How to Evaluate and Improve the Quality and Credibility of an Outcomes Database

OBJECTIVES: To assess the quality and completeness of a database of clinical outcomes after cardiac surgery and to determine whether a process of validation, monitoring, and feedback could improve the quality of the database. DESIGN: Stratified sampling of retrospective data followed by prospective re-sampling of database after intervention of monitoring, validation, and feedback. SETTING: Ten tertiary care cardiac surgery centres in the United Kingdom. INTERVENTION: Validation of data derived from a stratified sample of case notes (recording of deaths cross checked with mortuary records), monitoring of completeness and accuracy of data entry, feedback to local data managers and lead surgeons. MAIN OUTCOME MEASURES: Average percentage missing data, average k coefficient, and reliability score by centre for 17 variables required for assignment of risk scores. Actual minus risk adjusted mortality in each centre. RESULTS: The database was incomplete, with a mean (SE) of 24.96% (0.09%) of essential data elements missing, whereas only 1.18% (0.06%) were missing in the patient records (P < 0.0001). Intervention was associated with (a) significantly less missing data (9.33% (0.08%) P < 0.0001); (b) marginal improvement in reliability of data and mean (SE) overall centre reliability score (0.53 (0.15) v 0.44 (0.17)); and (c) improved accuracy of assigned Parsonnet risk scores (k 0.84 v 0.70). Mortality scores (actual minus risk adjusted mortality) for all participating centres fell within two standard deviations of the mean score. CONCLUSION: A short period of independent validation, monitoring, and feedback improved the quality of an outcomes database and improved the process of risk adjustment, but with substantial room for further improvement. Wider application of this approach should increase the credibility of similar databases before their public release.