In JoVE (1)

Other Publications (89)

Articles by Philip Payne in JoVE

Other articles by Philip Payne on PubMed

CRC Tissue Core Management System (TCMS): Integration of Basic Science and Clinical Data for Translational Research

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2003  |  Pubmed ID: 14728358

The Chronic Lymphocytic Leukemia (CLL) Research Consortium (CRC) consists of 9 geographically distributed sites conducting a program of research including both basic science and clinical components. The CRC TCMS was designed to capture and integrate basic science and clinical data sets. The system utilizes multiple data modeling methodologies and web-application platforms, and was designed with the high level objectives of providing an extensible, generalizable model for integrating data as required to conduct translational research.

CRC Clinical Trials Management System (CTMS): an Integrated Information Management Solution for Collaborative Clinical Research

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2003  |  Pubmed ID: 14728471

The Chronic Lymphocytic Leukemia (CLL) Research Consortium (CRC) consists of 9 geographically distributed sites conducting a program of research including both basic science and clinical components. To enable the CRC's clinical research efforts, a system providing for real-time collaboration was required. CTMS provides such functionality, and demonstrates that the use of novel data modeling, web-application platforms, and management strategies provides for the deployment of an extensible, cost effective solution in such an environment.

Quantifying Visual Similarity in Clinical Iconic Graphics

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2003  |  Pubmed ID: 14728519

Numerous studies have demonstrated the benefits of utilizing iconic presentation models in the context of complex medical information. However, very little literature exists that addresses the design of the graphical primitives that constitute such mediums. Utilizing a method named "Presentation Discovery", the authors of this study examine a manner in which objective techniques may be utilized to prototype such graphical primitives in order to increase the realized expressiveness of ensuing iconic presentation models.

Quantifying Visual Similarity in Clinical Iconic Graphics

Journal of the American Medical Informatics Association : JAMIA. May-Jun, 2005  |  Pubmed ID: 15684136

The use of icons and other graphical components in user interfaces has become nearly ubiquitous. The interpretation of such icons is based on the assumption that different users perceive the shapes similarly. At the most basic level, different users must agree on which shapes are similar and which are different. If this similarity can be measured, it may be usable as the basis to design better icons.

Breaking the Translational Barriers: the Value of Integrating Biomedical Informatics and Translational Research

Journal of Investigative Medicine : the Official Publication of the American Federation for Clinical Research. May, 2005  |  Pubmed ID: 15974245

The conduct of translational health research has become a vital national enterprise. However, multiple barriers prevent the effective translation of basic science discoveries into clinical and community practice. New information technology (IT) applications could help address these barriers. Unfortunately, owing to a combination of organizational, technical, and social factors, neither physician-investigators and research staff nor their clinical and community counterparts have harnessed such applications. Recently, at the request of the Institute of Medicine's Clinical Research Roundtable, a qualitative study of these factors was conducted at several leading academic medical centers. We explore the current status of IT in the translational research domain, describe the qualitative results, and conclude with a proposed set of initiatives to further increase the integration of IT into translational research.

Modeling Clinical Trials Workflow in Community Practice Settings

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2006  |  Pubmed ID: 17238375

Clinical research is vital to the translation of biomedical knowledge into standard clinical practice. Efforts are underway under the NIH Roadmap initiative to re-engineer the national research enterprise to sustain the rapid pace of innovation in the biomedical domain. As part of these efforts, we have embarked on an empirical evaluation of clinical research workflow in community practice settings. The reasons for this focus are three-fold. First, there is an increasing tendency by trial sponsors to conduct clinical trials in community, rather than academic, settings. Second, understanding workflow is critical to developing re-engineering strategies. Third, workflow associated with the conduct of clinical research in community practices have received virtually no attention in the scientific literature. In this paper, we describe a pilot study using time-motion observations, to determine the workflow of clinical research coordinators, the tools they use to conduct the constituent activities of those workflows, and their ultimate outcomes. The preliminary findings provide insights and understanding of clinical research workflow in community practice settings - knowledge that may significantly impact the way in which information technology based re-engineering can be deployed in such an environment.

Novel Techniques for Survey and Classification Studies to Improve Patient Centered Websites

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2006  |  Pubmed ID: 17238510

There is great interest in ascertaining patient perceptions in order to create more patient-friendly web resources. The recent proliferation of inexpensive web based data collection systems can facilitate this. Many quite sophisticated tools are commercially available. Unfortunately, researchers often recreate these capabilities in order to avoid privacy issues. This poster describes a simple architecture that allows use of a commercial system while maintaining privacy. In this example, the commercial tool supports the collection of complex categorical sorting data relating to chemotherapy systems. Hypothesis discovery techniques are used to convert the sort data into intuitive web menus.

Coverage of Clinical Trials Tasks in Existing Ontologies

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2006  |  Pubmed ID: 17238522

Clinical research trials involve multiple, often simultaneous processes and corresponding data that collectively involve a diverse group of stakeholders. As efforts are ongoing to enable computable clinical trials and harmonize clinical research data, an ontology targeting the domain of clinical research is essential. As part of a larger project to develop a Clinical Trials scheduling and tracking application, the domain coverage of the UMLS and two component ontologies- SNOMED CT, and the NCI Thesaurus-was evaluated in the context of common clinical trial tasks and events. In total, 102 unique activities were abstracted from 20 protocols, representing a variety of domains, and manually mapped to the target ontologies. Coverage ranged from 84% for UMLS to 32% for the NCI Thesaurus.

Consensus-based Construction of a Taxonomy of Clinical Trial Tasks

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2006  |  Pubmed ID: 17238678

The use of conceptual knowledge collections, such as taxonomies, is prevalent throughout the biomedical domain. Tools and methods to enable the computational representation of such knowledge collections are well established. However, methods for the design and construction of knowledge collections are varied and often rely on the judgments of a single, or small group of, specially trained knowledge engineers. This poster will report on a novel multi-expert consensus-based approach to the development of taxonomies that has been applied to the development of a taxonomy of clinical trial tasks and events.

Human Computer Interaction Issues in Clinical Trials Management Systems

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2006  |  Pubmed ID: 17238728

Clinical trials increasingly rely upon web-based Clinical Trials Management Systems (CTMS). As with clinical care systems, Human Computer Interaction (HCI) issues can greatly affect the usefulness of such systems. Evaluation of the user interface of one web-based CTMS revealed a number of potential human-computer interaction problems, in particular, increased workflow complexity associated with a web application delivery model and potential usability problems resulting from the use of ambiguous icons. Because these design features are shared by a large fraction of current CTMS, the implications extend beyond this individual system.

Conceptual Knowledge Acquisition in Biomedicine: A Methodological Review

Journal of Biomedical Informatics. Oct, 2007  |  Pubmed ID: 17482521

The use of conceptual knowledge collections or structures within the biomedical domain is pervasive, spanning a variety of applications including controlled terminologies, semantic networks, ontologies, and database schemas. A number of theoretical constructs and practical methods or techniques support the development and evaluation of conceptual knowledge collections. This review will provide an overview of the current state of knowledge concerning conceptual knowledge acquisition, drawing from multiple contributing academic disciplines such as biomedicine, computer science, cognitive science, education, linguistics, semiotics, and psychology. In addition, multiple taxonomic approaches to the description and selection of conceptual knowledge acquisition and evaluation techniques will be proposed in order to partially address the apparent fragmentation of the current literature concerning this domain.

A Day in the Life of a Clinical Research Coordinator: Observations from Community Practice Settings

Studies in Health Technology and Informatics. 2007  |  Pubmed ID: 17911716

One of the goals of the NIH Roadmap Initiative is to re-engineer the national clinical research enterprise, with an emphasis on information technology solutions. Understanding end-users' workflow is critical to developing technology systems that are grounded in the context of the users' environment and are designed to fulfill their needs. Community practices are becoming the prevailing setting for conducting clinical research. Few studies have assessed clinical research workflow in such settings. We have conducted a series of investigations to model the workflow and have previously reported on some basic aspects of it, like the lack of information systems to support the workflow. In this paper we describe finer details of the workflow, using results of observational studies. These findings highlight the needs and inefficiencies that suggest the kind of information system that must be developed to enhance collaboration, communication and improve efficiency. This preliminary investigation also opens ground for more extensive studies to further elucidate the workflow.

Identifying Challenges and Opportunities in Clinical Research Informatics: Analysis of a Facilitated Discussion at the 2006 AMIA Annual Symposium

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Oct, 2007  |  Pubmed ID: 18693830

Clinical Research Informatics (CRI) is a rapidly developing sub-domain of Biomedical Informatics that has seen considerable growth in recent years. While there are numerous activities and initiatives ongoing in this domain, systematic consideration and analysis of the challenges and opportunities that exist in this area are lacking. To begin to address this gap in knowledge and inform next steps in advancing this developing domain, we conducted a facilitated discussion among a diverse group of interested participants attending a meeting of the Clinical Research Informatics Working Group at the AMIA 2006 annual symposium. Findings from our analysis of these data are presented here and indicate a broad array of challenges and opportunities facing this developing area. These findings add new information to the limited literature regarding CRI and should provide direction for those working to set the CRI research and development agenda.

Modeling Participant-related Clinical Research Events Using Conceptual Knowledge Acquisition Techniques

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Oct, 2007  |  Pubmed ID: 18693905

The active phase of a clinical trial is defined by a protocol schema consisting of participant-related events organized into multiple visits. Current efforts to model protocol schemas in a computable format have focused on high-level abstractions, such as the temporal relationships between visits. However, such approaches do not address the need for a more granular computational model of the individual events that comprise each visit. To address the preceding gap in knowledge, this paper will describe a study in which conceptual knowledge acquisition (CKA) techniques were applied to a corpus of 32 clinical trials protocol documents in order to develop a knowledge collection of common participant-related clinical research events. These techniques identified 7 high-level concepts that could be used as organizing principles in the resulting knowledge collection. Such results confirm the utility of CKA methods in the clinical research domain.

Evaluating an NLP-based Approach to Modeling Computable Clinical Trial Eligibility Criteria

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Oct, 2007  |  Pubmed ID: 18693979

One area of active research and development within the clinical research informatics domain is the design and use of clinical trial participant recruitment tools. Such tools require the definition of clinical trial eligibility criteria in a computable format. This abstract describes a pilot study employing a natural language processing-based approach for abstracting generic query patterns that are representative of eligibility criteria as found in a corpus of clinical trial protocol documents.

Development of an Ontology-anchored Data Warehouse Meta-model

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Oct, 2007  |  Pubmed ID: 18694100

Data warehouses must provide a flexible data model that is integrated with knowledge and metadata describing their components and contents. To provide for advanced query functionality at The Ohio State University Medical Center (OSUMC), we have developed an abstraction layer, or meta-model for our existing Information Warehouse (IW) in order to conceptually and semantically describe and classify its structure and contents using the UMLS.

A Framework for Workflow-based Clinical Research Billing Disambiguation

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Oct, 2007  |  Pubmed ID: 18694169

Medicare received authorization in 2000 to reimburse for routine costs incurred in association with patients participating in clinical research. However, we hypothesize that the inability to accurately differentiate standard from investigational care has resulted in under-coding of potentially reimbursable clinical events. To address this problem, we have initiated the development of a methodology for constructing computational clinical workflow models that can be employed to aid in the disambiguation of routine versus research costs.

Integrating Heterogeneous Rules-engine Technologies with CaGrid

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Oct, 2007  |  Pubmed ID: 18694196

The use of rules-engines spans multiple computational and biomedical domains. Within the NCIs caBIG program, the orchestration of grid-based computational workflow has used the BPEL standard. However, recent strategic planning within caBIG has raised questions about the applicability of BPEL for other rule definition and execution scenarios. In response, we have reviewed the current state of rules-engine technologies, and have formulated an architectural model for the integration of heterogeneous rules-engines with caGrid.

A Roadmap for CaGrid, an Enterprise Grid Architecture for Biomedical Research

Studies in Health Technology and Informatics. 2008  |  Pubmed ID: 18560123

caGrid is a middleware system which combines the Grid computing, the service oriented architecture, and the model driven architecture paradigms to support development of interoperable data and analytical resources and federation of such resources in a Grid environment. The functionality provided by caGrid is an essential and integral component of the cancer Biomedical Informatics Grid (caBIG) program. This program is established by the National Cancer Institute as a nationwide effort to develop enabling informatics technologies for collaborative, multi-institutional biomedical research with the overarching goal of accelerating translational cancer research. Although the main application domain for caGrid is cancer research, the infrastructure provides a generic framework that can be employed in other biomedical research and healthcare domains. The development of caGrid is an ongoing effort, adding new functionality and improvements based on feedback and use cases from the community. This paper provides an overview of potential future architecture and tooling directions and areas of improvement for caGrid and caGrid-like systems. This summary is based on discussions at a roadmap workshop held in February with participants from biomedical research, Grid computing, and high performance computing communities.

The Design of a Pre-encounter Clinical Trial Screening Tool: ASAP

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Nov, 2008  |  Pubmed ID: 18998826

Manually screening patients for clinical trials eligibility prior to their clinical encounters is labor-intensive and time-consuming. In order to increase the efficiency of such processes, we have developed a web-based system, called Advanced Screening for Active Protocols (ASAP).

Integrating Web Portlet Technologies with CaGrid to Enable Rapid Application Development: the CRC Patient Study Calendar

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Nov, 2008  |  Pubmed ID: 18998940

The caGrid middleware provides an extensible platform for the integration of heterogeneous data sources and services; such as those found in geographically distributed research consortia. We describe an architectural approach to the integration of web portlet technologies with caGrid in order to facilitate the rapid development of highly usable presentation models for grid-based data sources and services, using the development of a patient study calendar for the CLL Research Consortium as a motivating use case.

Supporting the Design of Translational Clinical Studies Through the Generation and Verification of Conceptual Knowledge-anchored Hypotheses

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Nov, 2008  |  Pubmed ID: 18998958

The ability to generate hypotheses based upon the contents of large-scale, heterogeneous data sets is critical to the design of translational clinical studies. In previous reports, we have described the application of a conceptual knowledge engineering technique, known as constructive induction (CI) in order to satisfy such needs. However, one of the major limitations of this method is the need to engage multiple subject matter experts to verify potential hypotheses generated using CI. In this manuscript, we describe an alternative verification technique that leverages published biomedical literature abstracts. Our report will be framed in the context of an ongoing project to generate hypotheses related to the contents of a translational research data repository maintained by the CLL Research Consortium. Such hypotheses will are intended to inform the design of prospective clinical studies that can elucidate the relationships that may exist between biomarkers and patient phenotypes.

Implementation of a Metadata Architecture and Knowledge Collection to Support Semantic Interoperability in an Enterprise Data Warehouse

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Nov, 2008  |  Pubmed ID: 18999040

In order to enhance interoperability between enterprise systems, and improve data validity and reliability throughout The Ohio State University Medical Center (OSUMC), we have initiated the development of an ontology-anchored metadata architecture and knowledge collection for our enterprise data warehouse. The metadata and corresponding semantic relationships stored in the OSUMC knowledge collection are intended to promote consistency and interoperability across the heterogeneous clinical, research, business and education information managed within the data warehouse.

Leveraging an Existing Data Warehouse to Annotate Workflow Models for Operations Research and Optimization

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Nov, 2008  |  Pubmed ID: 18999220

Workflow analysis is frequently performed in the context of operations research and process optimization. In order to develop a data-driven workflow model that can be employed to assess opportunities to improve the efficiency of perioperative care teams at The Ohio State University Medical Center (OSUMC), we have developed a method for integrating standard workflow modeling formalisms, such as UML activity diagrams with data-centric annotations derived from our existing data warehouse.

Innovative Applications of an Enterprise-wide Information Warehouse

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Nov, 2008  |  Pubmed ID: 18999273

The Information Warehouse at The Ohio State University Medical Center is a comprehensive effort integrating data from over 70 sources throughout the enterprise. The IW serves a broad diversity of customers in all mission areas of the medical center, from clinical operations and administration to education, to research. This comprehensiveness has facilitated an innovative application of cross-disciplinary technologies and methodologies to problem domains beyond the roles traditionally envisioned for data warehousing.

E-Science, CaGrid, and Translational Biomedical Research

Computer. Nov, 2008  |  Pubmed ID: 21311723

Translational research projects target a wide variety of diseases, test many different kinds of biomedical hypotheses, and employ a large assortment of experimental methodologies. Diverse data, complex execution environments, and demanding security and reliability requirements make the implementation of these projects extremely challenging and require novel e-Science technologies.

Ontology-anchored Approaches to Conceptual Knowledge Discovery in a Multi-dimensional Research Data Repository

Summit on Translational Bioinformatics. Mar, 2008  |  Pubmed ID: 21347129

Chronic Lymphocytic Leukemia (CLL) is the most common adult leukemia in the U.S., and is currently incurable. Though a small number of biomarkers that may correlate to risk of disease progression or treatment outcome in CLL have been discovered, few have been validated in prospective studies or adopted in clinical practice. In order to address this gap in knowledge, it is desirable to discover and test hypotheses that are concerned with translational biomarker-to-phenotype correlations. We report upon a study in which commonly available ontologies were utilized to support the discovery of such translational correlations. We have specifically applied a technique known as constructive induction to reason over the contents of a research data repository utilized by the NCI-funded CLL Research Consortium. Our findings indicate that such an approach can produce semantically meaningful results that can inform hypotheses about higher-level relationships between the types of data contained in such a repository.

A Knowledge-anchored Integrative Image Search and Retrieval System

Journal of Digital Imaging. Apr, 2009  |  Pubmed ID: 18040742

Clinical data that may be used in a secondary capacity to support research activities are regularly stored in three significantly different formats: (1) structured, codified data elements; (2) semi-structured or unstructured narrative text; and (3) multi-modal images. In this manuscript, we will describe the design of a computational system that is intended to support the ontology-anchored query and integration of such data types from multiple source systems. Additional features of the described system include (1) the use of Grid services-based electronic data interchange models to enable the use of our system in multi-site settings and (2) the use of a software framework intended to address both potential security and patient confidentiality concerns that arise when transmitting or otherwise manipulating potentially privileged personal health information. We will frame our discussion within the specific experimental context of the concept-oriented query and integration of correlated structured data, narrative text, and images for cancer research.

Validation of an LC-MS Based Approach for Profiling Histones in Chronic Lymphocytic Leukemia

Proteomics. Mar, 2009  |  Pubmed ID: 19253275

The in vitro evaluation of histones and their PTMs has drawn substantial interest in the development of epigenetic therapies. The differential expression of histone isoforms may serve as a potential marker in the classification of diseases affected by chromatin abnormalities. In this study, protein profiling by LC and MS was used to explore differences in histone composition in primary chronic lymphocytic leukemia (CLL) cells. Extensive method validations were performed to determine the experimental variances that would impact histone relative abundance. The resulting data demonstrated that the proposed methodology was suitable for the analysis of histone profiles. In 4 normal individuals and 40 CLL patients, a significant decrease in the relative abundance of histone H2A variants (H2AFL and H2AFA/M*) was observed in primary CLL cells as compared to normal B cells. Protein identities were determined using high mass accuracy MS and shotgun proteomics.

Clinical Research Informatics: Challenges, Opportunities and Definition for an Emerging Domain

Journal of the American Medical Informatics Association : JAMIA. May-Jun, 2009  |  Pubmed ID: 19261934

Clinical Research Informatics, an emerging sub-domain of Biomedical Informatics, is currently not well defined. A formal description of CRI including major challenges and opportunities is needed to direct progress in the field.

Translational Informatics: Enabling High-throughput Research Paradigms

Physiological Genomics. Nov, 2009  |  Pubmed ID: 19737991

A common thread throughout the clinical and translational research domains is the need to collect, manage, integrate, analyze, and disseminate large-scale, heterogeneous biomedical data sets. However, well-established and broadly adopted theoretical and practical frameworks and models intended to address such needs are conspicuously absent in the published literature or other reputable knowledge sources. Instead, the development and execution of multidisciplinary, clinical, or translational studies are significantly limited by the propagation of "silos" of both data and expertise. Motivated by this fundamental challenge, we report upon the current state and evolution of biomedical informatics as it pertains to the conduct of high-throughput clinical and translational research and will present both a conceptual and practical framework for the design and execution of informatics-enabled studies. The objective of presenting such findings and constructs is to provide the clinical and translational research community with a common frame of reference for discussing and expanding upon such models and methodologies.

Biomedical Informatics and Outcomes Research: Enabling Knowledge-driven Health Care

Circulation. Dec, 2009  |  Pubmed ID: 19996023

Development of an Agile Knowledge Engineering Framework in Support of Multi-disciplinary Translational Research

Summit on Translational Bioinformatics. Mar, 2009  |  Pubmed ID: 21347164

In October 2006, the National Institutes of Health launched a new national consortium, funded through Clinical and Translational Science Awards (CTSA), with the primary objective of improving the conduct and efficiency of the inherently multi-disciplinary field of translational research. To help meet this goal, the Ohio State University Center for Clinical and Translational Science has launched a knowledge management initiative that is focused on facilitating widespread semantic interoperability among administrative, basic science, clinical and research computing systems, both internally and among the translational research community at-large, through the integration of domain-specific standard terminologies and ontologies with local annotations. This manuscript describes an agile framework that builds upon prevailing knowledge engineering and semantic interoperability methods, and will be implemented as part this initiative.

Adoption and Adaptation of CaGrid for CTSA

Summit on Translational Bioinformatics. Mar, 2009  |  Pubmed ID: 21347169

The field of informatics has been going through a rapid change over the past decade. New technologies such as grid computing[1-5] and knowledge anchored data, combined with major funding and growing community thrusts aimed at creating a richer multi-institutional research and clinical environment such as caBIG™[6-8] (Cancer Bioinformatics Grid), BIRN[9] (Bioinformatics Research Network), and CTSA(Clinical and Translational Science Awards) have lead to new ways to bring together information across institutional boundaries. This had lead to service oriented architectures based developments in creating semantically interoperable data and analytical services to increase speed, efficiency, and outcome of clinical and research efforts spanning the fields of medicine. The TRIAD (Translational Informatics and Data Management Grid) System, which will be used as the middleware system enabling the OSU CTSA to create a scalable, secure, and knowledge anchored data sharing environment, will adopt and adapt the caGrid infrastructure designed for the caBIG™ program.

Conceptual Dissonance: Evaluating the Efficacy of Natural Language Processing Techniques for Validating Translational Knowledge Constructs

Summit on Translational Bioinformatics. Mar, 2009  |  Pubmed ID: 21347178

The conduct of large-scale translational studies presents significant challenges related to the storage, management and analysis of integrative data sets. Ideally, the application of methodologies such as conceptual knowledge discovery in databases (CKDD) provides a means for moving beyond intuitive hypothesis discovery and testing in such data sets, and towards the high-throughput generation and evaluation of knowledge-anchored relationships between complex bio-molecular and phenotypic variables. However, the induction of such high-throughput hypotheses is non-trivial, and requires correspondingly high-throughput validation methodologies. In this manuscript, we describe an evaluation of the efficacy of a natural language processing-based approach to validating such hypotheses. As part of this evaluation, we will examine a phenomenon that we have labeled as "Conceptual Dissonance" in which conceptual knowledge derived from two or more sources of comparable scope and granularity cannot be readily integrated or compared using conventional methods and automated tools.

Foundational Biomedical Informatics Research in the Clinical and Translational Science Era: a Call to Action

Journal of the American Medical Informatics Association : JAMIA. Nov-Dec, 2010  |  Pubmed ID: 20962120

Advances in clinical and translational science, along with related national-scale policy and funding mechanisms, have provided significant opportunities for the advancement of applied clinical research informatics (CRI) and translational bioinformatics (TBI). Such efforts are primarily oriented to application and infrastructure development and are critical to the conduct of clinical and translational research. However, they often come at the expense of the foundational CRI and TBI research needed to grow these important biomedical informatics subdisciplines and ensure future innovations. In light of this challenge, it is critical that a number of steps be taken, including the conduct of targeted advocacy campaigns, the development of community-accepted research agendas, and the continued creation of forums for collaboration and knowledge exchange. Such efforts are needed to ensure that the biomedical informatics community is able to advance CRI and TBI science in the context of the modern clinical and translational science era.

Multi-dimensional Discovery of Biomarker and Phenotype Complexes

BMC Bioinformatics. 2010  |  Pubmed ID: 21044361

Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature.

Using Gene Co-expression Network Analysis to Predict Biomarkers for Chronic Lymphocytic Leukemia

BMC Bioinformatics. 2010  |  Pubmed ID: 21044363

Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered.

Standardizing Clinical Trials Workflow Representation in UML for International Site Comparison

PloS One. Nov, 2010  |  Pubmed ID: 21085484

With the globalization of clinical trials, a growing emphasis has been placed on the standardization of the workflow in order to ensure the reproducibility and reliability of the overall trial. Despite the importance of workflow evaluation, to our knowledge no previous studies have attempted to adapt existing modeling languages to standardize the representation of clinical trials. Unified Modeling Language (UML) is a computational language that can be used to model operational workflow, and a UML profile can be developed to standardize UML models within a given domain. This paper's objective is to develop a UML profile to extend the UML Activity Diagram schema into the clinical trials domain, defining a standard representation for clinical trial workflow diagrams in UML.

The TRITON Project: Design and Implementation of an Integrative Translational Research Information Management Platform

AMIA ... Annual Symposium Proceedings. AMIA Symposium. Nov, 2010  |  Pubmed ID: 21347052

Multi-site consortia have become the preferred setting for team-based translational research programs. Such consortia are able to facilitate increased breadth and depth of basic science and clinical research activities, but also present numerous challenges related to data collection, analysis, storage, and exchange. The Chronic Lymphocytic Leukemia (CLL) Research Consortium (CRC), a s a prototypical instance of such a consortia, uses numerous loosely coupled web applications to address its informatics needs. Over a decade of operations have allowed the CRC to identify usability and computational limitations relative to the preceding information management architecture. In response, the CRC has launched the TRITON project, with the ultimate objective of developing an open-source, extensible, and fully integrative translational research information management platform. In this manuscript, we describe the architecture, design processes, and initial implementation of thatplatform.

Evaluating the Impact of Conceptual Knowledge Engineering on the Design and Usability of a Clinical and Translational Science Collaboration Portal

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science. Mar, 2010  |  Pubmed ID: 21347146

With the growing prevalence of large-scale, team science endeavors in the biomedical and life science domains, the impetus to implement platforms capable of supporting asynchronous interaction among multidisciplinary groups of collaborators has increased commensurately. However, there is a paucity of literature describing systematic approaches to identifying the information needs of targeted end-users for such platforms, and the translation of such requirements into practicable software component design criteria. In previous studies, we have reported upon the efficacy of employing conceptual knowledge engineering (CKE) techniques to systematically address both of the preceding challenges in the context of complex biomedical applications. In this manuscript we evaluate the impact of CKE approaches relative to the design of a clinical and translational science collaboration portal, and report upon the preliminary qualitative users satisfaction as reported for the resulting system.

Improving Clinical Trial Participant Tracking Tools Using Knowledge-anchored Design Methodologies

Applied Clinical Informatics. 2010  |  Pubmed ID: 22132037

OBJECTIVE: Rigorous human-computer interaction (HCI) design methodologies have not traditionally been applied to the development of clinical trial participant tracking (CTPT) tools. Given the frequent us of iconic HCI models in CTPTs, and prior evidence of usability problems associated with the use of ambiguous icons in complex interfaces, such approaches may be problematic. Presentation Discovery (PD), a knowledge-anchored HCI design method, has been previously demonstrated to improve the design of iconic HCI models. In this study, we compare the usability of a CTPT HCI model designed using PD and an intuitively designed CTPT HCI model. METHODS: An iconic CPTP HCI model was created using PD. The PD-generated and an existing iconic CTPT HCI model were subjected to usability testing, with an emphasis on task accuracy and completion times. Study participants also completed a qualitative survey instrument to evaluate subjective satisfaction with the two models. RESULTS: CTPT end-users reliably and reproducibly agreed on the visual manifestation and semantics of prototype graphics generated using PD. The performance of the PD-generated iconic HCI model was equivalent to an existing HCI model for tasks at multiple levels of complexity, and in some cases superior. This difference was particularly notable when tasks required an understanding of the semantic meanings of multiple icons. CONCLUSION: The use of PD to design an iconic CTPT HCI model generated beneficial results and improved end-user subjective satisfaction, while reducing task completion time. Such results are desirable in information and time intensive domains, such as clinical trials management.

Health-care Hit or Miss?

Nature. Feb, 2011  |  Pubmed ID: 21331020

Transactional Database Transformation and Its Application in Prioritizing Human Disease Genes

IEEE/ACM Transactions on Computational Biology and Bioinformatics / IEEE, ACM. Mar, 2011  |  Pubmed ID: 21422495

Binary (0/1) matrices, commonly known as transactional databases, can represent many application data, including gene-phenotype data where '1' represents a confirmed gene-phenotype relation and '0' represents an unknown relation. It is natural to ask what information is hidden behind these '0's and '1's. Unfortunately, recent matrix completion methods, though very effective in many cases, are less likely to infer something interesting from these 0/1 matrices. To answer this challenge, we propose \textsc{IndEvi}, a very succinct and effective algorithm to perform independent-evidence-based transactional database transformation. Each entry of a 0/1 matrix is evaluated by "independent evidence" (maximal supporting patterns) extracted from the whole matrix for this entry. The value of an entry, no matter 0 or 1, has completely no effect for its independent evidence. The experiment on a gene-phenotype database shows that our method is highly promising in ranking candidate genes and predicting unknown disease genes.

Research-IQ: Development and Evaluation of an Ontology-anchored Integrative Query Tool

Journal of Biomedical Informatics. Dec, 2011  |  Pubmed ID: 21821150

Investigators in the translational research and systems medicine domains require highly usable, efficient and integrative tools and methods that allow for the navigation of and reasoning over emerging large-scale data sets. Such resources must cover a spectrum of granularity from bio-molecules to population phenotypes. Given such information needs, we report upon the initial design and evaluation of an ontology-anchored integrative query tool, Research-IQ, which employs a combination of conceptual knowledge engineering and information retrieval techniques to enable the intuitive and rapid construction of queries, in terms of semi-structured textual propositions, that can subsequently be applied to integrative data sets. Our initial results, based upon both quantitative and qualitative evaluations of the efficacy and usability of Research-IQ, demonstrate its potential to increase clinical and translational research throughput.

The TOKEn Project: Knowledge Synthesis for in Silico Science

Journal of the American Medical Informatics Association : JAMIA. Dec, 2011  |  Pubmed ID: 21984589

The conduct of investigational studies that involve large-scale data sets presents significant challenges related to the discovery and testing of novel hypotheses capable of supporting in silico discovery science. The use of what are known as Conceptual Knowledge Discovery in Databases (CKDD) methods provides a potential means of scaling hypothesis discovery and testing approaches for large data sets. Such methods enable the high-throughput generation and evaluation of knowledge-anchored relationships between complexes of variables found in targeted data sets.

Selected Papers from the 2011 Summit on Clinical Research Informatics

Journal of Biomedical Informatics. Dec, 2011  |  Pubmed ID: 22138363

The Joint Summits on Translational Science: Crossing the Translational Chasm

Journal of Biomedical Informatics. Dec, 2011  |  Pubmed ID: 22138364

K-Neighborhood Decentralization: A Comprehensive Solution to Index the UMLS for Large Scale Knowledge Discovery

Journal of Biomedical Informatics. Dec, 2011  |  Pubmed ID: 22154838

The Unified Medical Language System (UMLS) is the largest thesaurus in the biomedical informatics domain. Previous works have shown that knowledge constructs comprised of transitively-associated UMLS concepts are effective for discovering potentially novel biomedical hypotheses. However, the extremely large size of the UMLS becomes a major challenge for these applications. To address this problem, we designed a k-neighborhood Decentralization Labeling Scheme (kDLS) for the UMLS, and the corresponding method to effectively evaluate the kDLS indexing results. kDLS provides a comprehensive solution for indexing the UMLS for very efficient large scale knowledge discovery. We demonstrated that it is highly effective to use kDLS paths to prioritize disease-gene relations across the whole genome, with extremely high fold-enrichment values. To our knowledge, this is the first indexing scheme capable of supporting efficient large scale knowledge discovery on the UMLS as a whole. Our expectation is that kDLS will become a vital engine for retrieving information and generating hypotheses from the UMLS for future medical informatics applications.

Computational Challenges and Human Factors Influencing the Design and Use of Clinical Research Participant Eligibility Pre-screening Tools

BMC Medical Informatics and Decision Making. May, 2012  |  Pubmed ID: 22646313

Clinical trials are the primary mechanism for advancing clinical care and evidenced-based practice, yet challenges with the recruitment of participants for such trials are widely recognized as a major barrier to these types of studies. Data warehouses (DW) store large amounts of heterogenous clinical data that can be used to enhance recruitment practices, but multiple challenges exist when using a data warehouse for such activities, due to the manner of collection, management, integration, analysis, and dissemination of the data. A critical step in leveraging the DW for recruitment purposes is being able to match trial eligibility criteria to discrete and semi-structured data types in the data warehouse, though trial eligibility criteria tend to be written without concern for their computability. We present the multi-modal evaluation of a web-based tool that can be used for pre-screening patients for clinical trial eligibility and assess the ability of this tool to be practically used for clinical research pre-screening and recruitment.

Applying Knowledge-anchored Hypothesis Discovery Methods to Advance Clinical and Translational Research: the OAMiner Project

Journal of the American Medical Informatics Association : JAMIA. Nov-Dec, 2012  |  Pubmed ID: 22647689

The conduct of clinical and translational research regularly involves the use of a variety of heterogeneous and large-scale data resources. Scalable methods for the integrative analysis of such resources, particularly when attempting to leverage computable domain knowledge in order to generate actionable hypotheses in a high-throughput manner, remain an open area of research. In this report, we describe both a generalizable design pattern for such integrative knowledge-anchored hypothesis discovery operations and our experience in applying that design pattern in the experimental context of a set of driving research questions related to the publicly available Osteoarthritis Initiative data repository. We believe that this 'test bed' project and the lessons learned during its execution are both generalizable and representative of common clinical and translational research paradigms.

Chapter 1: Biomedical Knowledge Integration

PLoS Computational Biology. 2012  |  Pubmed ID: 23300416

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

Time Capture Tool (TimeCaT): Development of a Comprehensive Application to Support Data Capture for Time Motion Studies

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2012  |  Pubmed ID: 23304332

Time Motion Studies (TMS) have proved to be the gold standard method to measure and quantify clinical workflow, and have been widely used to assess the impact of health information systems implementation. Although there are tools available to conduct TMS, they provide different approaches for multitasking, interruptions, inter-observer reliability assessment and task taxonomy, making results across studies not comparable. We postulate that a significant contributing factor towards the standardization and spread of TMS would be the availability and spread of an accessible, scalable and dynamic tool. We present the development of a comprehensive Time Capture Tool (TimeCaT): a web application developed to support data capture for TMS. Ongoing and continuous development of TimeCaT includes the development and validation of a realistic inter-observer reliability scoring algorithm, the creation of an online clinical tasks ontology, and a novel quantitative workflow comparison method.

Towards a "4I" Approach to Personalized Healthcare

Clinical and Translational Medicine. Jul, 2012  |  Pubmed ID: 23369359

Personalized healthcare holds the promise of ensuring that every patient receives optimal wellness promotion and clinical care based upon his or her unique and multi-factorial phenotype, informed by the most up-to-date and contextually relevant science. However, achieving this vision requires the management, analysis, and delivery of complex data, information, and knowledge. While there are well-established frameworks that serve to inform the pursuit of basic science, clinical, and translational research in support of the operationalization of the personalized healthcare paradigm, equivalent constructs that may enable biomedical informatics innovation and practice aligned with such objectives are noticeably sparse. In response to this gap in knowledge, we propose such a framework for the advancement of biomedical informatics in order to address the fundamental information needs of the personalized healthcare domain. This framework, which we refer to as a "4I" approach, emphasizes the pursuit of research and practice that is information-centric, integrative, interactive, and innovative.

People, Organizational, and Leadership Factors Impacting Informatics Support for Clinical and Translational Research

BMC Medical Informatics and Decision Making. Feb, 2013  |  Pubmed ID: 23388243

In recent years, there have been numerous initiatives undertaken to describe critical information needs related to the collection, management, analysis, and dissemination of data in support of biomedical research (J Investig Med 54:327-333, 2006); (J Am Med Inform Assoc 16:316-327, 2009); (Physiol Genomics 39:131-140, 2009); (J Am Med Inform Assoc 18:354-357, 2011). A common theme spanning such reports has been the importance of understanding and optimizing people, organizational, and leadership factors in order to achieve the promise of efficient and timely research (J Am Med Inform Assoc 15:283-289, 2008). With the emergence of clinical and translational science (CTS) as a national priority in the United States, and the corresponding growth in the scale and scope of CTS research programs, the acuity of such information needs continues to increase (JAMA 289:1278-1287, 2003); (N Engl J Med 353:1621-1623, 2005); (Sci Transl Med 3:90, 2011). At the same time, systematic evaluations of optimal people, organizational, and leadership factors that influence the provision of data, information, and knowledge management technologies and methods are notably lacking.

Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research

Medical Care. Aug, 2013  |  Pubmed ID: 23774517

The growing amount of data in operational electronic health record systems provides unprecedented opportunity for its reuse for many tasks, including comparative effectiveness research. However, there are many caveats to the use of such data. Electronic health record data from clinical settings may be inaccurate, incomplete, transformed in ways that undermine their meaning, unrecoverable for research, of unknown provenance, of insufficient granularity, and incompatible with research protocols. However, the quantity and real-world nature of these data provide impetus for their use, and we develop a list of caveats to inform would-be users of such data as well as provide an informatics roadmap that aims to insure this opportunity to augment comparative effectiveness research can be best leveraged.

Knowledge Management and Informatics Considerations for Comparative Effectiveness Research: a Case-driven Exploration

Medical Care. Aug, 2013  |  Pubmed ID: 23793050

As clinical data are increasingly collected and stored electronically, their potential use for comparative effectiveness research (CER) grows. Despite this promise, challenges face those wishing to leverage such data. In this paper we aim to enumerate some of the knowledge management and informatics issues common to such data reuse.

Evidence Generating Medicine: Redefining the Research-practice Relationship to Complete the Evidence Cycle

Medical Care. Aug, 2013  |  Pubmed ID: 23793052

Accelerating clinical and translational science and improving healthcare effectiveness, quality, and efficiency are top priorities for the United States. Increasingly, the success of such initiatives relies on leveraging point-of-care activities, data, and resources to generate evidence through routine practice. At present, leveraging healthcare activities to advance knowledge is challenging. Underlying these challenges are a variety of persistent technological, regulatory, fiscal, and socio-organizational realities. Fundamentally, these result from the fact that the current healthcare system is designed around a paradigm that enables individual patient care and views the connection between research and practice as unidirectional (ie, research findings are applied to practice using evidence-based medicine) but does not support research-related activities during practice. We suggest that a fundamental paradigm shift is needed to redefine the relationship between research and practice as bidirectional rather than unidirectional and propose the concept of evidence generating medicine to provide a framework for realizing such a shift. We discuss how a transition toward evidence generating medicine would result in a range of much-needed system-level changes that would facilitate rather than frustrate the ongoing efforts of informaticians, health services researchers, and others working to accelerate research and improve healthcare.

EHR-based Clinical Trial Alert Effects on Recruitment to a Neurology Trial Across Institutions: Interim Analysis of a Randomized Controlled Study

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science. 2013  |  Pubmed ID: 24303248

EHR-based, point-of-care, clinical trial alerts (CTAs) have shown promise in prior studies to improve subject recruitment rates, but those studies have had limitations of generalizability. We report here an interim analysis of a cluster randomized controlled trial of the CTA approach applied to a neurology study at a second institution to test the efficacy of the approach across institutions with a different EHR. During the first phase (4 months) of our study, the CTA significantly improved physician-generated referrals among intervention physicians vs. control physicians (35 vs. 0). Additional trends and information about the usage of CTA features have also been gleaned. These findings add to the limited evidence for the utility and generalizability of the CTA approach.

Inter-observer Reliability Assessments in Time Motion Studies: the Foundation for Meaningful Clinical Workflow Analysis

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2013  |  Pubmed ID: 24551381

Understanding clinical workflow is critical for researchers and healthcare decision makers. Current workflow studies tend to oversimplify and underrepresent the complexity of clinical workflow. Continuous observation time motion studies (TMS) could enhance clinical workflow studies by providing rich quantitative data required for in-depth workflow analyses. However, methodological inconsistencies have been reported in continuous observation TMS, potentially reducing the validity of TMS' data and limiting their contribution to the general state of knowledge. We believe that a cornerstone in standardizing TMS is to ensure the reliability of the human observers. In this manuscript we review the approaches for inter-observer reliability assessment (IORA) in a representative sample of TMS focusing on clinical workflow. We found that IORA is an uncommon practice, inconsistently reported, and often uses methods that provide partial and overestimated measures of agreement. Since a comprehensive approach to IORA is yet to be proposed and validated, we provide initial recommendations for IORA reporting in continuous observation TMS.

Recommendations for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research

EGEMS (Washington, DC). 2013  |  Pubmed ID: 25848563

There is an increasing amount of clinical data in operational electronic health record (EHR) systems. Such data provide substantial opportunities for their re-use for many purposes, including comparative effectiveness research (CER). In a previous paper, we identified a number of caveats related to the use of such data, noting that they may be inaccurate, incomplete, transformed in ways that undermine their meaning, unrecoverable for research, of unknown provenance, of insufficient granularity, or incompatible with research protocols. In this paper, we provide recommendations for overcoming these caveats with the goal of leveraging such data to benefit CER and other health care activities. These recommendations include adaptation of "best evidence" approaches to use of data; processes to evaluate availability, completeness, quality, and transformability of data; creation of tools to manage data and their attributes; determination of metrics for assessing whether data are "research grade"; development of methods for comparative validation of data; construction of a methodology database for methods involving use of clinical data; standardized reporting methods for data and their attributes; appropriate use of informatics expertise; and a research agenda to determine biases inherent in operational data and to assess informatics approaches to their improvement.

Advancing User Experience Research to Facilitate and Enable Patient-centered Research: Current State and Future Directions

EGEMS (Washington, DC). 2013  |  Pubmed ID: 25848566

Human-computer interaction and related areas of user experience (UX) research, such as human factors, workflow evaluation, and data visualization, are thus essential to presenting data in ways that can further the analysis of complex data sets such as those used in patient-centered research. However, a review of available data on the state of UX research as it relates to patient-centered research demonstrates a significant underinvestment and consequently a large gap in knowledge generation. In response, this report explores trends in funding and research productivity focused on UX and patient-centered research and then presents a set of recommendations to advance innovation at this important intersection point. Ultimately, the aim is to catalyze a community-wide dialogue concerning future directions for research and innovation in UX as it applies to patient-centered research.

Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) Technology Infrastructure for a Distributed Data Network

EGEMS (Washington, DC). 2013  |  Pubmed ID: 25848567

Distributed Data Networks (DDNs) offer infrastructure solutions for sharing electronic health data from across disparate data sources to support comparative effectiveness research. Data sharing mechanisms must address technical and governance concerns stemming from network security and data disclosure laws and best practices, such as HIPAA.

Time Motion Studies in Healthcare: What Are We Talking About?

Journal of Biomedical Informatics. Jun, 2014  |  Pubmed ID: 24607863

Time motion studies were first described in the early 20th century in industrial engineering, referring to a quantitative data collection method where an external observer captured detailed data on the duration and movements required to accomplish a specific task, coupled with an analysis focused on improving efficiency. Since then, they have been broadly adopted by biomedical researchers and have become a focus of attention due to the current interest in clinical workflow related factors. However, attempts to aggregate results from these studies have been difficult, resulting from a significant variability in the implementation and reporting of methods. While efforts have been made to standardize the reporting of such data and findings, a lack of common understanding on what "time motion studies" are remains, which not only hinders reviews, but could also partially explain the methodological variability in the domain literature (duration of the observations, number of tasks, multitasking, training rigor and reliability assessments) caused by an attempt to cluster dissimilar sub-techniques. A crucial milestone towards the standardization and validation of time motion studies corresponds to a common understanding, accompanied by a proper recognition of the distinct techniques it encompasses. Towards this goal, we conducted a review of the literature aiming at identifying what is being referred to as "time motion studies". We provide a detailed description of the distinct methods used in articles referenced or classified as "time motion studies", and conclude that currently it is used not only to define the original technique, but also to describe a broad spectrum of studies whose only common factor is the capture and/or analysis of the duration of one or more events. To maintain alignment with the existing broad scope of the term, we propose a disambiguation approach by preserving the expanded conception, while recommending the use of a specific qualifier "continuous observation time motion studies" to refer to variations of the original method (the use of an external observer recording data continuously). In addition, we present a more granular naming for sub-techniques within continuous observation time motion studies, expecting to reduce the methodological variability within each sub-technique and facilitate future results aggregation.

Assessment of Life's Simple 7 in the Primary Care Setting: the Stroke Prevention in Healthcare Delivery EnviRonmEnts (SPHERE) Study

Contemporary Clinical Trials. Jul, 2014  |  Pubmed ID: 24721482

Adverse health behaviors and factors predict increased coronary heart disease and stroke risk, and effective use of health information technology (HIT) to automate assessment of and intervention on these factors is needed. A comprehensive, automated cardiovascular health (CVH) assessment deployed in the primary care setting offers the potential to enhance prevention, facilitate patient-provider communication, and ultimately reduce cardiovascular (CV) disease risk. We describe the methods for a study to develop and test an automated CVH application for stroke prevention in older women.

Community-level Determinants of Obesity: Harnessing the Power of Electronic Health Records for Retrospective Data Analysis

BMC Medical Informatics and Decision Making. May, 2014  |  Pubmed ID: 24886134

Obesity and overweight are multifactorial diseases that affect two thirds of Americans, lead to numerous health conditions and deeply strain our healthcare system. With the increasing prevalence and dangers associated with higher body weight, there is great impetus to focus on public health strategies to prevent or curb the disease. Electronic health records (EHRs) are a powerful source for retrospective health data, but they lack important community-level information known to be associated with obesity. We explored linking EHR and community data to study factors associated with overweight and obesity in a systematic and rigorous way.

Towards Symbiosis in Knowledge Representation and Natural Language Processing for Structuring Clinical Practice Guidelines

Studies in Health Technology and Informatics. 2014  |  Pubmed ID: 24943582

The successful adoption by clinicians of evidence-based clinical practice guidelines (CPGs) contained in clinical information systems requires efficient translation of free-text guidelines into computable formats. Natural language processing (NLP) has the potential to improve the efficiency of such translation. However, it is laborious to develop NLP to structure free-text CPGs using existing formal knowledge representations (KR). In response to this challenge, this vision paper discusses the value and feasibility of supporting symbiosis in text-based knowledge acquisition (KA) and KR. We compare two ontologies: (1) an ontology manually created by domain experts for CPG eligibility criteria and (2) an upper-level ontology derived from a semantic pattern-based approach for automatic KA from CPG eligibility criteria text. Then we discuss the strengths and limitations of interweaving KA and NLP for KR purposes and important considerations for achieving the symbiosis of KR and NLP for structuring CPGs to achieve evidence-based clinical practice.

Advancing Methodologies in Clinical Research Informatics (CRI): Foundational Work for a Maturing Field

Journal of Biomedical Informatics. Dec, 2014  |  Pubmed ID: 25484113

Enabling Online Studies of Conceptual Relationships Between Medical Terms: Developing an Efficient Web Platform

JMIR Medical Informatics. Oct, 2014  |  Pubmed ID: 25600290

The Unified Medical Language System (UMLS) contains many important ontologies in which terms are connected by semantic relations. For many studies on the relationships between biomedical concepts, the use of transitively associated information from ontologies and the UMLS has been shown to be effective. Although there are a few tools and methods available for extracting transitive relationships from the UMLS, they usually have major restrictions on the length of transitive relations or on the number of data sources.

Sustainability Through Technology Licensing and Commercialization: Lessons Learned from the TRIAD Project

EGEMS (Washington, DC). 2014  |  Pubmed ID: 25848609

Ongoing transformation relative to the funding climate for healthcare research programs housed in academic and non-profit research organizations has led to a new (or renewed) emphasis on the pursuit of non-traditional sustainability models. This need is often particularly acute in the context of data management and sharing infrastructure that is developed under the auspices of such research initiatives. One option for achieving sustainability of such data management and sharing infrastructure is the pursuit of technology licensing and commercialization, in an effort to establish public-private or equivalent partnerships that sustain and even expand upon the development and dissemination of research-oriented data management and sharing technologies. However, the critical success factors for technology licensing and commercialization efforts are often unknown to individuals outside of the private sector, thus making this type of endeavor challenging to investigators in academic and non-profit settings. In response to such a gap in knowledge, this article will review a number of generalizable lessons learned from an effort undertaken at The Ohio State University to commercialize a prototypical research-oriented data management and sharing infrastructure, known as the Translational Research Informatics and Data Management (TRIAD) Grid. It is important to note that the specific emphasis of these lessons learned is on the early stages of moving a technology from the research setting into a private-sector entity and as such are particularly relevant to academic investigators interested in pursuing such activities.

Domain Analysis of Integrated Data to Reduce Cost Associated with Liver Disease

Studies in Health Technology and Informatics. 2015  |  Pubmed ID: 26262083

Liver cancer, the fifth most common cancer and second leading cause of cancer-related death among men worldwide, is plagued by not only lack of clinical research, but informatics tools for early detection. Consequently, it presents a major health and cost burden. Among the different types of liver cancer, hepatocellular carcinoma (HCC) is the most common and deadly form, arising from underlying liver disease. Current models for predicting risk of HCC and liver disease are limited to clinical data. A domain analysis of existing research related to screening for HCC and liver disease suggests that metabolic syndrome (MetS) may present oppportunites to detect early signs of liver disease. The purpose of this paper is to (i) provide a domain analysis of the relationship between HCC, liver disease, and metabolic syndrome, (ii) a review of the current disparate sources of data available for MetS diagnosis, and (iii) recommend informatics solutions for the diagnosis of MetS from available administrative (Biometrics, PHA, claims) and laboratory data, towards early prediction of liver disease. Our domain analysis and recommendations incorporate best practices to make meaningful use of available data with the goal of reducing cost associated with liver disease.

Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma

Studies in Health Technology and Informatics. 2015  |  Pubmed ID: 26262134

The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies.

Text Mining and Data Modeling of Karyotypes to Aid in Drug Repurposing Efforts

Studies in Health Technology and Informatics. 2015  |  Pubmed ID: 26262336

Karyotyping, or visually examining and recording chromosomal abnormalities, is commonly used to diagnose and treat disease. Karyotypes are written in the International System for Human Cytogenetic Nomenclature (ISCN), a computationally non-readable language that precludes full analysis of these genomic data. In response, we developed a cytogenetic platform that transfers the ISCN karyotypes to a machine-readable model available for computational analysis. Here we use cytogenetic data from the National Cancer Institute (NCI)-curated Mitelman database1 to create a structured karyotype language. Then, drug-gene-disease triplets are generated via a computational pipeline connecting public drug-gene interaction data sources to identify potential drug repurposing opportunities.

A Metadata Based Knowledge Discovery Methodology for Seeding Translational Research

Studies in Health Technology and Informatics. 2015  |  Pubmed ID: 26262370

In this paper, we present a semantic, metadata based knowledge discovery methodology for identifying teams of researchers from diverse backgrounds who can collaborate on interdisciplinary research projects: projects in areas that have been identified as high-impact areas at The Ohio State University. This methodology involves the semantic annotation of keywords and the postulation of semantic metrics to improve the efficiency of the path exploration algorithm as well as to rank the results. Results indicate that our methodology can discover groups of experts from diverse areas who can collaborate on translational research projects.

Real-time Data Fusion Platforms: The Need of Multi-dimensional Data-driven Research in Biomedical Informatics

Studies in Health Technology and Informatics. 2015  |  Pubmed ID: 26262406

Systems designed to expedite data preprocessing tasks such as data discovery, interpretation, and integration that are required before data analysis drastically impact the pace of biomedical informatics research. Current commercial interactive and real-time data integration tools are designed for large-scale business analytics requirements. In this paper we identify the need for end-to-end data fusion platforms from the researcher's perspective, supporting ad-hoc data interpretation and integration.

EHR-based Visualization Tool: Adoption Rates, Satisfaction, and Patient Outcomes

EGEMS (Washington, DC). 2015  |  Pubmed ID: 26290891

Electronic health records (EHRs) have the potential to enhance patient-provider communication and improve patient outcomes. However, in order to impact patient care, clinical decision support (CDS) and communication tools targeting such needs must be integrated into clinical workflow and be flexible with regard to the changing health care landscape.

ResearchIQ: Design of a Semantically Anchored Integrative Query Tool

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science. 2015  |  Pubmed ID: 26306248

An important factor influencing the pace of research activity is the ability of researchers to discover and leverage heterogeneous resources. Usually, researcher profiles, laboratory equipment, data samples, clinical trials, and other research resources are stored in heterogeneous datasets in large organizations. Emergent semantic web technologies provide novel approaches to discover, annotate and consequently link such resources. In this manuscript, we describe the design of Research Integrative Query (ResearchIQ) tool, a semantically anchored resource discovery platform that facilitates semantic discovery of local and publically available data through a single web portal designed for researchers in the biomedical informatics domain within The Ohio State University.

Gene Expression Profiling of the Human Natural Killer Cell Response to Fc Receptor Activation: Unique Enhancement in the Presence of Interleukin-12

BMC Medical Genomics. Oct, 2015  |  Pubmed ID: 26470881

Traditionally, the CD56(dim)CD16(+) subset of Natural Killer (NK) cells has been thought to mediate cellular cytotoxicity with modest cytokine secretion capacity. However, studies have suggested that this subset may exert a more diverse array of immunological functions. There exists a lack of well-developed functional models to describe the behavior of activated NK cells, and the interactions between signaling pathways that facilitate effector functions are not well understood. In the present study, a combination of genome-wide microarray analyses and systems-level bioinformatics approaches were utilized to elucidate the transcriptional landscape of NK cells activated via interactions with antibody-coated targets in the presence of interleukin-12 (IL-12).

A Novel Multiple Choice Question Generation Strategy: Alternative Uses for Controlled Vocabulary Thesauri in Biomedical-Sciences Education

AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2015  |  Pubmed ID: 26958222

Multiple choice questions play an important role in training and evaluating biomedical science students. However, the resource intensive nature of question generation limits their open availability, reducing their contribution to evaluation purposes mainly. Although applied-knowledge questions require a complex formulation process, the creation of concrete-knowledge questions (i.e., definitions, associations) could be assisted by the use of informatics methods. We envisioned a novel and simple algorithm that exploits validated knowledge repositories and generates concrete-knowledge questions by leveraging concepts' relationships. In this manuscript we present the development and validation of a prototype which successfully produced meaningful concrete-knowledge questions, opening new applications for existing knowledge repositories, potentially benefiting students of all biomedical sciences disciplines.

Interdisciplinary Training to Build an Informatics Workforce for Precision Medicine

Applied & Translational Genomics. Sep, 2015  |  Pubmed ID: 27054076

The proposed Precision Medicine Initiative has the potential to transform medical care in the future through a shift from interventions based on evidence from population studies and empiric response to ones that account for a range of individual factors that more reliably predict response and outcomes for the patient. Many things are needed to realize this vision, but one of the most critical is an informatics workforce that has broad interdisciplinary training in basic science, applied research and clinical implementation. Current approaches to informatics training do not support this requirement. We present a collaborative model of training that has the potential to produce a workforce prepared for the challenges of implementing precision medicine.


Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 2016  |  Pubmed ID: 26776168

The Geographic Distribution of Cardiovascular Health in the Stroke Prevention in Healthcare Delivery Environments (SPHERE) Study

Journal of Biomedical Informatics. Apr, 2016  |  Pubmed ID: 26828957

Community-level factors have been clearly linked to health outcomes, but are challenging to incorporate into medical practice. Increasing use of electronic health records (EHRs) makes patient-level data available for researchers in a systematic and accessible way, but these data remain siloed from community-level data relevant to health.

Rethinking the Role and Impact of Health Information Technology: Informatics As an Interventional Discipline

BMC Medical Informatics and Decision Making. Mar, 2016  |  Pubmed ID: 27025583

Recent advances in the adoption and use of health information technology (HIT) have had a dramatic impact on the practice of medicine. In many environments, this has led to the ability to achieve new efficiencies and levels of safety. In others, the impact has been less positive, and is associated with both: 1) workflow and user experience dissatisfaction; and 2) perceptions of missed opportunities relative to the use of computational tools to enable data-driven and precise clinical decision making. Simultaneously, the "pipeline" through which new diagnostic tools and therapeutic agents are being developed and brought to the point-of-care or population health is challenged in terms of both cost and timeliness. Given the confluence of these trends, it can be argued that now is the time to consider new ways in which HIT can be used to deliver health and wellness interventions comparable to traditional approaches (e.g., drugs, devices, diagnostics, and behavioral modifications). Doing so could serve to fulfill the promise of what has been recently promoted as "precision medicine" in a rapid and cost-effective manner. However, it will also require the health and life sciences community to embrace new modes of using HIT, wherein the use of technology becomes a primary intervention as opposed to enabler of more conventional approaches, a model that we refer to in this commentary as "interventional informatics". Such a paradigm requires attention to critical issues, including: 1) the nature of the relationships between HIT vendors and healthcare innovators; 2) the formation and function of multidisciplinary teams consisting of technologists, informaticians, and clinical or scientific subject matter experts; and 3) the optimal design and execution of clinical studies that focus on HIT as the intervention of interest. Ultimately, the goal of an "interventional informatics" approach can and should be to substantially improve human health and wellness through the use of data-driven interventions at the point of care of broader population levels. Achieving a vision of "interventional informatics" will requires us to re-think how we study HIT tools in order to generate the necessary evidence-base that can support and justify their use as a primary means of improving the human condition.

'RE:fine Drugs': an Interactive Dashboard to Access Drug Repurposing Opportunities

Database : the Journal of Biological Databases and Curation. 2016  |  Pubmed ID: 27189611

The process of discovering new drugs has been extremely costly and slow in the last decades despite enormous investment in pharmaceutical research. Drug repurposing enables researchers to speed up the process of discovering other conditions that existing drugs can effectively treat, with low cost and fast FDA approval. Here, we introduce 'RE:fine Drugs', a freely available interactive website for integrated search and discovery of drug repurposing candidates from GWAS and PheWAS repurposing datasets constructed using previously reported methods in Nature Biotechnology. 'RE:fine Drugs' demonstrates the possibilities to identify and prioritize novelty of candidates for drug repurposing based on the theory of transitive Drug-Gene-Disease triads. This public website provides a starting point for research, industry, clinical and regulatory communities to accelerate the investigation and validation of new therapeutic use of old drugs.Database URL:

Classification of Indeterminate Melanocytic Lesions by MicroRNA Profiling

Annals of Surgical Oncology. Jul, 2016  |  Pubmed ID: 27469124

Identification of indeterminate melanocytic skin lesions capable of neoplastic progression is suboptimal and may potentially result in unnecessary morbidity from surgery. MicroRNAs (miRs) may be useful in classifying indeterminate Spitz tumors as having high or low risk for malignant behavior.

Electronic Health Record-based Assessment of Cardiovascular Health: The Stroke Prevention in Healthcare Delivery Environments (SPHERE) Study

Preventive Medicine Reports. Dec, 2016  |  Pubmed ID: 27486559

< 3% of Americans have ideal cardiovascular health (CVH). The primary care encounter provides a setting in which to conduct patient-provider discussions of CVH. We implemented a CVH risk assessment, visualization, and decision-making tool that automatically populates with electronic health record (EHR) data during the encounter in order to encourage patient-centered CVH discussions among at-risk, yet under-treated, populations. We quantified five of the seven CVH behaviors and factors that were available in The Ohio State University Wexner Medical Center's EHR at baseline (May-July 2013) and compared values to those ascertained at one-year (May-July 2014) among intervention (n = 109) and control (n = 42) patients. The CVH of women in the intervention clinic improved relative to the metrics of body mass index (16% to 21% ideal) and diabetes (62% to 68% ideal), but not for smoking, total cholesterol, or blood pressure. Meanwhile, the CVH of women in the control clinic either held constant or worsened slightly as measured using those same metrics. Providers need easy-to-use tools at the point-of-care to help patients improve CVH. We demonstrated that the EHR could deliver such a tool using an existing American Heart Association framework, and we noted small improvements in CVH in our patient population. Future work is needed to assess how to best harness the potential of such tools in order to have the greatest impact on the CVH of a larger patient population.

Global MicroRNA Profiling for Diagnostic Appraisal of Melanocytic Spitz Tumors

The Journal of Surgical Research. Oct, 2016  |  Pubmed ID: 27664883

Melanoma skin cancer remains the leading cause of skin cancer-related deaths. Spitz lesions represent a subset of melanocytic skin lesions characterized by epithelioid or spindled melanocytes organized in nests. These lesions occupy a spectrum ranging from benign Spitz and atypical Spitz lesions all the way to malignant Spitz tumors. Appropriate management is reliant on accurate diagnostic classification, yet this effort remains challenging using current light microscopic techniques. The discovery of novel biomarkers such as microRNAs (miR) may ultimately be a useful diagnostic adjunct for the evaluation of Spitz lesions. miR expression profiles have been suggested for non-Spitz melanomas but have yet to be ascribed to Spitz lesions. We hypothesized that distinct miR expression profiles would be associated with different lesions along the Spitz spectrum.

MicroRNA Profiling of Patient Plasma for Clinical Trials Using Bioinformatics and Biostatistical Approaches

OncoTargets and Therapy. 2016  |  Pubmed ID: 27729802

MicroRNAs (miRNAs) are short noncoding RNAs that function to repress translation of mRNA transcripts and contribute to the development of cancer. We hypothesized that miRNA array-based technologies work best for miRNA profiling of patient-derived plasma samples when the techniques and patient populations are precisely defined.


Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 2016  |  Pubmed ID: 27897016

The modern healthcare and life sciences ecosystem is moving towards an increasingly open and data-centric approach to discovery science. This evolving paradigm is predicated on a complex set of information needs related to our collective ability to share, discover, reuse, integrate, and analyze open biological, clinical, and population level data resources of varying composition, granularity, and syntactic or semantic consistency. Such an evolution is further impacted by a concomitant growth in the size of data sets that can and should be employed for both hypothesis discovery and testing. When such open data can be accessed and employed for discovery purposes, a broad spectrum of high impact end-points is made possible. These span the spectrum from identification of de novo biomarker complexes that can inform precision medicine, to the repositioning or repurposing of extant agents for new and cost-effective therapies, to the assessment of population level influences on disease and wellness. Of note, these types of uses of open data can be either primary, wherein open data is the substantive basis for inquiry, or secondary, wherein open data is used to augment or enrich project-specific or proprietary data that is not open in and of itself. This workshop is concerned with the key challenges, opportunities, and methodological best practices whereby open data can be used to drive the advancement of discovery science in all of the aforementioned capacities.

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