The Experts below are selected from a list of 211926 Experts worldwide ranked by ideXlab platform
Ghulam Rasool Haider - One of the best experts on this subject based on the ideXlab platform.
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Measuring management’s perspective of Data Quality in Pakistan’s Tuberculosis control programme: a test-based approach to identify Data Quality dimensions
BMC Research Notes, 2018Co-Authors: Syed Mustafa Ali, Naveed Anjum, Maged N. Kamel Boulos, Muhammad Ishaq, Javariya Aamir, Ghulam Rasool HaiderAbstract:Background Data Quality is core theme of programme’s performance assessment and many organizations do not have any Data Quality Improvement strategy, wherein Data Quality dimensions and Data Quality assessment framework are important constituents. As there is limited published research about the Data Quality specifics that are relevant to the context of Pakistan’s Tuberculosis control programme, this study aims at identifying the applicable Data Quality dimensions by using the ‘fitness-for-purpose’ perspective. Results Forty-two respondents pooled a total of 473 years of professional experience, out of which 223 years (47%) were in TB control related programmes. Based on the responses against 11 practical cases, adopted from the routine recording and reporting system of Pakistan’s TB control programme (real identities of patient were masked), completeness, accuracy, consistency, vagueness, uniqueness and timeliness are the applicable Data Quality dimensions relevant to the programme’s context, i.e. work settings and field of practice. Conclusion Based on a ‘fitness-for-purpose’ approach to Data Quality, this study used a test-based approach to measure management’s perspective and identified Data Quality dimensions pertinent to the programme and country specific requirements. Implementation of a Data Quality Improvement strategy and achieving enhanced Data Quality would greatly help organizations in promoting Data use for informed decision making.
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Measuring management's perspective of Data Quality in Pakistan's Tuberculosis control programme: a test-based approach to identify Data Quality dimensions.
BMC Research Notes, 2018Co-Authors: Syed Mustafa Ali, Naveed Anjum, Muhammad Ishaq, Javariya Aamir, Maged N. Kamel Boulos, Ghulam Rasool HaiderAbstract:Data Quality is core theme of programme’s performance assessment and many organizations do not have any Data Quality Improvement strategy, wherein Data Quality dimensions and Data Quality assessment framework are important constituents. As there is limited published research about the Data Quality specifics that are relevant to the context of Pakistan’s Tuberculosis control programme, this study aims at identifying the applicable Data Quality dimensions by using the ‘fitness-for-purpose’ perspective. Forty-two respondents pooled a total of 473 years of professional experience, out of which 223 years (47%) were in TB control related programmes. Based on the responses against 11 practical cases, adopted from the routine recording and reporting system of Pakistan’s TB control programme (real identities of patient were masked), completeness, accuracy, consistency, vagueness, uniqueness and timeliness are the applicable Data Quality dimensions relevant to the programme’s context, i.e. work settings and field of practice. Based on a ‘fitness-for-purpose’ approach to Data Quality, this study used a test-based approach to measure management’s perspective and identified Data Quality dimensions pertinent to the programme and country specific requirements. Implementation of a Data Quality Improvement strategy and achieving enhanced Data Quality would greatly help organizations in promoting Data use for informed decision making.
Hema Magge - One of the best experts on this subject based on the ideXlab platform.
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effect of Data Quality Improvement intervention on health management information system Data accuracy an interrupted time series analysis
PLOS ONE, 2020Co-Authors: Zewdie Mulissa, Naod Wendrad, Befikadu Bitewulign, Abera Biadgo, Mehiret Abate, Haregeweyni Alemu, Biruk Abate, Abiyou Kiflie, Hema MaggeAbstract: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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Effect of Data Quality Improvement intervention on health management information system Data accuracy: An interrupted time series analysis.
PLOS ONE, 2020Co-Authors: Zewdie Mulissa, Naod Wendrad, Befikadu Bitewulign, Abera Biadgo, Mehiret Abate, Haregeweyni Alemu, Biruk Abate, Abiyou Kiflie, Hema Magge, Gareth ParryAbstract: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
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21 Effect of Data Quality Improvement intervention on health management information system Data accuracy: an interrupted time series analysis
Abstracts, 2019Co-Authors: Zewdie Mulissa, Naod Wendrad, Befikadu Bitewulign, Abera Biadgo, Mehiret Abate, Haregeweyni Alemu, Biruk Abate, Abiyou Kiflie, Hema Magge, Gareth ParryAbstract:Background As part of a partnership between the Institute for Healthcare Improvement and Ethiopian Federal Ministry of Health (FMoH), a Maternal and Neonatal Health (MNH) Collaborative 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 districts. Before starting the MNH Collaborative, assessments found inaccuracies in core process and outcome Data obtained from Health Management Information System (HMIS) reports. Objectives We aimed to assess the effect of an initiative to improve Data Quality in the MNH Collaborative. Methods Building on the core MNH Collaborative design, Data Quality Improvement activities were added. We used the WHO review methodology to derive a verification factor (VF) for the core measures of number of pregnant women that received antenatal care first 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 was assessed using interrupted time series. Results Data Quality improved across all measures, for example, in Duguna Fango, the VF for number of pregnant women that received antenatal care first visit improved from 0.79 (quartiles 0.73, 0.85) pre-intervention to 0.99 (0.93, 1.00) post intervention, p Conclusions A Data Quality Improvement initiative significantly improved accuracy of Data used to monitor maternal progress of this MNH Collaborative in Ethiopia.
Michael D. Kluger - One of the best experts on this subject based on the ideXlab platform.
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Ensuring safe surgical care across resource settings via surgical outcomes Data & Quality Improvement initiatives.
International Journal of Surgery, 2019Co-Authors: Belain Eyob, Marissa A. Boeck, Patrick Fasioen, Shamir O. Cawich, Michael D. KlugerAbstract:Staggering statistics regarding the global burden of disease due to lack of surgical care worldwide has been gaining attention in the global health literature over the last 10 years. The Lancet Commission on Global Surgery reported that 16.9 million lives were lost due to an absence of surgical care in 2010, equivalent to 33% of all deaths worldwide. Although Data from low- and middle-income countries (LMICs) are limited, recent investigations, such as the African Surgical Outcomes Study, highlight that despite operating on low risk patients, there is increased postoperative mortality in LMICs versus higher-resource settings, a majority of which occur secondary to seemingly preventable complications like surgical site infections. We propose that implementing creative, low-cost surgical outcomes monitoring and select Quality Improvement systems proven effective in high-income countries, such as surgical infection prevention programs and safety checklists, can enhance the delivery of safe surgical care in existing LMIC surgical systems. While efforts to initiate and expand surgical access and capacity continues to deserve attention in the global health community, here we advocate for creative modifications to current service structures, such as promoting a culture of safety, employing technology and mobile health (mHealth) for patient Data collection and follow-up, and harnessing partnerships for information sharing, to create a framework for improving morbidity and mortality in responsible, scalable, and sustainable ways.
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ensuring safe surgical care across resource settings via surgical outcomes Data Quality Improvement initiatives
International Journal of Surgery, 2019Co-Authors: Belain Eyob, Marissa A. Boeck, Patrick Fasioen, Shamir O. Cawich, Michael D. KlugerAbstract:Staggering statistics regarding the global burden of disease due to lack of surgical care worldwide has been gaining attention in the global health literature over the last 10 years. The Lancet Commission on Global Surgery reported that 16.9 million lives were lost due to an absence of surgical care in 2010, equivalent to 33% of all deaths worldwide. Although Data from low- and middle-income countries (LMICs) are limited, recent investigations, such as the African Surgical Outcomes Study, highlight that despite operating on low risk patients, there is increased postoperative mortality in LMICs versus higher-resource settings, a majority of which occur secondary to seemingly preventable complications like surgical site infections. We propose that implementing creative, low-cost surgical outcomes monitoring and select Quality Improvement systems proven effective in high-income countries, such as surgical infection prevention programs and safety checklists, can enhance the delivery of safe surgical care in existing LMIC surgical systems. While efforts to initiate and expand surgical access and capacity continues to deserve attention in the global health community, here we advocate for creative modifications to current service structures, such as promoting a culture of safety, employing technology and mobile health (mHealth) for patient Data collection and follow-up, and harnessing partnerships for information sharing, to create a framework for improving morbidity and mortality in responsible, scalable, and sustainable ways.
Syed Mustafa Ali - One of the best experts on this subject based on the ideXlab platform.
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Measuring management’s perspective of Data Quality in Pakistan’s Tuberculosis control programme: a test-based approach to identify Data Quality dimensions
BMC Research Notes, 2018Co-Authors: Syed Mustafa Ali, Naveed Anjum, Maged N. Kamel Boulos, Muhammad Ishaq, Javariya Aamir, Ghulam Rasool HaiderAbstract:Background Data Quality is core theme of programme’s performance assessment and many organizations do not have any Data Quality Improvement strategy, wherein Data Quality dimensions and Data Quality assessment framework are important constituents. As there is limited published research about the Data Quality specifics that are relevant to the context of Pakistan’s Tuberculosis control programme, this study aims at identifying the applicable Data Quality dimensions by using the ‘fitness-for-purpose’ perspective. Results Forty-two respondents pooled a total of 473 years of professional experience, out of which 223 years (47%) were in TB control related programmes. Based on the responses against 11 practical cases, adopted from the routine recording and reporting system of Pakistan’s TB control programme (real identities of patient were masked), completeness, accuracy, consistency, vagueness, uniqueness and timeliness are the applicable Data Quality dimensions relevant to the programme’s context, i.e. work settings and field of practice. Conclusion Based on a ‘fitness-for-purpose’ approach to Data Quality, this study used a test-based approach to measure management’s perspective and identified Data Quality dimensions pertinent to the programme and country specific requirements. Implementation of a Data Quality Improvement strategy and achieving enhanced Data Quality would greatly help organizations in promoting Data use for informed decision making.
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Measuring management's perspective of Data Quality in Pakistan's Tuberculosis control programme: a test-based approach to identify Data Quality dimensions.
BMC Research Notes, 2018Co-Authors: Syed Mustafa Ali, Naveed Anjum, Muhammad Ishaq, Javariya Aamir, Maged N. Kamel Boulos, Ghulam Rasool HaiderAbstract:Data Quality is core theme of programme’s performance assessment and many organizations do not have any Data Quality Improvement strategy, wherein Data Quality dimensions and Data Quality assessment framework are important constituents. As there is limited published research about the Data Quality specifics that are relevant to the context of Pakistan’s Tuberculosis control programme, this study aims at identifying the applicable Data Quality dimensions by using the ‘fitness-for-purpose’ perspective. Forty-two respondents pooled a total of 473 years of professional experience, out of which 223 years (47%) were in TB control related programmes. Based on the responses against 11 practical cases, adopted from the routine recording and reporting system of Pakistan’s TB control programme (real identities of patient were masked), completeness, accuracy, consistency, vagueness, uniqueness and timeliness are the applicable Data Quality dimensions relevant to the programme’s context, i.e. work settings and field of practice. Based on a ‘fitness-for-purpose’ approach to Data Quality, this study used a test-based approach to measure management’s perspective and identified Data Quality dimensions pertinent to the programme and country specific requirements. Implementation of a Data Quality Improvement strategy and achieving enhanced Data Quality would greatly help organizations in promoting Data use for informed decision making.
Javariya Aamir - One of the best experts on this subject based on the ideXlab platform.
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Measuring management’s perspective of Data Quality in Pakistan’s Tuberculosis control programme: a test-based approach to identify Data Quality dimensions
BMC Research Notes, 2018Co-Authors: Syed Mustafa Ali, Naveed Anjum, Maged N. Kamel Boulos, Muhammad Ishaq, Javariya Aamir, Ghulam Rasool HaiderAbstract:Background Data Quality is core theme of programme’s performance assessment and many organizations do not have any Data Quality Improvement strategy, wherein Data Quality dimensions and Data Quality assessment framework are important constituents. As there is limited published research about the Data Quality specifics that are relevant to the context of Pakistan’s Tuberculosis control programme, this study aims at identifying the applicable Data Quality dimensions by using the ‘fitness-for-purpose’ perspective. Results Forty-two respondents pooled a total of 473 years of professional experience, out of which 223 years (47%) were in TB control related programmes. Based on the responses against 11 practical cases, adopted from the routine recording and reporting system of Pakistan’s TB control programme (real identities of patient were masked), completeness, accuracy, consistency, vagueness, uniqueness and timeliness are the applicable Data Quality dimensions relevant to the programme’s context, i.e. work settings and field of practice. Conclusion Based on a ‘fitness-for-purpose’ approach to Data Quality, this study used a test-based approach to measure management’s perspective and identified Data Quality dimensions pertinent to the programme and country specific requirements. Implementation of a Data Quality Improvement strategy and achieving enhanced Data Quality would greatly help organizations in promoting Data use for informed decision making.
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Measuring management's perspective of Data Quality in Pakistan's Tuberculosis control programme: a test-based approach to identify Data Quality dimensions.
BMC Research Notes, 2018Co-Authors: Syed Mustafa Ali, Naveed Anjum, Muhammad Ishaq, Javariya Aamir, Maged N. Kamel Boulos, Ghulam Rasool HaiderAbstract:Data Quality is core theme of programme’s performance assessment and many organizations do not have any Data Quality Improvement strategy, wherein Data Quality dimensions and Data Quality assessment framework are important constituents. As there is limited published research about the Data Quality specifics that are relevant to the context of Pakistan’s Tuberculosis control programme, this study aims at identifying the applicable Data Quality dimensions by using the ‘fitness-for-purpose’ perspective. Forty-two respondents pooled a total of 473 years of professional experience, out of which 223 years (47%) were in TB control related programmes. Based on the responses against 11 practical cases, adopted from the routine recording and reporting system of Pakistan’s TB control programme (real identities of patient were masked), completeness, accuracy, consistency, vagueness, uniqueness and timeliness are the applicable Data Quality dimensions relevant to the programme’s context, i.e. work settings and field of practice. Based on a ‘fitness-for-purpose’ approach to Data Quality, this study used a test-based approach to measure management’s perspective and identified Data Quality dimensions pertinent to the programme and country specific requirements. Implementation of a Data Quality Improvement strategy and achieving enhanced Data Quality would greatly help organizations in promoting Data use for informed decision making.
