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Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia
BACKGROUND: Health management information system (HMIS) is a system whereby health data are recorded, stored, retrieved and processed to improve decision-making. HMIS data quality should be monitored routinely as production of high quality statistics depends on assessment of data quality and actions...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423785/ https://www.ncbi.nlm.nih.gov/pubmed/30885204 http://dx.doi.org/10.1186/s12913-019-3991-7 |
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author | Endriyas, Misganu Alano, Abraham Mekonnen, Emebet Ayele, Sinafikish Kelaye, Temesgen Shiferaw, Mekonnen Misganaw, Tebeje Samuel, Teka Hailemariam, Tesfahun Hailu, Samuel |
author_facet | Endriyas, Misganu Alano, Abraham Mekonnen, Emebet Ayele, Sinafikish Kelaye, Temesgen Shiferaw, Mekonnen Misganaw, Tebeje Samuel, Teka Hailemariam, Tesfahun Hailu, Samuel |
author_sort | Endriyas, Misganu |
collection | PubMed |
description | BACKGROUND: Health management information system (HMIS) is a system whereby health data are recorded, stored, retrieved and processed to improve decision-making. HMIS data quality should be monitored routinely as production of high quality statistics depends on assessment of data quality and actions taken to improve it. Thus, this study assessed accuracy of the routine HMIS data. METHODS: Facility based cross-sectional study was conducted in Southern Nations Nationalities and People’s region in 2017. Document review was done in 163 facilities of different levels. Statistical Package for the Social Sciences (SPSS) for windows version 20 was used to perform data analysis. Data accuracy was presented in terms of mean and standard deviation of data verification factor. RESULTS: Though inaccuracy was noted for all data elements, 96.9 and 84.7% of facilities reported institutional maternal death and skilled birth attendance within acceptable range respectively while confirmed malaria (45.4%), antenatal care fourth visit (46.6%), postnatal care (55.2%), fully immunized (55.8%), severe acute malnutrition (54.6%) and total malaria (50.3%) were reported accurately only by about half of facilities. Antenatal care fourth visit was over reported by 24% while total malaria was under reported by 28%. Reasons for variations included technical, behavioral and organizational factors. CONCLUSIONS: Majority of facilities over reported services while under reporting diseases. Data quality should be monitored routinely against data quality parameters quantitatively and/or qualitatively to catch-up country’s information revolution agenda. |
format | Online Article Text |
id | pubmed-6423785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64237852019-03-28 Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia Endriyas, Misganu Alano, Abraham Mekonnen, Emebet Ayele, Sinafikish Kelaye, Temesgen Shiferaw, Mekonnen Misganaw, Tebeje Samuel, Teka Hailemariam, Tesfahun Hailu, Samuel BMC Health Serv Res Research Article BACKGROUND: Health management information system (HMIS) is a system whereby health data are recorded, stored, retrieved and processed to improve decision-making. HMIS data quality should be monitored routinely as production of high quality statistics depends on assessment of data quality and actions taken to improve it. Thus, this study assessed accuracy of the routine HMIS data. METHODS: Facility based cross-sectional study was conducted in Southern Nations Nationalities and People’s region in 2017. Document review was done in 163 facilities of different levels. Statistical Package for the Social Sciences (SPSS) for windows version 20 was used to perform data analysis. Data accuracy was presented in terms of mean and standard deviation of data verification factor. RESULTS: Though inaccuracy was noted for all data elements, 96.9 and 84.7% of facilities reported institutional maternal death and skilled birth attendance within acceptable range respectively while confirmed malaria (45.4%), antenatal care fourth visit (46.6%), postnatal care (55.2%), fully immunized (55.8%), severe acute malnutrition (54.6%) and total malaria (50.3%) were reported accurately only by about half of facilities. Antenatal care fourth visit was over reported by 24% while total malaria was under reported by 28%. Reasons for variations included technical, behavioral and organizational factors. CONCLUSIONS: Majority of facilities over reported services while under reporting diseases. Data quality should be monitored routinely against data quality parameters quantitatively and/or qualitatively to catch-up country’s information revolution agenda. BioMed Central 2019-03-18 /pmc/articles/PMC6423785/ /pubmed/30885204 http://dx.doi.org/10.1186/s12913-019-3991-7 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Endriyas, Misganu Alano, Abraham Mekonnen, Emebet Ayele, Sinafikish Kelaye, Temesgen Shiferaw, Mekonnen Misganaw, Tebeje Samuel, Teka Hailemariam, Tesfahun Hailu, Samuel Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia |
title | Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia |
title_full | Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia |
title_fullStr | Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia |
title_full_unstemmed | Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia |
title_short | Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia |
title_sort | understanding performance data: health management information system data accuracy in southern nations nationalities and people’s region, ethiopia |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423785/ https://www.ncbi.nlm.nih.gov/pubmed/30885204 http://dx.doi.org/10.1186/s12913-019-3991-7 |
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