@article{ref1,
title="Improving the quality of data entry in a low-budget head injury database",
journal="Acta neurochirurgica",
year="2007",
author="Beretta, L. and Aldrovandi, V. and Grandi, E. and Citerio, G. and Stocchetti, N.",
volume="149",
number="9",
pages="903-909",
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.<p /> <p>Language: en</p>",
language="en",
issn="0001-6268",
doi="10.1007/s00701-007-1257-3",
url="http://dx.doi.org/10.1007/s00701-007-1257-3"
}