4_DataQualityAssessmentMethods_D4Ifinal.pptx

It talks about the data quality assessment methods in detail with examples and illustrations

1. Data Quality Assessment Methods Name, Data for Impact Meeting or event Date

2. Be knowledgeable about some of the different data quality assessment tools that help identify data issues and measure the quality of data. Identify the appropriate data quality tool(s) to apply in different contexts. Understand how best to identify and select indicators for data quality assessment. Understand how to define, calculate, and interpret data quality metrics. Objectives:

3. SS to EMU: Desk review examines population data quality across four dimensions: Completeness Internal consistency External comparisons External consistency RDQA: Facility/district data quality assessment examines routine service delivery data: Accuracy between data sources and reports Data completeness in data sources and available reporting tools Data reporting timeliness against defined deadlines Data management Existing Data Quality Assessment Tools

4. Combines important features and processes from the RDQA and the SS to EMU tools to solve the practical challenges faced by FP programs. SS to EMU at national and subnational levels: Identifies indicators or data elements with quality problems. Informs where data quality problems are located. Determines whether problems are limited to specific regions and/or certain FP methods. RDQA at subnational and health facility levels: Assesses the strengths and weaknesses of the underlying data management and reporting system. Verifies the quality of reported data against data recorded in the primary source documents. What is the integrated approach for FP data quality assessment?

5. What challenges will be addressed by the integrated approach? Top-down approach using the SS to EMU tool to improve targeting and/or prioritization of when and where a RDQA may be most useful. Conduct RDQA(s) at a limited number of facilities (selected using purposive sampling) to understand the drivers of the data quality issues identified through the top-down approach. Integrate feedback on these drivers to national-level stakeholders through national-level review meetings. Improve national-level routine review systems/FP HMIS dashboards by identifying elements of the RDQA, such as data verification and cross-checks that can be integrated into: Ongoing routine supervision visits Data monitoring meetings District/region periodic coordination meetings

6. Understandability/interpretation by users— indicator trends and data quality can be: Processed Explained Actionability by users—use information about data quality to: Implement actionable steps that will either maintain data quality, or Improve data quality What will the integrated approach improve?

7. HMIS data inputs: Method Year Source of data (commodities, visits, users) Pre-populated data demographics: Contraceptive prevalence Method and source mix to improve benchmarking and to estimate the total volume of FP services in years between surveys Data quality review including: Internal validation that reviews each type of service statistic to identify method specific and overall trends, and internal consistency across different types of service statistics data: outliers anomalies External validation that includes benchmarking with surveys and modeled estimates Decision making about the findings on quality for each type of service statistic. SS to EMU components

8. Assessment of the quality of selected indicator data: Data verifications Assessment of the strengths and weaknesses of the overall data management and reporting system: System assessment RDQA Components