Bias and key issues in healthcare data (11:19)

This video lesson will give you an understanding of:

• The risks of bias in healthcare data science and how it can distort findings and lead to unfair outcomes.

• Different types of bias in machine learning (historical, representation, data leakage) and traditional statistics (confounding, sampling).

• Practical strategies to mitigate bias, such as careful dataset selection and transparent reporting using TRIPOD guidelines.

By the end of this video lesson, you will:

• Understand how biased data can reinforce existing healthcare inequalities.

• Be able to identify common pitfalls in study design and model training.

• Know how to apply fairness audits and transparent reporting to ensure reliable results.

For a transcript of this video lesson, click ‘download handout’ above. Once you have completed the bite-sized video, please ensure you fill out our feedback survey which can be found at the end of the course page.