Data Stewardship

During this course, you will develop skills regarding Communication, Scientific reporting, Research, and Implementation. By the end of the course, you should be able to:

• Discuss the issues of retrospective and prospective data verification with both domain experts and data experts.
• Explain data research approaches to the data scientist.
• Formulate questions and issues on data with data scientists.
• Use basic principles of scientific medical background when reporting about data science results.
• Use basic principles of the clinical decision-making process at the individual patient level when reporting about personalized medicine.
• Use basic principles of the limitations of the model with respect to medical, clinical, technical, and model exceptions when reporting about data science models.
• Make a technical file for CE marking that explains basic principles of reporting.
• Design data validation rules compliant to GDPR to limit data entry for analytics.
• Retrospectively check data usefulness for research by verifying parameter values at both intra and inter-parameter levels dependent on a specific domain.
• Translates care data to high quality structured data for research.
• Recognize errors in data and deletes, alters, or excludes data from study.
• Validate data using semi-automated approaches fit for large data sets.
• Explain to the data scientist that sensitivity analysis is needed when the model cannot be validated manually.
• Search for understanding of putative mechanisms of found correlations in clinical decision-making tools, within domain knowledge, personal interactions, and literature search.
• Develop Data privacy impact assessment (DPIA) and data management plan (DMP).

• Introduction to Data Stewardship
• Data handling
• Data Stewardship in Clinical Decision Support
• Data validation
• Data sharing
• Data management plan

To enroll in this course, you need:

• MSc degree or similar
• Access to Microsoft Excel
• Sufficient proficiency in English reading and writing