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Visualizing the quality of partially accruing data for use in decision making

Secondary use of clinical health data for near real-time public health surveillance presents challenges surrounding its utility due to data quality issues. Data used for real-time surveillance must be timely, accurate and complete if it is to be useful; if incomplete data are used for surveillance,...

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Detalles Bibliográficos
Autores principales: Eaton, Julia, Painter, Ian, Olson, Don, Lober, William B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: University of Illinois at Chicago Library 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874726/
https://www.ncbi.nlm.nih.gov/pubmed/27252794
http://dx.doi.org/10.5210/ojphi.v7i3.6096
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author Eaton, Julia
Painter, Ian
Olson, Don
Lober, William B
author_facet Eaton, Julia
Painter, Ian
Olson, Don
Lober, William B
author_sort Eaton, Julia
collection PubMed
description Secondary use of clinical health data for near real-time public health surveillance presents challenges surrounding its utility due to data quality issues. Data used for real-time surveillance must be timely, accurate and complete if it is to be useful; if incomplete data are used for surveillance, understanding the structure of the incompleteness is necessary. Such data are commonly aggregated due to privacy concerns. The Distribute project was a near real-time influenza-like-illness (ILI) surveillance system that relied on aggregated secondary clinical health data. The goal of this work is to disseminate the data quality tools developed to gain insight into the data quality problems associated with these data. These tools apply in general to any system where aggregate data are accrued over time and were created through the end-user-as-developer paradigm. Each tool was developed during the exploratory analysis to gain insight into structural aspects of data quality. Our key finding is that data quality of partially accruing data must be studied in the context of accrual lag—the difference between the time an event occurs and the time data for that event are received, i.e. the time at which data become available to the surveillance system. Our visualization methods therefore revolve around visualizing dimensions of data quality affected by accrual lag, in particular the tradeoff between timeliness and completion, and the effects of accrual lag on accuracy. Accounting for accrual lag in partially accruing data is necessary to avoid misleading or biased conclusions about trends in indicator values and data quality.
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spelling pubmed-48747262016-06-01 Visualizing the quality of partially accruing data for use in decision making Eaton, Julia Painter, Ian Olson, Don Lober, William B Online J Public Health Inform Research Article Secondary use of clinical health data for near real-time public health surveillance presents challenges surrounding its utility due to data quality issues. Data used for real-time surveillance must be timely, accurate and complete if it is to be useful; if incomplete data are used for surveillance, understanding the structure of the incompleteness is necessary. Such data are commonly aggregated due to privacy concerns. The Distribute project was a near real-time influenza-like-illness (ILI) surveillance system that relied on aggregated secondary clinical health data. The goal of this work is to disseminate the data quality tools developed to gain insight into the data quality problems associated with these data. These tools apply in general to any system where aggregate data are accrued over time and were created through the end-user-as-developer paradigm. Each tool was developed during the exploratory analysis to gain insight into structural aspects of data quality. Our key finding is that data quality of partially accruing data must be studied in the context of accrual lag—the difference between the time an event occurs and the time data for that event are received, i.e. the time at which data become available to the surveillance system. Our visualization methods therefore revolve around visualizing dimensions of data quality affected by accrual lag, in particular the tradeoff between timeliness and completion, and the effects of accrual lag on accuracy. Accounting for accrual lag in partially accruing data is necessary to avoid misleading or biased conclusions about trends in indicator values and data quality. University of Illinois at Chicago Library 2015-12-30 /pmc/articles/PMC4874726/ /pubmed/27252794 http://dx.doi.org/10.5210/ojphi.v7i3.6096 Text en This is an Open Access article. Authors own copyright of their articles appearing in the Online Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes.
spellingShingle Research Article
Eaton, Julia
Painter, Ian
Olson, Don
Lober, William B
Visualizing the quality of partially accruing data for use in decision making
title Visualizing the quality of partially accruing data for use in decision making
title_full Visualizing the quality of partially accruing data for use in decision making
title_fullStr Visualizing the quality of partially accruing data for use in decision making
title_full_unstemmed Visualizing the quality of partially accruing data for use in decision making
title_short Visualizing the quality of partially accruing data for use in decision making
title_sort visualizing the quality of partially accruing data for use in decision making
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874726/
https://www.ncbi.nlm.nih.gov/pubmed/27252794
http://dx.doi.org/10.5210/ojphi.v7i3.6096
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