The aim of this study was to evaluate the impact of a program of training, education and awareness on the accuracy of the data collected from hospital discharge abstracts. Four random samples of hospital discharge abstracts relating to four different periods were studied. The evaluation of the impact of systematic training and education activities was performed by checking the quality of abstracting information from the medical records. The analysis was carried out at the Istituto Dermopatico dell'Immacolata, a research hospital (335 beds) in Rome, Italy, which specializes in dermatology, plastic and vascular surgery. Error rates in discharge abstracts were subdivided into six categories: selection of the wrong principal diagnosis (type A); low specificity of the principal diagnosis (type B); incomplete reporting of secondary diagnoses (type C); selection of the wrong principal procedure (type D); low specificity of the principal procedure (type E); incomplete reporting of procedures (type F). A specific rate for errors modifying classification in diagnosis related groups (DRG) was then estimated and the effect of re-abstracting on the case-mix index evaluated. Error types A, B, C, E and F dropped from 8.5% to 2%, 15.8 to 4.9, 31.8 to 13.1,4.1 to 0.3 and 22 to 2.6%, respectively. Error type D was 0.7 both in the first (the baseline) and fourth periods of analysis. All differences in error types were statistically significant. In 1999 8.3% of cases were assigned to a different DRG after re-abstracting as compared with 24.3% in the third quarter of 1994, 23.8% in the first quarter of 1995 and 5.5% in September-October 1997. Continuous training and feedback of information to departments have shown to be successful in improving the quality of abstracting information at patient level from the medical record. These positive results were facilitated by the introduction of a prospective payment system to finance inpatient hospital activity. The effort to increase administrative data quality at hospital level facilitates the use of those data sets for internal quality management activities.