OBJECTIVES: To describe the methodology for the development of data quality metrics in multi-institutional databases, deriving a cumulative data quality score [Aggregate Data Quality score (ADQ)]. The ESTS database was used to create and apply the metrics. The Units contributing to the ESTS database were ranked for the quality of data uploaded using the ADQ. METHODS: We analysed data obtained from 96 Units contributing with at least 100 major lung resections ( January 2007 to December 2014). The Units were anonymized assigning a casual numeric code. The following metrics were developed for measuring the data quality of each Unit: (i) record Completeness (COM); rate of present variables on 16 expected variables for all the records uploaded [1 - ('null values'/total expected values for the Unit) × 100, the concept of 'null value' was defined for each variable]; (ii) record Reliability (REL); rate of consistent checks on 9 checks tested for all the records uploaded [1 - (valid controls/total possible controls for the Unit) × 100, specific reliability control queries were defined]. These two metrics were rescaled using the mean and standard deviation of the entire dataset and summed, obtaining: (iii) ADQ score: [COM rescaled + REL rescaled]; it measures the cumulative data quality of a given dataset. The ADQ was used to rank the contributors. RESULTS: The COM of ESTS database contributors varied from 98.6 to 43% and the REL from 100 to 69%. Combining the rescaled metrics, the obtained ADQ ranged between 2.67 (highest data quality) and -7.85 (lowest data quality). Comparing the rating using just the COM value to the one obtained using the ADQ, 93% of Units changed their position. The major change was the drop of 66 positions considering the ADQ list. CONCLUSIONS: We described a reproducible method for data quality assessment in clinical multi-institutional databases. The ADQ is a unique indicator able to describe data quality and to compare it among centres. It has the potential of objectively guiding projects of data quality management and improvement.
- Data quality
- Database management systems
- Quality indicators
ASJC Scopus subject areas
- Cardiology and Cardiovascular Medicine
- Pulmonary and Respiratory Medicine