Application of Interactive Code Blocks in teaching Clinical Data Validation using R

Date: Saturday, 26 September 2026 | Time: 1435 - 1605 | Venue: Whitespace, Academia (SGH Campus)

Track Type: Workshop

Speaker(s):

Overview:

High-quality clinical data is essential for accurate decision-making; timely data validation helps to identify errors or discrepancies in clinical data promptly. This ensures that accurate patient information is available to healthcare professionals, minimising the risk of medical errors and enhancing patient safety.

This workshop is for healthcare professionals who wish to upgrade their data management skills further by using R to conduct efficient and accurate data validation workflows. In addition, participants will be exposed to interactive code blocks which is an interactive and effective educational tool for teaching programming.

Participants will receive hands-on experience by working with interactive R code blocks online with small exercises on clinical examples. No installation of R, RStudio and relevant R packages is required as interactive teaching materials are available online.

Clinical data validation checks include (but not limited to):

• Range Checks: Verify that numerical data, such as age and other quantitative measurements, fall within predefined acceptable ranges.
• Completeness Checks: Ensure that all required data fields are completed.
• Logical Checks: Verify the logical relationships between different data points.

Learning Outcome(s):

By the end of the workshop, participants will have some hands-on experience in using interactive code blocks as a means of showing how R and its corresponding R package “pointblank” are able to:

• Initiate a data validation workflow to create an interactive report to identify clinical data that fails a given data validation test.
• Separate data that passed the validation test for further analysis and data that failed the validation test for verification.
• Validate if the specified column contains positive values.
• Validate if the specified column has no missing rows.
• Validate if the specified column has unique values in every row.
• Validate if the specified column has a specific list of categorical values.
• Validate if the specified column is within a certain range.
• Create exceptions to ensure specific rows are exempted in a given data validation step.

Target Audience:

As the workshop will be conducted using the R programming language, basic experience of R is ideal but not required. Participants instead should have some familiarity with messy tabular clinical data to fully understand the course content.

Interactive course material is also available online. Hence, it is sufficient if the participant has Internet access on their laptop or notepad in order to type and run the R codes by clicking a few buttons.

Typing of R codes is needed when doing the exercise. Hints and solutions are provided if participants need help. No installation of R, RStudio and relevant R packages is required to participate.