The most serious data quality issues appear to be: inaccuracies in crash location and time, difficulties in data linkage (e.g. with traffic data) due to inconsistencies in databases, severity misclassification, inaccuracies and incompleteness of involved users' demographics and inaccurate identification of crash contributory factors.
Working from an existing database, several disease categories and severities contained small sample sizes, attenuating the statistical power to find significant differences.
We analysed needs for database and severity scoring.
We extracted from our routine database: demographic characteristics, severity scores, monitoring data, early mobilization activities, reasons for not providing such therapy, as well as any adverse events.
For each patient, the following data were retrospectively obtained from medical records and institutional databases: demographic and severity of illness parameters, the amount and costs of laboratory tests, daily blood volume drawn, number of red cells transfusions and ICU and in-hospital outcomes.
The Monitoring Trends in Burn Severity (MTBS) database provided the spatial distribution, severity, and frequency of fires (i.e. patch size, location and date) for the years 1984 2014 [36].
A database where demographic characteristics, severity scores (ISS, APACHE II), location and etiology of trauma, surgery requirement and survival were recorded.
Comparisons between our study and these registry studies are also limited by the lack of clinical data in our database (e.g., disease severity).
We also explored, after clinician consultations, other variables present in the database such as bed severity allocation which is a variable based on the bed need of the patient.
