Collecting and using data are core activities in a well-functioning Quality Rating and Improvement System (QRIS). Yet, data used in a QRIS are frequently housed in different systems, using different data management techniques. Ensuring a high level of QRIS data quality involves implementing a number of best practices drawn from established practices used in other fields. The purpose of this brief is to describe the specific strategies QRIS data stakeholders can use to improve upon the collection, management, and dissemination of QRIS data. The audience for this brief includes QRIS program administrators, technical assistance providers, data managers, and researchers. This brief is structured around the five stages of the Data Lifecycle: planning, collection, processing, management and distribution. Best practices are recommended for each stage of the Lifecycle. (author abstract)
Best practices in ensuring data quality in quality rating and improvement systems (QRIS)
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