generator.

Sub-projects include: BodyGen. Digital Anatomist Atlases, Digital Anatomist Jigsaw Puzzle, Dynamic Scene Generator, Image Manager WIRM Repository, Skandha4, Who Wants to Be A (Digital) Anatomist?, WIRM, and Biolucida. See web pages for more details.

Sub-projects include: Emily, Foundational Model Explorer, Foundational Model of Anatomy DB, GAPP Server, NOQAFMA, OQAFMA DB, OQAFMA Server, and WebGAPP.

An on-line information system for managing and visualizing data about the human brain. In this case a structural model is used as a framework for organizing other information.

Related subprojects: Brain Browser Web Interface, Brain Map WIRM Repository, Brain Visualizer Graphics Server, Single-Cell Recording EMS, X_Batch, XBrain and MindSeer.

Valerie Daggett

The Dynameomics project utilizes molecular dynamic simulations to characterize the dynamics and folding / unfolding pathways of representative proteins from all known protein folds (www.dynameomics.org).  To date, this project has resulted in hundreds of terabytes of information from over 11,000 simulations of over 2,000 proteins.  From this data, we are extracting biologically relevant information for new approaches to areas such as protein folding and drug design.  To this end, we are developing and employing an array of data mining and analysis techniques.

The data mining techniques developed in the Daggett lab include flexibility analysis of protein atoms, wavelet analysis of atomic motion, and graph representations of local chemical environments.  These techniques give us "data-scopes" that help us understand what is happening within the molecule at sub-angstrom and sub-picosecond resolutions.  We are then able to apply this information to domains such as protein folding and drug design.  For example, understanding the atomic-level behaviors of proteins helps us create libraries of protein fragments that are being used to better understand how proteins move through their folding pathways.  Similarly, understanding these forces at these resolutions helps us to understand the behavioral differences between wild-type proteins and the mutant proteins associated with disease.  Once we understand these differences, we are better able to design drugs and other interventions to interfere with the negative behaviors and either deactivate the mutant or rescue it and return it to its wild-type functionality.

Our goal is to perform realistic molecular modeling studies relating to protein stability, function, and folding. Protein folding is one of the fundamental unsolved problems in molecular biology. A protein must assume a stable and precisely ordered conformation to perform its biological function properly. Although much is known of the structural details of the native folded conformation of proteins, very little is known about the actual folding process.

An understanding of protein folding has important implications for all biological processes, including protein degradation, protein translocation, aging, and human diseases, including cancer and amyloid diseases. The solution to the protein folding problem also has applications in the human genome project and biotechnology. Given that protein folding is of such widespread importance to human health and the fact that experimental approaches only provide limited amounts of information on the structural transitions and interactions occurring during protein folding, we are using computer simulation methods to characterize this process.

Bioinformatics methods are playing an increasingly important role in this area. We have just finished performing realistic, atomistic simulations of representatives of all protein folds---an effort we are calling Dynameomics. We have developed a novel hybrid relational / multidimensional database to house the multi-terabyte datasets; this is the largest collection of protein simulations and protein structures in the world. We are actively working on mining this very rich dataset to undercover the general rules of folding and for drug and protein design applications. This entails design and development of novel mining methods and a new emphasis on visual analytics. See below for more information.

The Dynameomics project contains a vast amount of complex data organized in a Microsoft SQL data warehouse.  We are constantly expanding our data warehouse to incorporate new analyses as well as stay on the leading edge of database technologies. Currently we are investigating multi-dimensional data storage and analysis through OLAP.  We are also developing novel approaches to dynamic, interactive data visualization through the use of the graphical user interfaces, real-time video game quality rendering engines, and hardware accelerated data analysis.

George Demiris

The aim of this project is to utilize an informatics platform to assess multiple components of wellness for community dwelling older adults and visualize health related information to support shared decision making.

Transforming Community-Based Elder Care through Heterogeneous Activity Sensing Analytics

This project aims to  (1) demonstrate the first integration of multiple infrastructure-mediated sensing platforms, (2) develop a new ultralow-power mechanism for direct sensing in the home, (3) develop new streaming data and query models addressing the noisy, low-level nature of home activity sensing data, (4) develop scalable support for these new models in eldercare, and (5) develop novel combinations of example-based training and direct specification for effective end-user interaction.

The aim of this project is to study the design, implementation and evaluation of smart home technologies that aim to support older adults, increase their independence and quality of life as they age in place. Funding agencies: NSF, NIH, Administration on Agin.

Sherrilyne Fuller

John Gennari

Ricordo (Italian for memory) is a sub-project within the European Commission's Virtual Physiological Human (VPH) project focused on improving the sharing and reuse of physiological models and data.

The larger VPH project is a network of international researchers, primarily in Europe, developing computer models and simulations of human physiology. These models will use the physiological data of individual patients to run simulations that will test potential personalized medical therapies. As part of the Ricordo team, we are developing a semantic framework for linking biosimulation models and biomedical data. Our research will help VPH modelers more effectively search for, share, and reuse their models and data.

The University of Washington Ricordo team - Dr. Daniel Cook, Dr. John Gennari, Onard Mejino, MD and Dr. Maxwell Neal work in close collaboration with a research group at the University of Cambridge, UK to develop new software for the VPH project.

The broad goal of this research is to determine the feasibility of, and create prototype software for, a library of reusable synthetic biology components using Semantic Web technologies OWL, RDF, SPARQL and SWRL. This library will dramatically increase the availability of information about reusable components for synthetic biology, including machine-readable descriptions of the semantic meaning of the components. Such information, amenable to automated reasoning, intelligent retrieval, and other services, will increase the utility of the synthetic biology library for researchers as well as for commercial vendors engaged in designing synthetic biology products. Such a library will hasten the industrialization of synthetic biology, with a host of resulting benefits to public health, including better "living machines" designed and manufactured more quickly and efficiently.

This project is in collaboration with Clark & Parsia, LLC, the makers of the Pellet reasoning system for OWL description logic representations.

In this project, we pursue ontology-based informatics research that focuses on the representation and use of process knowledge within biomedical applications. Our primary driving use case is with bio-simulation systems. We collaborate closely with bioengineers who build simulation models of biology ranging from blood flow to pathway-level molecular processes.

A primary work product has been the Ontology of Physics for Biology, as published in the BioPortal collection of biological ontologies. See our project home page for more information, including our project's publications to date.

John Gennari and Daniel Cook co-lead this project.

I am mentoring Wynona Black, who has acquired a unique, very large (> 6 million patients), 9-year patient database derived from EMR data from 128 health care institutions across the United States, exclusivey focused on Acute Coronary Syndrome (ACS) patients (heart attack and Angina).  Together, we propose to use data mining methods to explore and analyze practice patterns and to create a predictive model of outcomes for ACS patients.

We plan to integrate expert knowledge (from clinical practice guidelines, and from direct interaction with cardiology specialists) with data mining results. We also plan to use use our 9-yr EMR data source to discover temporal associations and build predictive models of patient outcomes. If successful, the long term goals are to elucidate clinical practice patterns and to create easy to use predictive models that will potentially aid clinical care providers in selecting treatment(s) that lead to optimal patient outcomes.

Ira Kalet

The Prism Project is a long term development project to build a base for exploration of new ideas in radiation treatment. It models individual patient anatomy, radiation machinery, and is currently being extended to model the biological effects of the radiation on tumors and normal tissue. It has been used for testing of knowledge based automated planning techniques, as well as providing a context for eventual testing of the results of the Clinical Target Volume project.

Peter Myler

Our current research is focused on using genome-wide laboratory and bioinformatics-based approaches to study regulation of gene expression in protozoan parasites. This work has been greatly enhanced by the recent completion of the trypanosomatid genome sequence, in which we played a leadership role. One NIH-funded sub-project seeks to characterize the molecular machinery involved in the unique polycistronic transcription of protein-coding genes in trypanosomatids. This involves both wet lab (molecular genetics, microarray, proteomics, EMSA, ChIP) and computational (sequence analysis, motif identification, microarray analysis) approaches.

A second sub-project uses DNA microarrays to identify changes in gene expression during differentiation of Leishmania between the insect and mammalian stages. As part of the Trypanosomatid program at SBRI, Dr. Myler is also actively involved in the discovery of new drugs against trypanosomes and Leishmania, as well as development of improved diagnostics for infection with these parasites.

Wanda Pratt

As clinicians are forced to decrease time spent with patients and the specialization and fragmentation of care increases, patients are required to play an increasingly prominent role in their health care. Yet, few information tools exist to support patients in this active role. In particular, patients often must coordinate their health care across multiple clinicians, learn new health terminology, make treatment choices, manage their home care, track insurance benefits, etc.

At the same time, patients are trying to maintain their normal professional and personal lives, but the intense information management demands placed on them can interfere with all those activities. Little is known about this information management work of patients, but such knowledge must be a first step towards developing the tools that patients need to support their active role. The long-term objective of this research is both to understand patients�| information management work and to develop new technology that will support that work. Specifically, we propose to (1) develop a model of patients�| personal health information management work, (2) develop new technology that supports patients in that work, and (3) evaluate the effectiveness of our new technology in helping patients manage their personal health information, participate in their own health care, and maintain their daily life activities.

Funded by the National Library of Medicine.

Many studies have pointed to the importance of quality patient-clinician communication during clinic visits and its positive effect on a variety of valued outcomes, such as patient satisfaction, adherence to treatment plans, and quality of life. However, few researchers have investigated the potential for novel technologies to support both patients and clinicians in collaborating to make those clinic visits highly effective. In particular, patients and clinicians often come to those visits with their own agendas but with limited tools to help them share those agendas with each other. Neither is there support for patients in preparing for, participating in, or processing information after clinic visits. In this grant, we propose to address that gap. Specifically, we propose to (1) determine how cancer patients, clinicians, and others collaborate for clinic visits, (2) develop technology to support enhanced collaboration for visits, particularly through shared agenda creation, (3) develop technology to support capture and retrieval of information from visits, based largely on those shared agendas, and (4) evaluate the effectiveness of our collaborative technologies. The long-term impact of our work will be to improve patient-provider communication, help patients become informed and active partners in their care, and ultimately improve overall health care.

Our goal in this project is to leverage mobile technology and Microsoft HealthVault to help heart-disease patients lead heart-healthy lifestyles. We are using mobile phones and wearable sensors; to study contextual factors that shape the behavior change process of heart-disease patients. This information is stored in HealthVault to enable data trending, exploration, sharing, and use in combination with patients' other HealthVault records. In this project, we focus on patients who have had a cardiac event within the last five years and are now trying to incorporate health-related behavior changes into their daily life. As patients' symptoms diminish and the demands of their daily lives return to their previous levels, heart-disease patients require additional support to avoid relapse and sustain heart-healthy lifestyles. To address these problems, we are developing novel mobile and web-based prototypes that encourage opportunistic engagement in healthy activities throughout the day. Our prototypes also enable patients to share their lifestyle information with selected members of their social network to seek social support and negotiate shared activities. Together, the support for collaboratively negotiated social routines and small daily decisions about healthy activities could enable creation of a sustainable, heart-healthy lifestyle.

Funded by Microsoft's Be Well Fund Award.

Online health communities offer a broad base of patient expertise, yet people can miss out on this valuable resource because, all too often, patients struggle to identify appropriate experienced patients and yields advice that doesn’t quite fit one’s personal situation or context. In this project, we aim to overcome this challenge by applying social matching as a technical and intellectual framework for connecting peers for mentorship in online health communities. In partnership with an online cancer community, we will conduct a series of three studies that: (1) Identify critical mentorship characteristics through a comparative study of automated text extraction versus direct user entry of data, (2) Develop tools that connect peers for mentorship through rapid prototyping and usability testing with cancer patients, and (3) Assess the value of matching for peermentoring through an online community-based field evaluation. Matchmaking among individuals with sharedcircumstances helps newcomers glean tailored insights into ‘what to expect’ and helps those with experience to share their valuable expertise by ‘giving back’. Thus, our work will guide future research and services to enhance the health and wellbeing of citizens by empowering peer mentorship.

Collaborators:

Andrea Hartzler http://staff.washington.edu/andreah/
David McDonald http://www.pensivepuffin.com/dwmcphd/index.html

Linda Shapiro

The goal of this work is to provide tools for the study of craniofacial anatomy from either CT scans or from a 12-camera active stereo photogrammetry system. Our image-analysis tools provide low-level operators for working with 3D craniofacial data. Our feature extraction tools produce quantitative representations (descriptors) of the 3D data that can be used to summarize the 3D shape as pertains to the condition being studied and the question being asked. Our similarity-measure tools compare the data from two individuals (or between an individual and the average of a population) and produce a numeric similarity score. Our organizational tools provide an image indexing mechanism that allows rapid retrieval of all images in a database that are similar to a query image in order of similarity. Our user-interface tools allow users to easily pose queries specifying both relational constraints on alphanumeric data and similarity constraints on image data in order to find subjects in a database who are most similar to a query subject with respect to studies from multiple different imaging modalities.

We will apply our tools for analysis, description, similarity measurement, organization and shape-based retrieval to a database of patients with midface hypoplasia and cleft lip and/or palate as part of the NIH/NIDCR FaceBase Consortium.

This research is supported by the National Institute of Dental and Craniofacial Research.

Peter Tarczy-Hornoch

Health Informatics Core - Surgical Care and Outcomes Assessment Program (SCOAP) Comparative Effectiveness Research Translation Network (CERTN) 

This project is developing infrastructure and improving the methodology for collecting prospective data from electronic medical records through SCOAP (the Surgical Care Outcomes Assessment Program) to generate new evidence on the comparative effectiveness of healthcare interventions and drive clinical quality improvement in vascular disease. The work involves deployment of the Amalga UIS system across ~10 SCOAP hospitals and clinics in WA and then using the system to prospectively compare outcomes. Informatics research relates to data integration and text mining. SCOAP CERTN is funded AHRQ (Dave Flum PI). Faculty: Peter Tarczy-Hornoch (Director), Meliha Yetisgen-Yildiz, Erik Van Eaton, Beth Devine. Students: Daniel Capurro (graduated Summer 2012)

Description: The focus of the Washington Phenotyped Biospecimen Resources (WBPR) is to develop a resource to provide high-quality, information-rich biological samples to researchers and startup companies statewide.

Progress in basic and translational research on human disease depends increasingly on access to samples of fluids (e.g., blood) and tissue ("biospecimens") from large and diverse populations. Obtaining such specimens can be complicated and time-consuming for scientists and their research organizations. WPBR proposes to create an infrastructure to make the process simpler and more efficient for those patients who wish to contribute to medical research. All specimens will be “clinically annotated,” meaning that they will be linked to biologically relevant data on the donors. The WPBR will: (1) make existing specimens available to biomedical investigators at non-profit and for-profit organizations throughout Washington, (2) facilitate collection and widespread distribution of new specimens specifically for research, (3) allow specimens from a single individual to be obtained at multiple time points, and (4) facilitate identification of consenting patients for inclusion in clinical trials. The focus on of the informatics research is on  clinical data repositories and Honest Broker systems and research workflow support. The WPBR overall is funded by a WA State Life Sciences Discovery Fund (John Slattery PI). The WPBR complements the Cancer Biospecimen Resource that Dr. Peggy Porter of the Fred Hutchinson Cancer Research Center is developing under an LSDF 2009 program grant. WBPR Informatics Faculty: Peter Tarczy-Hornoch (Core Director), Nick Anderson, Dave Chou. Students: none thus far (project initiated Fall 2011).

The goal of the Biomedical Informatics Core (BMI) of the Institute of Translational Health Sciences (ITHS, the regional CTSA) is to enable clinical investigators to effectively use electronic biomedical data to advance their research. To achieve this end the core engages in foundational and applied research and delivers services. The first aim of the core is to provide evolving BMI services and resources to a broad range of investigators: a) expanded access to EHR data for research, b) secure and effective use of EHR data via an “Honest Broker" environment, c) tools and training to support electronic data capture, d) expanded tools and resources for acquiring and managing biospecimens, e) expanded support for data integration, and f) consultation and training for these  resources. The 2nd aim of the core focused on new informatics models and tools to help researchers using two driving classes of research (genomic research and Comparative Effectiveness Research (CER)): a) developing and deploying cross-institutional data integration infrastructures, and b) phenotype extraction particularly via text mining.  The ITHS overall is funded by NCRR (Nora Disis PI). ITHS BMI Core Informatics Faculty: Peter Tarczy-Hornoch (Core Director), Nick Anderson (Core Associate Director), Jim Brinkley (Core Associate Director), Kari Stephens. Postdocs: Ping Lin. Students: Daniel Capurro (graduated Summer 2012).

Description: (from overall NIH grant proposl, Gail Jarvik PI): Since the completion of the Human Genome Project in 2001, there have been great expectations for translating human genomic information directly into clinically practice. During the last several years, numerous large studies have cataloged human DNA variation. In parallel, advances in DNA sequencing technologies have increased the throughput and decreased costs. We are now positioned to broadly deploy our knowledge of human genetic variation, coupled with high-throughput DNA sequencing methods, for individualized, large-scale “medical resequencing” to comprehensively reveal the genetic mechanisms underlying disease and influence clinical treatment. This “base pairs to bedside” translation requires multidisciplinary study. The University of Washington is a leader in clinical genetics (Bennett, Burke, Byers, Hisama, and Jarvik, Motulsky, Raskind, and Sybert), bioethics (Burke, Jarvik, Fullerton, and Trinidad), second-generation sequencing, variant calling and annotation (Rieder and Nickerson), disease gene discovery (Browning, Heagerty, Jarvik, Nickerson, and Rieder), biomedical informatics (Tarczy-Hornoch), and health services research (Heagerty, Patrick, Regier, and Veenstra). In this highly integrated proposal, we combine these strengths to investigate aspects of using exomic data clinically. We propose a randomized controlled trial of usual care vs. the addition of exome analysis in University of Washington Medical Genetics Clinic patients who have clinical indications for colorectal cancer/polyposis (CRCP) genetic testing. We will evaluate the effectiveness of this technology for the identification of clinically relevant CRCP gene mutations, cost, and patient derived measures. After deliberations by experts to indentify variants that are incidental findings that should be returned, we will also return CLIA certified results to the participants. We will obtain structured feedback from subjects in both the usual care and exome arms of the RCT to evaluate their experiences. We will further consider the input of referring physicians and patients using focus groups. We will investigate the legal basis of the need to return CLIA certified research results. An important component of our work is determination of not only which results to return, but how best to incorporate these genomic data into the medical record. Finally, we will perform CRCP gene discovery studies for families without identifiable CRCP mutations; such novel gene discovery can impact prevention and treatment. The informatics research centers on integration of genomic data into the medical record in the context of genomic medicine. The work overall is funded by a NIH U01 grant (Gail Jarvik PI). Informatics Faculty: Peter Tarczy-Hornoch. Students: Cheryl Lee

Jim Tufano

 My Ph.D. dissertation research focused on user experiences with the uses and effects of EMRs, patient web portals, electronic health risk appraisals (HRAs), and secure patient/provider email communications in patient-centered practice redesign initiatives based on the Patient-Centered Access and Wagner’s Chronic Care/Patient-Centered Medical Home care models.  Outgrowths of this research include my studies of online primary care/specialty care provider consultation via their collaborative uses of EMRs that offer integrated provider-to-provider clinical messaging features.  I’ve also continued to work as a member of the Medical Home Evaluation Team at Group Health to study that organization’s experience with enterprise-wide replication of their prototype PCMH transformation model using Lean methodologies applied to clinical work and facility redesign. Recently I have also begun collaborating with investigators from Penn State Hershey Medical Center and the University of Pennsylvania who are studying a large-scale PCMH transformation initiative in southeastern Pennsylvania.  

 My current work conducting deployment planning, user studies, and design research for the GroupHealthMobile iPhone app builds on my prior and ongoing work with colleagues at Group Health Cooperative, the University of Washington, and the Norwegian Centre for Integrated Care and Telemedicine/Tromsø Telemedicine Laboratory. As a graduate student, in 2005 I published my first paper in this domain, highlighting opportunities to leverage global trends in mobile phone adoption in approaches to addressing the obesity epidemic. I also served as a key contributor to the development and evaluation of two smartphone applications designed for use by people living with diabetes – one intended for use in collaboration with healthcare providers, and the other designed primarily for pure self-care uses.  I have continued to study the design, user experiences with, and effects of smartphone applications integrated with patient web portals (“tethered mobile personal health applications”) as components chronic disease self-management support interventions.  Populations I continue to work with most extensively include people living with diabetes, hypertension, and/or HIV.

 Prior to entering graduate school in 2004, I worked with colleagues at Group Health Research Institute and the NCI’s Breast Cancer Surveillance Consortium as a contracted consultant to develop and evaluate a tablet PC-based mammography clinic patient intake form dual-purposed to support both patient care and breast cancer risk detection research.  I have continued my collaborations with several of these colleagues, most recently as co-PI of an NCI grant proposal to design ICT and ICT use cases that support patient notification and follow-up to abnormal mammography screening results (resubmission in process).  Since 2008 I have also worked with UW colleagues on PROMIS-, NIMH- and NIAID-funded studies of the design, clinical integration, and effects of a point-of-care tablet PC application used to elicit patient-reported outcomes (PROs) and other measures related to HIV care and research.

Subareas:  Clinical informatics, chronic care, patient-centered care

Patients using some automated digital oscillometric blood pressure monitors (OM) can measure their blood pressure (BP) as accurately as clinicians using mercury manometers and proper technique.  These OM devices also capture BP measures as discrete electronic data and can potentially be interfaced with EMRs to reduce data entry errors, either directly or via patient Web portal extensions of EMRs. But despite their potential advantages, the medical community has not widely adopted OM, in part because clinicians are uncertain of their accuracy and optimal uses, and are skeptical of patients’ abilities to use them correctly. The aims of this study are to develop and test “plug and play” OM device/EMR interfaces; step-by-step patient and clinician protocols for their proper uses and maintenance; and reference use case models for their efficient integration into routine primary care and patient self-care workflows. Achieving these aims will provide us with a foundation for conducting subsequent large-scale trials evaluating the comparative effectiveness, efficiency, and end-user (patient and provider) experiences with ICT-enabled HTN management interventions that include both clinic-based and home-based patient-performed BP measurement components.  Other related projects in which I am serving as a co-investigator include studies of community-based pharmacist-supported HTN detection and patient self-management support interventions that utilize both retail portable OM and stationary BP measurement kiosks interfaced with smartphones, patient Web portals, pharmacy information systems, and EMRs.

Health risk appraisals (HRAs) are interactive web survey applications typically administered by employers or insurers to assess individuals’ health habits and risk factors, estimate their risk of experiencing adverse health outcomes, and provide tailored feedback to promote healthy behaviors.  Although using HRAs as components of primary care-based interventions represents a significant departure from their conventional use cases and contexts of administration, emerging evidence demonstrates that they can be effective when deployed along with health education and enhanced access to healthcare services.  Such “HRA plus” interventions exhibit evidence of effectiveness in promoting beneficial health behavior changes in many domains including physical activity, seat belt use, dietary intake of fat, tobacco use, total cholesterol, at-risk alcohol use, overall blood pressure, and appropriate utilization of various preventive healthcare services.  I initiated my work in the domain of HRAs and electronic health surveys as a graduate student.  This provided a foundation for my more recent work with HRAs as a faculty member.  I collaborated with colleagues from the UW and the Washington State Department of Health from 2009-2011 to develop and evaluate an email-administered HRA comprising questions drawn from Behavioral Risk Factor Surveillance System instruments.  We designed this HRA to serve as the technology component of a prototype “HRA plus” intervention for integrating clinical preventive services with primary care.  I hope to continue my work in this domain in collaboration with colleagues from the Group Health Research Institute, with whom I recently developed a proposal to design a prototype patient-controlled preventive health reminders and notifications system (resubmission in process).

Using some automated digital oscillometric blood pressure monitors (OM), patients can measure their blood pressure (BP) more accurately than clinicians using mercury manometers and proper technique.  These OM devices also capture BP measures as electronic data and can potentially be interfaced with EMRs (either in clinic or remotely using patient web portals) to reduce data entry errors. But the medical community has not widely adopted OM, in part because clinicians are uncertain of their accuracy and optimal uses, and are often skeptical of patients’ abilities to use them correctly. The aims of this study are to develop and test “plug and play” OM device/EMR interfaces; develop user-centered patient-directed protocols for the proper uses and maintenance of EMR-interfaced OM both in clinic and at home; and develop and validate reference use case models for the efficient integration of these protocols and technologies into routine primary care and patient self-care workflows. Achieving these aims will provide us with a foundation for conducting subsequent large-scale trials evaluating the comparative effectiveness, efficiency, and end-user (patient and provider) experiences with ICT-enabled hypertension (HTN) management interventions that involve both clinic-based and home-based patient-performed BP measurement. My other related projects include studies of community-based pharmacist-supported HTN detection and patient self-management support interventions that utilize both retail portable OM and stationary BP measurement kiosks interfaced with smartphones, patient web portals, pharmacy information systems, and EMRs.

Anne Turner

Machine Translation to Improve Access to Multilingual Health Information

The goal of this research is to increase the availability of multilingual public health information for LEP populations through advancing domain-specific machine translation technology. Current translations of consumer health materials are generated manually at great expense. This project involves developing novel domain-specific natural language processing and machine translation (MT) technology and evaluating its impact on the process of producing multilingual consumer health materials. The long term goal of this project is to expand the availability of health information for LEP populations while maintaining accurate, readable content.

I am involved in several projects using detailed qualitative ethnographic methods to investigate the information workflow of public health activities of local health department throughout the Northwest. Areas of local public health practice research have included communicable disease reporting, emergency preparedness and chronic disease prevention.

I am involved in several projects using detailed qualitative ethnographic methods to investigate the information workflow of public health activities of local health department throughout the Northwest. Areas of local public health practice research have included communicable disease reporting, emergency preparedness and chronic disease prevention.

The major goal of this CDC funded project is to increase and improve bystander CPR among groups with limited English proficiency (LEP).  We are using GIS technology to identify interventions for LEP groups living in areas with historically high rates of cardiac arrest and low bystander CPR.  The main objective of this phase of the grant is to develop culturally appropriate educational targeted interventions to increase Chinese and Latino communities' awareness and knowledge of bystander CPR.

Self-efficacy Theory and Adult Learning theory

The goal of this AHRQ funded project is to develop a simulation-based training intervention for 911 dispatchers based on Self-efficacy Theory and Adult Learning Theory. The randomized controlled trial will test whether simulated instruction can improve the recognition and response of cardiac arrest calls by 911 dispatchers in King County, Washington.

Fred Wolf

The ITHS Institute for Comparative Effectiveness Research (CER) Training and Web-based Toolkit

This project will: 1) Develop curriculum and hands-on training activities for a weeklong summer Institute for Comparative Effectiveness Research Training (ICERT) designed to educate clinical and translational scientists at all levels of career development, including a train-the-trainer component to promote dissemination and adoption of CER in our partner communities, 2) Develop, implement, and evaluate a state-of-the-art CER grand rounds lecture series, and 3) Disseminate the educational materials broadly in the form of a web-based electronic toolkit that will be easily accessible throughout the WWAMI and American Indian-Alaska Native (AI/NA) communities, as well as nationally, including the CTSA national repository.

Update of Cochrane Collaboration Systematic Review and Meta-analysis of Asthma Self Management Interventions for Children and Adolescents

This project will update a 2003 International Cochrane Collaboration Systematic Review (Cochrane Database of Systematic Reviews, 2003 (1), CD000326), also published in the British Medical Journal (BMJ, 2003; 326 (14 June): 1308-1309). This systematic evidence review and meta-analysis (SR/MA) of randomized and controlled clinical trials of asthma self-management education programs are designed to estimate their effectiveness on four types of health outcomes in children and adolescents: 1) Lung function (FEV1 and Peak Flow), 2) Asthma morbidity (asthma exacerbations, days of school absence, days of restricted activity, nights disturbed by asthma, asthma severity), 3) Health care utilization (physician and emergency department visits, hospitalizations), and 4) Self-reported perceptions of self-care abilities (self-efficacy). This updated SR/MA will allow us to incorporate newer evidence to perform more valid and reliable subgroup analyses that are potentially important in designing new programs, such as effectiveness related to 1) Degree of asthma severity, 2) Time of enrollment in the intervention, 3) Type of self-management strategy, 4) Intervention type (individual vs. group), 5) Intensity of intervention  (single vs. multiple sessions), and 6) Study quality.

Faculty: Fred Wolf plus colleagues at the University of Michigan (Drs. Cyril Grum and Noreen Clark) and University of Pennsylvania (Dr. Jim Guevara). Students: Project can provide opportunities for interested graduate students and post-docs.

Effectiveness of patient-centered technology on medication errors during intensive chemotherapy treatment

This project will develop, pilot test, and evaluate a computerized patient-centered documentation system to track the accuracy of dosages and frequency of drug administration during complex, intensive, in-patient chemotherapy treatment regimens in an effort to reduce potential medication administration errors. In addition, the project will record and document associated medication reactions and side effects, and evaluate patients’ and providers’ (physicians, nurses) opinions and satisfaction with the system in accurately tracking treatment and reactions, and in preventing potential errors.

Faculty: Fred Wolf, Tom Payne, Paul Sutton; Students: Project can provide research opportunities for interested graduate students and post-docs.

Meliha Yetisgen-Yildiz

Accessibility to patient data available in EMR systems is critical to improve health care process. However most patient information is represented in free-text form and textual information is difficult for automated approaches to reliably access. A representation step is required to convert the unstructured information available in free-text into a structured format so that the automated approaches can work on. In this project, we focus on extraction of medical entities (e.g. medical problem, treatments, tests), temporal phrases, and their relations.

Communication of recommendations and abnormal test results is prone to error. If important imaging findings and recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. In this project, we investigate NLP and supervised classification approaches to identify critical recommendation sentences in radiology reports.

Clinical and translational research involving critical illness phenotypes relies heavily on the identification of clinical syndromes defined by consensus definitions (e.g. pneumonia, sepsis, acute lung injury). The overall goal of this project is to apply natural language processing, machine learning, and network analysis to develop an automated screening tool that accurately identifies critical illness phenotypes and their interactions among ICU patients.

Subgroup: The major aims of NWIGM include to 1) enhance the capabilities of investigators to carry out cutting edge research on the etiology, prevention, and treatment outcomes of common human disease by developing a large repository linked to detailed clinical data; improving access to quality electronic medical records and coordinating and increasing access to existing human subject approval and sample collection resources; coordinating and increasing access to existing and developing high-throughput genotype, sequencing, and proteomics services; providing new resources for study design, data-basing, and analysis,2) Initiate outcomes research on the clinical use of genetic technology, 3) Assist in the protection of research subjects and explore the ethical  delivery of genetic medicine, 4) Facilitate communications among the faculty, trainees, and industry partners working in translational genetic medicine. Informatics core focuses on building text mining approaches to identify phenotypic information from free-text clinical records to be used by NWIGM investigators primarily in cohort selection.

Faculty: Peter Tarczy-Hornoch (Informatics Core Lead), Meliha Yetisgen-Yildiz (Informatics support)