OUTIL D'ÉVALUATION DE LA QUALITE DES DONNEES DE ROUTINE (RDQA)

QA refers to the process of measuring performance against established standards at a given time, while QI involves evidence-based activities designed to continuously improve performance, test changes in services, and measure their effects. QI is a specific component of continuous quality improvement (CQI), an ongoing process that engages implementation teams in identifying obstacles and factors that facilitate the delivery of quality services. Data quality assessment (DQA) is a quality assurance activity that evaluates one or more indicators and dimensions of data quality, including validity, accuracy/precision, thoroughness/completeness, confidentiality, integrity, reliability, and timeliness. The primary goal of DQAs is to ensure that high-quality data are communicated to stakeholders, including the Ministry of Health, USAID, and PEPFAR. This involves identifying system problems that affect data quality, validating data aggregation and reporting processes, correcting divergences between counted figures and reported figures, supporting staff capacity building, and identifying problems related to program quality and developing remediation plans. Several challenges can affect data quality, including system-related problems such as inadequate resources for collecting and analyzing data, poorly defined roles and responsibilities for data entry, aggregation, validation, and reporting, and dependence on other entities for data reporting. Other factors that can impact data quality include poor understanding of indicator definitions, data compilation processes, and report preparation, lack of interest or motivation during data entry and quality control, and calculation errors when consolidating data from different sources. High-quality data are essential for HIV programs, which are focused on results and managed based on data or evidence. Accurate data are necessary for monitoring and evaluating progress toward achieving the 95-95-95 targets, precisely evaluating partner performance, accountability and good governance, and planning and decision making. Data are also used to evaluate compliance with quality standards and technical guidelines. Poor data quality can have significant consequences, including inaccurate reporting of new HIV-positive cases, which can lead to unnecessary medication shipments and staff hiring. Underreporting of clients can result in compliance issues, reduced inputs, and insufficient staffing. Therefore, it is crucial to have a data QA/QI process in place to ensure the accuracy and reliability of data. The primary goal of this training is to enable managers to have high-quality data to document, monitor, and manage HIV/AIDS programs. Participants will learn about the importance of quality data, the measures that programs can take to improve data quality, and the various tools available to monitor and improve data quality. By the end of this training, participants will be equipped with the knowledge and skills necessary to ensure the accuracy and reliability of data in their programs.