Part of Data Quality Assurance Framework for Providers – Part 2
Measures
Internal metrics
Objective
To ensure that internal data quality metrics are in place to provide assurance and confidence in data critical to delivering care along the entire patient pathway, supporting clinical decision making and reporting against key performance frameworks.
Benefit
Internal data quality metrics ensure that acceptable quality thresholds are explicit, in place and visible to all. They provide assurance of adherence to national information standards and internal quality standards thereby offering confidence to data users across both primary and secondary use settings. Internal metrics also allows for better identification of data quality issues, planning, prioritisation of actions and tracking of improvements.
Best Practice
Having a range of internal metrics with quantifiable thresholds that measures validity, completeness, timeliness and auditability of data is essential to achieving and maintaining consistent levels of data quality. Understanding the relationship between reported data items, associated internal metrics and data uses is important for encouraging wider awareness of data quality. It is crucial that all staff in the organisation understand the internal metrics in order to both participate in validating data against these metrics and appreciate how collected data drives health care uses. Consistency in meeting thresholds can be enabled by internal data quality KPIs and service level agreements managed by regular overarching data quality statements to senior management.
Case study
Rotherham NHS Foundation Trust have created an assurance process for all Trust Integrated Performance Report (IPR) Indicators based on 6 Key dimensions of data quality; granularity, timeliness, completeness, signoff, systems and audit. A matrix has been developed based on these areas of focus with points for consideration when evaluating an individual indicator. Each indicator is assessed, scored and rated against the 6 dimensions and then combined to create a Data Quality Assurance Mark (Kite Mark) that is placed in the IPR Report as a visual representation of how robust the indicator is assessed to be. This is supported by a report based on the findings of the assessment and recommendations to improve the data quality supporting an indicator.
Last edited: 6 March 2025 2:05 pm