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IMPLEMENTATION TOOLHIV STRATEGIC INFORMATION FOR IMPACTDATA QUALITY ASSESSMENT OF NATIONAL AND PARTNER HIV TREATMENT AND PATIENT MONITORING SYSTEMS AUGUST 2018HIV STRATEGIC INFORMATION FOR IMPACTDATA
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How to fill out who data quality assessment

01
Gather relevant WHO data and documentation.
02
Identify the indicators that will be assessed for quality.
03
Review the data collection methods used to ensure they meet WHO standards.
04
Evaluate the completeness of the data by checking for missing values.
05
Assess the accuracy of the data by comparing it against reliable sources.
06
Analyze the timeliness of the data to determine if it is current.
07
Ensure that the data is consistent across various datasets.
08
Document any findings or issues identified during the assessment.
09
Provide recommendations for improving data quality based on the assessment results.

Who needs who data quality assessment?

01
Public health officials who require reliable data for decision-making.
02
Researchers studying public health trends and outcomes.
03
Government agencies assessing health programs and policies.
04
Non-governmental organizations (NGOs) working in health sectors.
05
International organizations collaborating with WHO on health initiatives.
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WHO Data Quality Assessment is a systematic process used to evaluate the quality of data collected by the World Health Organization, ensuring that it is accurate, reliable, and suitable for decision-making.
Entities or organizations that collect health-related data under the guidelines of the World Health Organization are required to file WHO Data Quality Assessment.
To fill out the WHO Data Quality Assessment, one must follow the prescribed guidelines and templates provided by WHO, ensuring all relevant information is accurately reported and validated.
The purpose of WHO Data Quality Assessment is to ensure that health data is of high quality, which is essential for effective public health decision-making and to improve health outcomes globally.
The information that must be reported includes data sources, methodology, quality indicators, and any challenges faced during data collection and processing.
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