Characteristics of tissue-centric biomedical researchers using a survey and cluster analysis
Journal of the American Society for Information Science and Technology (Impact Factor: 1.85). 06/2008; 59(8):1210-1223. DOI: 10.1002/asi.20807
The objective of this study was to characterize the types of tissue-centric users based on tissue use, requirements, and their job or work-related variables at the University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA. A self-reporting questionnaire was distributed to biomedical researchers at the UPMC. Descriptive and cluster analyses were performed to identify and characterize the complex types of tissue-based researchers. A total of 62 respondents completed the survey, and two clusters were identified based on all variables. Two distinct groups of tissue-centric users made direct use of tissue samples for their research as well as associated information, while a third group of indirect users required only the associated information. The study shows that tissue-centric users were composed of various types. These types were distinguished in terms of tissue use and data requirements, as well as by their work or research-related activities. © 2008 Wiley Periodicals, Inc.
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ABSTRACT: Introduction. This study was undertaken to develop an information requirement framework for scientists who use biological samples and related data in their research. Method. A self-reporting questionnaire completed by 137 respondents was used to collect data regarding demographics, bio-sample management, bio-sample use and requirements, data requirements, and work and research-related roles and activities. Analysis. Descriptive and TwoStep Cluster analyses were used to analyse the survey data necessary for developing a framework of information requirements. Results. Two groups of biomedical scientists (clinical group and basic scientist group) were formed by their distinct characteristics. A conceptual framework of information requirements for bio-sample researchers was formed. The study determined the following as core components: work roles, tasks, characteristics of data and bio-sample needs, factors affecting information seeking, and outcomes. Conclusions. This study will enable the system designer to understand bio-sample users by means of their information requirements resulted in the proposed framework. Future empirical studies should assess potential users, types of information required depending on their work-related roles, factors affecting information seeking, and the evaluation of information seeking effectiveness.
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ABSTRACT: Accelerating the translation of new scientific discoveries to improve human health and disease management is the overall goal of a series of initiatives integrated in the National Institutes of Health (NIH) "Roadmap for Medical Research." The Clinical and Translational Science Award (CTSA) program is, arguably, the most visible component of the NIH Roadmap providing resources to institutions to transform their clinical and translational research enterprises along the goals of the Roadmap. The CTSA program emphasizes biomedical informatics as a critical component for the accomplishment of the NIH's translational objectives. To be optimally effective, emerging biomedical informatics programs must link with the information technology platforms of the enterprise clinical operations within academic health centers.This report details one academic health center's transdisciplinary initiative to create an integrated academic discipline of biomedical informatics through the development of its infrastructure for clinical and translational science infrastructure and response to the CTSA mechanism. This approach required a detailed informatics strategy to accomplish these goals. This transdisciplinary initiative was the impetus for creation of a specialized biomedical informatics core, the Center for Biomedical Informatics (CBI). Development of the CBI codified the need to incorporate medical informatics including quality and safety informatics and enterprise clinical information systems within the CBI. This article describes the steps taken to develop the biomedical informatics infrastructure, its integration with clinical systems at one academic health center, successes achieved, and barriers encountered during these efforts.
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