2018
DOI: 10.1007/s10461-018-2104-7
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An End-User Participatory Approach to Collaboratively Refine HIV Care Data, The New York State Experience

Abstract: Existing data dissemination structures primarily rely on top-down approaches. Unless designed with the end user in mind, this may impair data-driven clinical improvements to Human Immunodeficiency Virus (HIV) prevention and care. In this study, we implemented a data visualization activity to create region-specific data presentations collaboratively with HIV providers, consumers of HIV care, and New York State (NYS) Department of Health AIDS Institute staff for use in local HIV care decision-making. Data from t… Show more

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Cited by 5 publications
(2 citation statements)
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“…The use of mobile devices to improve the data collection, processing, and analysis processes for epidemiology and surveillance has contributed to great advances in public health in the aspects of innovation and digital transformation in this area [9][10][11][12][13][14][15]. With the evolution and ubiquity of mobile devices and their operating systems, in addition to increasing digital inclusion and internet connectivity, research and collaborative strategies have been adopted to improve the quality of information generated in health, especially in the understanding of epidemiological patterns [16][17][18]. Strategies for monitoring respiratory, diarrheal, and rash syndromes due to arboviruses are examples of how to drive digital disease detection platforms to address the production of strategic information for health surveillance based on crowdsourcing in the American, European, African, and Asian continents; some platforms include Flu Near You, Influenza.Net, AfyaData, Vigilant-e, Saúde na Copa, and Guardians of Health [4,[19][20][21][22][23][24][25][26][27][28][29].…”
Section: Introductionmentioning
confidence: 99%
“…The use of mobile devices to improve the data collection, processing, and analysis processes for epidemiology and surveillance has contributed to great advances in public health in the aspects of innovation and digital transformation in this area [9][10][11][12][13][14][15]. With the evolution and ubiquity of mobile devices and their operating systems, in addition to increasing digital inclusion and internet connectivity, research and collaborative strategies have been adopted to improve the quality of information generated in health, especially in the understanding of epidemiological patterns [16][17][18]. Strategies for monitoring respiratory, diarrheal, and rash syndromes due to arboviruses are examples of how to drive digital disease detection platforms to address the production of strategic information for health surveillance based on crowdsourcing in the American, European, African, and Asian continents; some platforms include Flu Near You, Influenza.Net, AfyaData, Vigilant-e, Saúde na Copa, and Guardians of Health [4,[19][20][21][22][23][24][25][26][27][28][29].…”
Section: Introductionmentioning
confidence: 99%
“…Hague et al [16] present an application of a data-to-care approach implemented by the Massachusetts Department of Public Health involving the generation of an out-of-care list of patients lacking a laboratory test result for at least a 6 month period. Swain et al [17] describe their process in the production of data packages that integrate surveillance and treatment data. The data packages were customized for regional use by twelve health care organizations engaged in the NYLinks initiative.…”
mentioning
confidence: 99%