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Digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection

Dealing with the threats of vector-borne diseases necessitates robust disease surveillance systems. The gathered information from surveillance studies is used to evaluate the effectiveness of control measures. It also guides the allocation of resources within the healthcare system. The disease surve...

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Detalles Bibliográficos
Autores principales: Gupta, Sanjeev K, Singh, Himmat, Joshi, Mahesh C, Sharma, Amit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074606/
https://www.ncbi.nlm.nih.gov/pubmed/37034306
http://dx.doi.org/10.1177/20552076231164098
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author Gupta, Sanjeev K
Singh, Himmat
Joshi, Mahesh C
Sharma, Amit
author_facet Gupta, Sanjeev K
Singh, Himmat
Joshi, Mahesh C
Sharma, Amit
author_sort Gupta, Sanjeev K
collection PubMed
description Dealing with the threats of vector-borne diseases necessitates robust disease surveillance systems. The gathered information from surveillance studies is used to evaluate the effectiveness of control measures. It also guides the allocation of resources within the healthcare system. The disease surveillance data also identify high-risk populations or geographic areas to target interventions. Because of the importance of surveillance in decision-making and its timely requirement, real-time data collection is vital. A few advantages of real-time data collection apps are building powerful digital forms, exporting data for quick analysis in various formats, and being open-source. These apps automate data collection and transfer to an online server even without an internet connection. While collecting disease surveillance data digitally one crucial aspect lacking is data quality. This paper aims to present the importance of dashboards that includes paradata in improving data quality using real-time data collection tools in disease surveillance. Various types of paradata such as timestamps, geo-referencing, audio recording and so on help enhance the quality of data and can help monitor and evaluate surveillance staff. The outcomes of the paradata analysis may lead to the retraining of the surveillance team and even re-planning of surveillance. Undoubtedly, real-time data collection is the way of the future in any field-based study, and studies should be planned in conjunction with paradata to ensure that high-quality data are recorded.
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spelling pubmed-100746062023-04-06 Digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection Gupta, Sanjeev K Singh, Himmat Joshi, Mahesh C Sharma, Amit Digit Health Brief Communication Dealing with the threats of vector-borne diseases necessitates robust disease surveillance systems. The gathered information from surveillance studies is used to evaluate the effectiveness of control measures. It also guides the allocation of resources within the healthcare system. The disease surveillance data also identify high-risk populations or geographic areas to target interventions. Because of the importance of surveillance in decision-making and its timely requirement, real-time data collection is vital. A few advantages of real-time data collection apps are building powerful digital forms, exporting data for quick analysis in various formats, and being open-source. These apps automate data collection and transfer to an online server even without an internet connection. While collecting disease surveillance data digitally one crucial aspect lacking is data quality. This paper aims to present the importance of dashboards that includes paradata in improving data quality using real-time data collection tools in disease surveillance. Various types of paradata such as timestamps, geo-referencing, audio recording and so on help enhance the quality of data and can help monitor and evaluate surveillance staff. The outcomes of the paradata analysis may lead to the retraining of the surveillance team and even re-planning of surveillance. Undoubtedly, real-time data collection is the way of the future in any field-based study, and studies should be planned in conjunction with paradata to ensure that high-quality data are recorded. SAGE Publications 2023-04-02 /pmc/articles/PMC10074606/ /pubmed/37034306 http://dx.doi.org/10.1177/20552076231164098 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Brief Communication
Gupta, Sanjeev K
Singh, Himmat
Joshi, Mahesh C
Sharma, Amit
Digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection
title Digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection
title_full Digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection
title_fullStr Digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection
title_full_unstemmed Digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection
title_short Digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection
title_sort digital dashboards with paradata can improve data quality where disease surveillance relies on real-time data collection
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074606/
https://www.ncbi.nlm.nih.gov/pubmed/37034306
http://dx.doi.org/10.1177/20552076231164098
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