Background. To assess the efficacy of a centralised review of a voluntary low-budget head injury database with a retrospective analysis of data before and after a centralised review. Method. A computerised data collection (Neurolink) on traumatic brain injury cases admitted to three neuro-intensive care units in Milan (Italy): analysis of a three-year period (1999-2001). Data from 499 patients (epidemiology, type of lesion, clinical course, monitoring, treatment, complications and outcome). The audit involved a review of forms relating to patients enrolled in the three-year period, with the aim of improving the quality of data entry. Missing data in all empty fields were identified; evident errors and contradictory data were identified and corrected; missing and final data were analysed to test the efficacy of the review. Findings. The total post-review missing data rate was significantly lower than the paired pre-review missing data rate (p = 0.001). The same was confirmed for each of the 3 years (p = 0.001 for each year). The missing data rate significantly improved over the three-year period (p = 0.001). Data for the pre-hospitalisation period had the highest missing rates; data regarding the ICU stay showed the greatest improvement after the review. A total of 407 items (0.44%) were identified as errors. Conclusions. Data quality is fundamental to avoid information bias in database analysis. This study indicates that it is possible to generate a serious data collection without significant resources. Audit seems to be an important tool before the final data is used for scientific projects. © 2007 Springer-Verlag.
Beretta, L., Aldrovandi, V., Grandi, E., Citerio, G., Stocchetti, N. (2007). Improving the quality of data entry in a low-budget head injury database. ACTA NEUROCHIRURGICA, 149(9), 903-909 [10.1007/s00701-007-1257-3].
Improving the quality of data entry in a low-budget head injury database
CITERIO, GIUSEPPE
;
2007
Abstract
Background. To assess the efficacy of a centralised review of a voluntary low-budget head injury database with a retrospective analysis of data before and after a centralised review. Method. A computerised data collection (Neurolink) on traumatic brain injury cases admitted to three neuro-intensive care units in Milan (Italy): analysis of a three-year period (1999-2001). Data from 499 patients (epidemiology, type of lesion, clinical course, monitoring, treatment, complications and outcome). The audit involved a review of forms relating to patients enrolled in the three-year period, with the aim of improving the quality of data entry. Missing data in all empty fields were identified; evident errors and contradictory data were identified and corrected; missing and final data were analysed to test the efficacy of the review. Findings. The total post-review missing data rate was significantly lower than the paired pre-review missing data rate (p = 0.001). The same was confirmed for each of the 3 years (p = 0.001 for each year). The missing data rate significantly improved over the three-year period (p = 0.001). Data for the pre-hospitalisation period had the highest missing rates; data regarding the ICU stay showed the greatest improvement after the review. A total of 407 items (0.44%) were identified as errors. Conclusions. Data quality is fundamental to avoid information bias in database analysis. This study indicates that it is possible to generate a serious data collection without significant resources. Audit seems to be an important tool before the final data is used for scientific projects. © 2007 Springer-Verlag.File | Dimensione | Formato | |
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