ArticleBMC medical informatics and decision making2008
Evaluation of SOVAT: an OLAP-GIS decision support system for community health assessment data analysis.
Article in BMC medical informatics and decision making, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- User-Centered Design to Enhance mHealth Systems for Individuals With Dexterity Impairments: Accessibility and Usability Study.JMIR human factors · 2022Article
- Mobile technologies and geographic information systems to improve health care systems: a literature review.JMIR mHealth and uHealth · 2014Review
- Applications of electronic health information in public health: uses, opportunities & barriers.EGEMS (Washington, DC) · 2013Article
- Displaying R spatial statistics on Google dynamic maps with web applications created by Rwui.International journal of health geographics · 2012Article
- Easier surveillance of climate-related health vulnerabilities through a Web-based spatial OLAP application.International journal of health geographics · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundData analysis in community health assessment (CHA) involves the collection, integration, and analysis of large numerical and spatial data sets in order to identify health priorities. Geographic Information Systems (GIS) enable for management and analysis using spatial data, but have limitations in performing analysis of numerical data because of its traditional database architecture.On-Line Analytical Processing (OLAP) is a multidimensional datawarehouse designed to facilitate querying of large numerical data. Coupling the spatial capabilities of GIS with the numerical analysis of OLAP, might enhance CHA data analysis. OLAP-GIS systems have been developed by university researchers and corporations, yet their potential for CHA data analysis is not well understood. To evaluate the potential of an OLAP-GIS decision support system for CHA problem solving, we compared OLAP-GIS to the standard information technology (IT) currently used by many public health professionals.
methodsSOVAT, an OLAP-GIS decision support system developed at the University of Pittsburgh, was compared against current IT for data analysis for CHA. For this study, current IT was considered the combined use of SPSS and GIS ("SPSS-GIS"). Graduate students, researchers, and faculty in the health sciences at the University of Pittsburgh were recruited. Each round consisted of: an instructional video of the system being evaluated, two practice tasks, five assessment tasks, and one post-study questionnaire. Objective and subjective measurement included: task completion time, success in answering the tasks, and system satisfaction.
resultsThirteen individuals participated. Inferential statistics were analyzed using linear mixed model analysis. SOVAT was statistically significant (alpha = .01) from SPSS-GIS for satisfaction and time (p < .002). Descriptive results indicated that participants had greater success in answering the tasks when using SOVAT as compared to SPSS-GIS.
conclusionUsing SOVAT, tasks were completed more efficiently, with a higher rate of success, and with greater satisfaction, than the combined use of SPSS and GIS. The results from this study indicate a potential for OLAP-GIS decision support systems as a valuable tool for CHA data analysis.
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