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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 4 of 4 Research Studies DisplayedCohen GR, Jones DJ, Heeringa J
AHRQ Author: Furukawa MF, Miller D
Leveraging diverse data sources to identify and describe U.S. health care delivery systems.
Health care delivery systems are a growing presence in the U.S., yet research is hindered by the lack of universally agreed-upon criteria to denote formal systems. This study assesses available data sources to identify and describe systems, including system members and relationships among the members.
AHRQ-authored.
Citation: Cohen GR, Jones DJ, Heeringa J .
Leveraging diverse data sources to identify and describe U.S. health care delivery systems.
eGEMS 2017 Dec 15;5(3):9. doi: 10.5334/egems.200..
Keywords: Healthcare Delivery, Data, Health Services Research (HSR), System Design
Ji X, Machiraju R, Ritter A
Visualizing article similarities via sparsified article network and map projection for systematic reviews.
In this study, the authors visualized article similarities to extend its utilization in practical settings for SR researchers, aiming to promote human comprehension of article distributions and hidden patterns. To prompt an effective visualization in an interpretable, intuitive, and scalable way, they implemented a graph-based network visualization with three network sparsification approaches and a distance-based map projection via dimensionality reduction.
AHRQ-funded; HS025047.
Citation: Ji X, Machiraju R, Ritter A .
Visualizing article similarities via sparsified article network and map projection for systematic reviews.
Stud Health Technol Inform 2017;245:422-26.
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Keywords: Data, Evidence-Based Practice, Health Services Research (HSR), Research Methodologies
Groeneveld PW, Rumsfeld JS
Can big data fulfill its promise?
This article discusses the pros and cons of using big data analytics in healthcare. The authors note that the rise of big data analytics in health care settings has promise. However, they assert that it is critical to recognize that the fundamental pitfalls of observational data analysis cannot be ignored, and in fact the risks of such pitfalls demand rigorous scientific testing and novel methods for peer review of big data analytic models.
AHRQ-funded; HS023615.
Citation: Groeneveld PW, Rumsfeld JS .
Can big data fulfill its promise?
Circ Cardiovasc Qual Outcomes 2016 Nov;9(6):679-82. doi: 10.1161/circoutcomes.116.003097..
Keywords: Data, Health Services Research (HSR)
Terza JV
Inference using sample means of parametric nonlinear data transformations.
AHRQ-funded; HS017434.
Citation: Terza JV .
Inference using sample means of parametric nonlinear data transformations.
Health Serv Res 2016 Jun;51(3):1109-13. doi: 10.1111/1475-6773.12494.
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Keywords: Research Methodologies, Health Services Research (HSR), Data