Grid-enabled measures: using Science 2.0 to standardize measures and share data

National Cancer Institute, 6130 Executive Boulevard, Bethesda, MD 20892-7365, USA.
American journal of preventive medicine (Impact Factor: 4.53). 05/2011; 40(5 Suppl 2):S134-43.
Source: PubMed


Scientists are taking advantage of the Internet and collaborative web technology to accelerate discovery in a massively connected, participative environment--a phenomenon referred to by some as Science 2.0. As a new way of doing science, this phenomenon has the potential to push science forward in a more efficient manner than was previously possible. The Grid-Enabled Measures (GEM) database has been conceptualized as an instantiation of Science 2.0 principles by the National Cancer Institute (NCI) with two overarching goals: (1) promote the use of standardized measures, which are tied to theoretically based constructs; and (2) facilitate the ability to share harmonized data resulting from the use of standardized measures. The first is accomplished by creating an online venue where a virtual community of researchers can collaborate together and come to consensus on measures by rating, commenting on, and viewing meta-data about the measures and associated constructs. The second is accomplished by connecting the constructs and measures to an ontological framework with data standards and common data elements such as the NCI Enterprise Vocabulary System (EVS) and the cancer Data Standards Repository (caDSR). This paper will describe the web 2.0 principles on which the GEM database is based, describe its functionality, and discuss some of the important issues involved with creating the GEM database such as the role of mutually agreed-on ontologies (i.e., knowledge categories and the relationships among these categories--for data sharing).

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Available from: Paul Courtney, Sep 29, 2015
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    • "A new initiative led by the US National Cancer Institute, using a web-based Grid-Enabled Measures (GEM) database, is collating constructs and measures relevant to shared decision making, including data on their development, psychometric properties, and availability [65]. The GEM-SDM database also allows peers to post informal reviews of the instruments, which will provide an important source of guidance to researchers in the field. "
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    ABSTRACT: Establishing the effectiveness of patient decision aids (PtDA) requires evidence that PtDAs improve the quality of the decision-making process and the quality of the choice made, or decision quality. The aim of this paper is to review the theoretical and empirical evidence for PtDA effectiveness and discuss emerging practical and research issues in the measurement of effectiveness. This updated overview incorporates: a) an examination of the instruments used to measure five key decision-making process constructs (i.e., recognize decision, feel informed about options and outcomes, feel clear about goals and preferences, discuss goals and preferences with health care provider, and be involved in decisions) and decision quality constructs (i.e., knowledge, realistic expectations, values-choice agreement) within the 86 trials in the Cochrane review; and b) a summary of the 2011 Cochrane Collaboration's review of PtDAs for these key constructs. Data on the constructs and instruments used were extracted independently by two authors from the 86 trials and any disagreements were resolved by discussion, with adjudication by a third party where required. The 86 studies provide considerable evidence that PtDAs improve the decision-making process and decision quality. A majority of the studies (76/86; 88%) measured at least one of the key decision-making process or decision quality constructs. Seventeen different measurement instruments were used to measure decision-making process constructs, but no single instrument covered all five constructs. The Decisional Conflict Scale was most commonly used (n = 47), followed by the Control Preference Scale (n = 9). Many studies reported one or more constructs of decision quality, including knowledge (n = 59), realistic expectation of risks and benefits (n = 21), and values-choice agreement (n = 13). There was considerable variability in how values-choice agreement was defined and determined. No study reported on all key decision-making process and decision quality constructs. Evidence of PtDA effectiveness in improving the quality of the decision-making process and decision quality is strong and growing. There is not, however, consensus or standardization of measurement for either the decision-making process or decision quality. Additional work is needed to develop and evaluate measurement instruments and further explore theoretical issues to advance future research on PtDA effectiveness.
    BMC Medical Informatics and Decision Making 11/2013; 13 Suppl 2(Suppl 2):S12. DOI:10.1186/1472-6947-13-S2-S12 · 1.83 Impact Factor
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    • "In order to do this effectively, a number of data sets need to be leveraged, which means that relationships between those datasets need to be made explicit so that the data may be harmonized. One goal of this effort is to enable new analyses of complex behaviors and systems [1] [2] thereby allowing researchers and policymakers to explore hypotheses about possible correlations between intervention strategies and possible health outcomes, such as the Statewide Health Information Network of New York (SHIN-NY). "
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    ABSTRACT: Increasingly, experts and interested laypeople are turning to the explosion of online data to form and explore hypotheses about relationships between public health intervention strategies and their possible impacts. We have engaged in a multi-year collaboration to use and design semantic techniques and tools to support the current and next generation of these explorations. We introduce a tool, qb.js, to enable access to multidimensional statistical data in ways that allow non-specialists to explore and create specific visualizations of that data. We focus on explorations of health data - in particular aimed at helping to support the formation and analysis of hypotheses about public health intervention strategies and their correlation with health-related behavior changes. We used qb.js to formulate and explore the hypothesis that youth tobacco access laws have consistent, measurable impacts on the rate of change in cigarette smoking among high school students over time. While focused in this instance on one particular intervention strategy (i.e., limiting youth access to tobacco), this analytics platform may be used for a wide range of correlational analyses. To address this hypothesis, we converted population science data on tobacco-related policy and behavior from Impacteen to a Resource Description framework (RDF) representation that was annotated with the RDF Data Cube vocabulary. A Semantic Data Dictionary enabled mapping between the original datasets and the RDF representation. This allowed for the creation and publication of data visualizations using qb.js. The RDF Data Cube representation made it possible to discover a significant downward effect from the introduction of nine youth tobacco access laws on the rate of change in smoking prevalence among high school-aged youth.
    System Sciences (HICSS), 2013 46th Hawaii International Conference on; 01/2013
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    • "GEM leverages the principles of technology-mediated social participation (TMSP), such as open access, collective intelligence, and data-driven decision making, to build a knowledge base that encourages and supports collaboration [17]. The community-generated content on GEM consists of constructs and their associated measures, along with information about (i.e., meta-data) constructs and measures, such as theoretical foundation, reliability, and mode of administration, which provide the information needed—together with the qualitative data from user comments—to rate and assess each construct and measure [12]. This type of functionality is becoming ubiquitous with many websites that seek a bi-lateral exchange of information between the website and the user. "
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    ABSTRACT: Background The field of implementation science (IS) encompasses a broad range of constructs and uses measures from a variety of disciplines. However, there has been little standardization of measures or agreement on definitions of constructs across different studies, fields, authors, or research groups. Methods We describe a collaborative, web-based activity using the United States National Cancer Institute’s (NCI) Grid-Enabled Measures (GEM) portal that uses a wiki platform to focus discussion and engage the research community to enhance the quality and harmonization of measures for IS health-related research and practice. We present the history, process, and preliminary data from the GEM Dissemination & Implementation (D&I) Campaign on IS measurement. Results The GEM D&I Campaign has been ongoing for eight weeks as of this writing, and has used a combination of expert opinion and crowd-sourcing approaches. To date it has listed definitions for 45 constructs and summarized information on 120 measures. Usage of the website peaked at a rate of 124 views from 89 visitors on week seven. Users from seven countries have contributed measures and/or constructs, shared experience in using different measures, contributed comments, and identified research gaps and needs. Conclusion Thus far, this campaign has provided information about different IS measures, their associated characteristics, and comments. The next step is to rate these measures for quality and practicality. This resource and ongoing activity have potential to advance the quality and harmonization of IS measures and constructs, and we invite readers to contribute to the process.
    Implementation Science 12/2012; 7(1):119. DOI:10.1186/1748-5908-7-119 · 4.12 Impact Factor
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