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Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis

BACKGROUND: As part of a partnership between the Institute for Healthcare Improvement and the Ethiopian Federal Ministry of Health, woreda-based quality improvement collaboratives took place between November 2016 and December 2017 aiming to accelerate reduction of maternal and neonatal mortality in...

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Autores principales: Mulissa, Zewdie, Wendrad, Naod, Bitewulign, Befikadu, Biadgo, Abera, Abate, Mehiret, Alemu, Haregeweyni, Abate, Biruk, Kiflie, Abiyou, Magge, Hema, Parry, Gareth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428163/
https://www.ncbi.nlm.nih.gov/pubmed/32797091
http://dx.doi.org/10.1371/journal.pone.0237703
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author Mulissa, Zewdie
Wendrad, Naod
Bitewulign, Befikadu
Biadgo, Abera
Abate, Mehiret
Alemu, Haregeweyni
Abate, Biruk
Kiflie, Abiyou
Magge, Hema
Parry, Gareth
author_facet Mulissa, Zewdie
Wendrad, Naod
Bitewulign, Befikadu
Biadgo, Abera
Abate, Mehiret
Alemu, Haregeweyni
Abate, Biruk
Kiflie, Abiyou
Magge, Hema
Parry, Gareth
author_sort Mulissa, Zewdie
collection PubMed
description BACKGROUND: As part of a partnership between the Institute for Healthcare Improvement and the Ethiopian Federal Ministry of Health, woreda-based quality improvement collaboratives took place between November 2016 and December 2017 aiming to accelerate reduction of maternal and neonatal mortality in Lemu Bilbilu, Tanqua Abergele and Duguna Fango woredas. Before starting the collaboratives, assessments found inaccuracies in core measures obtained from Health Management Information System reports. METHODS AND RESULTS: Building on the quality improvement collaborative design, data quality improvement activities were added and we used the World Health Organization review methodology to drive a verification factor for the core measures of number of pregnant women that received their first antenatal care visit, number of pregnant women that received antenatal care on at least four visits, number of pregnant women tested for syphilis and number of births attended by skilled health personnel. Impact of the data quality improvement was assessed using interrupted time series analysis. We found accurate data across all time periods for Tanqua Abergele. In Lemu Bilbilu and Duguna Fango, data quality improved for all core metrics over time. In Duguna Fango, the verification factor for number of pregnant women that received their first antenatal care visit improved from 0.794 (95%CI 0.753, 0.836; p<0.001) pre-intervention by 0.173 (95%CI 0.128, 0.219; p<0.001) during the collaborative; and the verification factor for number of pregnant women tested for syphilis improved from 0.472 (95%CI 0.390, 0.554; p<0.001) pre-intervention by 0.460 (95%CI 0.369, 0.552; p<0.001) during the collaborative. In Lemu Bilbilu, the verification factor for number of pregnant women receiving a fourth antenatal visit rose from 0.589 (95%CI 0.513, 0.664; p<0.001) at baseline by 0.358 (95%CI 0.258, 0.458; p<0.001) post-intervention; and skilled birth attendance rose from 0.917 (95%CI 0.869, 0.965) at baseline by 0.083 (95%CI 0.030, 0.136; p<0.001) during the collaborative. CONCLUSIONS: A Data quality improvement initiative embedded within woreda clinical improvement collaborative improved accuracy of data used to monitor maternal and newborn health services in Ethiopia.
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spelling pubmed-74281632020-08-20 Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis Mulissa, Zewdie Wendrad, Naod Bitewulign, Befikadu Biadgo, Abera Abate, Mehiret Alemu, Haregeweyni Abate, Biruk Kiflie, Abiyou Magge, Hema Parry, Gareth PLoS One Research Article BACKGROUND: As part of a partnership between the Institute for Healthcare Improvement and the Ethiopian Federal Ministry of Health, woreda-based quality improvement collaboratives took place between November 2016 and December 2017 aiming to accelerate reduction of maternal and neonatal mortality in Lemu Bilbilu, Tanqua Abergele and Duguna Fango woredas. Before starting the collaboratives, assessments found inaccuracies in core measures obtained from Health Management Information System reports. METHODS AND RESULTS: Building on the quality improvement collaborative design, data quality improvement activities were added and we used the World Health Organization review methodology to drive a verification factor for the core measures of number of pregnant women that received their first antenatal care visit, number of pregnant women that received antenatal care on at least four visits, number of pregnant women tested for syphilis and number of births attended by skilled health personnel. Impact of the data quality improvement was assessed using interrupted time series analysis. We found accurate data across all time periods for Tanqua Abergele. In Lemu Bilbilu and Duguna Fango, data quality improved for all core metrics over time. In Duguna Fango, the verification factor for number of pregnant women that received their first antenatal care visit improved from 0.794 (95%CI 0.753, 0.836; p<0.001) pre-intervention by 0.173 (95%CI 0.128, 0.219; p<0.001) during the collaborative; and the verification factor for number of pregnant women tested for syphilis improved from 0.472 (95%CI 0.390, 0.554; p<0.001) pre-intervention by 0.460 (95%CI 0.369, 0.552; p<0.001) during the collaborative. In Lemu Bilbilu, the verification factor for number of pregnant women receiving a fourth antenatal visit rose from 0.589 (95%CI 0.513, 0.664; p<0.001) at baseline by 0.358 (95%CI 0.258, 0.458; p<0.001) post-intervention; and skilled birth attendance rose from 0.917 (95%CI 0.869, 0.965) at baseline by 0.083 (95%CI 0.030, 0.136; p<0.001) during the collaborative. CONCLUSIONS: A Data quality improvement initiative embedded within woreda clinical improvement collaborative improved accuracy of data used to monitor maternal and newborn health services in Ethiopia. Public Library of Science 2020-08-14 /pmc/articles/PMC7428163/ /pubmed/32797091 http://dx.doi.org/10.1371/journal.pone.0237703 Text en © 2020 Mulissa et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mulissa, Zewdie
Wendrad, Naod
Bitewulign, Befikadu
Biadgo, Abera
Abate, Mehiret
Alemu, Haregeweyni
Abate, Biruk
Kiflie, Abiyou
Magge, Hema
Parry, Gareth
Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis
title Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis
title_full Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis
title_fullStr Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis
title_full_unstemmed Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis
title_short Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis
title_sort effect of data quality improvement intervention on health management information system data accuracy: an interrupted time series analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428163/
https://www.ncbi.nlm.nih.gov/pubmed/32797091
http://dx.doi.org/10.1371/journal.pone.0237703
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