Harnessing data for healthcare advancement

This course introduces key principles of data-driven decision-making in healthcare data science, focusing on managing and leveraging health data effectively across 3 modules. You'll begin in Module 1 by exploring what it means to be data-driven, ensuring data consistency, accessibility, and reliability while addressing challenges like missing information. You’ll also learn the essentials of metadata management and ethical considerations for handling health data. Module 2 will teach you how to develop a HDS project by understanding healthcare systems, teamwork, and project management. You'll explore how data flows within healthcare, the roles involved in health data science, and how collaboration enhances patient outcomes. Essential tools for navigating health data projects will also be introduced. In Module 3, you’ll focus on collaboration and communication in health data science. You’ll examine factors that contribute to project success, including effective documentation and data visualization techniques. By the end of this course, you'll be equipped with the knowledge and skills to manage health data projects efficiently and communicate findings to diverse stakeholders. 3b:T5c

• describe what data-driven decision-making is.
• explain data integrity, FAIR principles and five-safes rule
• demonstrate an understanding of data quality and metadata.
• analyse data access processes and principles.
• describe the healthcare system
• outline the different health data science project roles.
• distinguish between the different types of data flows.
• show how patient pathways and collaborative pathways operate.
• compare different effective tools in a health data science project.
• define a common language for a health data science collaborations
• discuss the best study protocols for a common collaborative language.
• illustrate how to effectively document, evaluate and communicate results.
• analyse data visualisation in health data reports.