Scientific Concept of the National Cohort (status ... - Nationale Kohorte

A.5
A.5 Methods for quality assurance and quality control
A.5.9.2 quality control of data management
Once the National Cohort IT system has been installed in its final productive environment,
the quality of the data items and the data flow will be continuously monitored and controlled.
As the IT system is installed in different geographic locations, we present four individual
perspectives to approach the main quality control issues: a general point of view, a study
center point of view, an integration center point of view, and a competence unit point of view.
Quality control issues from a general point of view
� Standardization: each organizational unit works with the same version of the software
and delivers data in the same way.
� Historiography: the software will allow data changes at any point. These changes will
be documented. It should be possible to track every change in a history table and
restore each value that each stored parameter took at different times. Recovery procedures
to obtain previous versions of the data will be tested before the software is
used.
� Data safety: new security breaches are discovered for older software technologies
over time; therefore, it is necessary to regularly monitor the relevant sources of information
about security leaks, and to adapt the software in a timely manner in order to
eliminate potential vulnerabilities.
� Specification of the data flow and responsibilities to solve problems: users with dedicated
roles (e.g., competence units) monitor the data flow during recruitment to detect
possible interobserver variability within or across study centers.
� Flagging problems: each part of the system will be allowed to flag problems to other
parts. Other parts that work with the same data will be warned about these problems.
If necessary, flagged data will be banned from further processing until the flag has
been resolved.
� Data versioning: all data packages that are sent from one entity to another will be
versioned, including the name of the entity that sent the data, the exact date when the
data were sent, and an incremental versioning number.
Quality control issues at the level of the study center
� Plausibility checks during data entry: the data being entered will be checked for completeness
and plausibility. Violations of such conditions will be made evident to the
examiner; where appropriate, it will be impossible to submit such data.
� Check for completeness: at the end of the input phase, the system will check all data
and issue warnings if incomplete records are encountered. Whenever a value is missing,
an appropriate explanation will be available.
� Data flow control: Data from study centers will be transferred to the integration centers
immediately, or at least as soon as possible. Independently of the data transfer, the
study centers will be able to mark data records as complete and qualitatively sound
and ready for further processing or analysis.
Quality control issues at the level of the integration center
� Cross-center differences: these will be controlled on a regular basis by the data mangament
team to ensure that changes over time (e.g., turnover of study personnel) do
not impact the data.
� Unit testing and software updates: the quality of the IT system will be maintained
through software updates, including during the recruitment process. Such software
updates ensure that changes in real-life procedures to correct last-minute problems
are also reflected in the IT. For example, changes made to one component also affect
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