## 2 Materials and Methods  
The Direcht modelling tool [21] utilises various modelling paradigms from the ISO 13606-1 Reference Model [22; 23], such as 'Element', 'Cluster', 'Entry', and 'Sections'. For instance, 'BodyMeasurement' is a 'Cluster' which encompasses different data dictionary items such as 'BMI' and 'height' (represented as 'Elements'). These 'Clusters' and 'Elements' can be brought together under an 'Entry' that represents a well-defined, communicable piece of information. One or more of such 'Entries' can be grouped together under headings, represented as a 'Section' within the tool.  
Figure 1 shows the procedure used for the modelling of data dictionary items in Excel, a snapshot of which is shown in Figure 2. BodyMeasurement (Vtial Signs) was used for the first pilot study using Direcht and reproduced at the start of the second pilot. The first step was the creation of an account in Direcht. Modelling of the data dictionary items was started under the project CCPDD-pilot 2 within the 'Create Clinical Model' section as shown in Figure 3 below. In the 'Entry' section, the page was completed, which included 'Entry Name' ('BodyMeasurement'), 'Specialization', 'Version Status', 'Conformance', 'Cardinality', 'Version Number', 'Entry Description', 'Mapping Type', 'Mapping URL', 'Mapping Description', and 'Elements'. The 'Conformance' and 'Cardinality' values were selected on the basis of the importance of the data dictionary item in the specific aspect of a clinical trial. Moreover, it is important to recognise that not all of the sections were compulsory to complete, and as such, compulsory fields were marked with a red asterisk. In the mapping section, the CDISC link to the data dictionary item being modelled was entered in the 'Mapping URL' section, the description of the entry was stated in the 'Mapping Description' section, and the 'Mapping Type' was labelled as 'CDISC'.  
Through the data modelling tool, one can model data dictionary items in a non-CDISC standard environment. Moreover, data content can be exported to the CDISC ODM format and imported to one or more clinical EDC tools. These features of the tool provide an avenue for data from the clinical trial environment to flow effectively to the point of healthcare where they are needed. It also provides a better structural and visualisation of data for use by those at the lower levels of the clinical trial ladder across EDCs. Furthermore, it helps to make the data dictionary contextually complete.  
In the 'Element' fields, as shown below in Figure 4, four 'Elements' were completed, namely, 'BMI', 'Height', 'TotalBodyLength', and 'Weight'. The 'Cardinality' and 'Conformance' of these 'Elements' were completed according to the importance of the measurements in specific clinical trials. Following this, the 'Entry' was saved.  
Figure 1: Schematic diagram showing the flow chart of the use of Direcht to model data dictionary items. HTML—hypertext markup language; ODM—operational data model.  
Figure 3: Image showing the ‘Create Entry’ screen in Direcht.  
Figure 2: Microsoft Excel worksheet showing the c4c data dictionary item for BodyMeasurement. CDASH—Clinical Data Acquisition Standards Harmonisation; SDTM—Study Data Tabulation Model; CDISC—Clinical Data Interchange Standards Consortium; CDASHIG—Clinical Data Acquisition Standards Harmonisation Implementation Guide; SDTMIG—Study Data Tabulation Model Implementation Guide.  
Afterwards, the 'Create Cluster' page was opened and, like the 'Entry' page, completed. The 'Clusters' were named 'BloodPressureMeasurement' and 'BodySurfaceAreaDetails'. 'Elements' completed included 'SystolicBloodPressure', 'DiastolicBloodPressure' and BodySurfaceArea'. Finally, the data dictionary items modelled in the 'Clusters' were then attached to the already modelled 'Entry' and saved.