Table 2.

List of tasks performed with our DQA tool.

Missing data	The identification of the core variables that constitute the minimum data set (MDS) of each record
Create clinical impact variables	Creation of clinical variables like APACHE-II, SOFA, PaFi, BMI, CURB65, compliance, driving pressure, ventilation ratio, etc.
Unstructured data	Rule programming to transform unstructured text to standardized encoded data. Use of specific lists or international classification systems such as ICD-10 for diseases or ATC for drugs
Outliers	Development of rules to identify and validate extreme values
Data inconsistencies	Development of rules to identify and manage inconsistencies related to dates, events, duration of treatments, etc.
Database homogenization	Development of rules to automatize the homogenization of the different laboratory units of the analytical variables
Tracking report	Weekly sample tracking report to monitor the data inclusion
Dashboard	To monitor, in real time, the different existing data inconsistencies
Combined filter	To identify queries associated with the sub-studies (patient profile) and/or desired variables associated with a working hypothesis
Dashboard for queries management	Mass sending of queries, visualization of data entry responses in RedCap, internal resolution of queries, and automatic closing of inconsistencies when the system detects that they have disappeared
SPSS database	Dump of data in SPSS format, weekly, after cleaning and debugging data