Data Quality Assurance

Background Maternal and neonatal outcomes in the immediate post-delivery period are critical indicators of quality of care. Data on childbirth outcomes in low-income settings usually require home visits, which can be constrained by cost and access. We report on the use of a call center to measure post-discharge outcomes within a multi-site improvement study of facility-based childbirth in Uttar Pradesh, India. Methods Of women delivering at study sites eligible for inclusion, 97.9% (n = 157,689) consented to follow-up. All consenting women delivering at study facilities were eligible to receive a phone call between days eight and 42 post-partum to obtain outcomes for the seven-day period after birth. Women unable to be contacted via phone were visited at home. Outcomes, including maternal and early neonatal mortality and maternal morbidity, were ascertained using a standardized script developed from validated survey questions. Data Quality Assurance (DQA) included accuracy (double coding of calls) and validity (consistency between two calls to the same household). Regression models were used to identify factors associated with inconsistency. Findings Over 23 months, outcomes were obtained by the call center for 98.0% (154,494/157,689) consenting women and their neonates. 87.9% of call center-obtained outcomes were

This article outlines the development of a data quality framework on Azure Cloud aimed at ensuring data accuracy, completeness, and consistency. The framework is designed to address common data quality issues such as missing data, inconsistent data formats, and inaccurate data. It utilizes various Azure Cloud services such as Azure Data Factory, Azure Databricks, and Azure SQL Database to automate data quality checks and ensure data integrity. The article also discusses the benefits of using the framework, including improved decision-making, compliance with regulatory requirements, and enhanced customer experience. Overall, the data quality framework on Azure Cloud provides a comprehensive solution for ensuring data quality in business operations.

Background Maternal and neonatal outcomes in the immediate post-delivery period are critical indicators of quality of care. Data on childbirth outcomes in low-income settings usually require home visits, which can be constrained by cost and access. We report on the use of a call center to measure post-discharge outcomes within a multi-site improvement study of facility-based childbirth in Uttar Pradesh, India. Methods Of women delivering at study sites eligible for inclusion, 97.9% (n = 157,689) consented to follow-up. All consenting women delivering at study facilities were eligible to receive a phone call between days eight and 42 post-partum to obtain outcomes for the seven-day period after birth. Women unable to be contacted via phone were visited at home. Outcomes, including maternal and early neonatal mortality and maternal morbidity, were ascertained using a standardized script developed from validated survey questions. Data Quality Assurance (DQA) included accuracy (double coding of calls) and validity (consistency between two calls to the same household). Regression models were used to identify factors associated with inconsistency. Findings Over 23 months, outcomes were obtained by the call center for 98.0% (154,494/157,689) consenting women and their neonates. 87.9% of call center-obtained outcomes were

Medical record abstraction (MRA) is often cited as a significant source of error in research data, yet MRA methodology has rarely been the subject of investigation. Lack of a common framework has hindered application of the extant literature in practice, and, until now, there were no evidence-based guidelines for ensuring data quality in MRA. We aimed to identify the factors affecting the accuracy of data abstracted from medical records and to generate a framework for data quality assurance and control in MRA. Candidate factors were identified from published reports of MRA. Content validity of the top candidate factors was assessed via a four-round two-group Delphi process with expert abstractors with experience in clinical research, registries, and quality improvement. The resulting coded factors were categorized into a control theory-based framework of MRA. Coverage of the framework was evaluated using the recent published literature. Analysis of the identified articles yielded 29...

Background Maternal and neonatal outcomes in the immediate post-delivery period are critical indicators of quality of care. Data on childbirth outcomes in low-income settings usually require home visits, which can be constrained by cost and access. We report on the use of a call center to measure post-discharge outcomes within a multi-site improvement study of facility-based childbirth in Uttar Pradesh, India. Methods Of women delivering at study sites eligible for inclusion, 97.9% (n = 157,689) consented to follow-up. All consenting women delivering at study facilities were eligible to receive a phone call between days eight and 42 post-partum to obtain outcomes for the seven-day period after birth. Women unable to be contacted via phone were visited at home. Outcomes, including maternal and early neonatal mortality and maternal morbidity, were ascertained using a standardized script developed from validated survey questions. Data Quality Assurance (DQA) included accuracy (double coding of calls) and validity (consistency between two calls to the same household). Regression models were used to identify factors associated with inconsistency. Findings Over 23 months, outcomes were obtained by the call center for 98.0% (154,494/157,689) consenting women and their neonates. 87.9% of call center-obtained outcomes were

Background Maternal and neonatal outcomes in the immediate post-delivery period are critical indicators of quality of care. Data on childbirth outcomes in low-income settings usually require home visits, which can be constrained by cost and access. We report on the use of a call center to measure post-discharge outcomes within a multi-site improvement study of facility-based childbirth in Uttar Pradesh, India. Methods Of women delivering at study sites eligible for inclusion, 97.9% (n = 157,689) consented to follow-up. All consenting women delivering at study facilities were eligible to receive a phone call between days eight and 42 post-partum to obtain outcomes for the seven-day period after birth. Women unable to be contacted via phone were visited at home. Outcomes, including maternal and early neonatal mortality and maternal morbidity, were ascertained using a standardized script developed from validated survey questions. Data Quality Assurance (DQA) included accuracy (double coding of calls) and validity (consistency between two calls to the same household). Regression models were used to identify factors associated with inconsistency. Findings Over 23 months, outcomes were obtained by the call center for 98.0% (154,494/157,689) consenting women and their neonates. 87.9% of call center-obtained outcomes were

The article gives a presentation of the data quality in the life cycle of information systems, emphasizing the importance of data to and during the operation of information systems. As described, analyzed and presented the perspectives of data quality in terms of the efforts of the quality of data, data evolution, legacy information systems, information systems implementation, data quality problems, data quality the dimensions, the wear of information systems, data quality control, the processes inherent in the quality of data, and master data management. Finally, the organizations will improve the credibility of their data while aware of the potential problem of data quality and the possibility of a timely resolution; also recommends developing an “Action Plan” from sources to the results of information through the various processes involved in its evolution.El article Hace Una Presentación de la Calidad de Datos en el Ciclo de Vida de los Sistemas de Información, Haciendo Énfasis ...

This article shows how the international organizations' goal i.e to zero HIV new infection and zero decease this goal. Whereas the macroeconomic literature studies the impact of HIV/AIDS on the economic growth, this analysis assimilates the HIV/AIDS virus to a perfect foresight dynamics to study health alteration process. results found are: before the "seropositivity", vaccine ma AIDS thresholds, medical-care may slow death process. After the cross of the AIDS threshold, the organism converges to the dead zone. Indeed, HIV eradication needs population implication on the one cooperation between the low cost pharmaceutical companies and the poor countries government with the international organizations on the other hand.

This paper presents the outcomes of Data Quality Assurance exercises conducted in health facilities in three states in Nigeria. This was done to demonstrate the importance implementation. Using the Routine Data Quality Assessment Tool and an Internal Data Verification checklist, HIV data management and data quality in these facilities were assessed. The management of HIV data in m the facilities visited was below expectations and incomplete or discrepant data were found in some facilities and organisations. The paper finds that conducting Data Quality Assurance exercises are very useful in improving grant implementation. This paper recommends regular data auditing and supervisory activities for HIV programmes.

The concept of accountability within the field of organizations is not a new concept within the field of business management. This Anglo-Saxon conceptual approach does not have an exact translation into the Spanish language.
The concept is similar, depending on the context, to accountability or having timely explanations at all times about the process, the results together with the different key elements of the governance/management framework (depending on the accountability framework), such as the people responsible and the technologies used.

Cloud data accountability is considered as the set of principles, elements of practice and measurements that demonstrates the internal control activity, the correct performance of the control framework related to cloud infrastructures and services throughout the life cycle of the data whatever its status, both in real time and over time. Thus, the concept differs from the auditability of the data, since said concept is more in line with the processes of punctual/discrete/periodic evaluation of the audit (internal/external) and improvement of the government or management than with the audit process itself. interCloud data accountability is considered as the set of principles, elements of practice and measurements that demonstrates the internal control activity, the correct performance of the control framework related to cloud infrastructures and services throughout the life cycle of the data whatever its status, both in real time and over time. Thus, the concept differs from the auditability of the data, since said concept is more in line with the processes of punctual/discrete/periodic evaluation of the audit (internal/external) and improvement of the government or management than with the audit process itself. internal control (related between the two by the concept of assurance).nal control (related between the two by the concept of assurance).

This paper presents the outcomes of Data Quality Assurance exercises conducted in health facilities in three states in Nigeria. This was done to demonstrate the importance implementation. Using the Routine Data Quality Assessment Tool and an Internal Data Verification checklist, HIV data management and data quality in these facilities were assessed. The management of HIV data in m the facilities visited was below expectations and incomplete or discrepant data were found in some facilities and organisations. The paper finds that conducting Data Quality Assurance exercises are very useful in improving grant implementation. This paper recommends regular data auditing and supervisory activities for HIV programmes.