Defines software automation and distinguishes it from manual and semi-automated work. Explains why automation matters for accuracy, reproducibility, and time saved in clinical and laboratory settings, and where its limits lie.
Topic 1 — Foundations of Software Automation in Healthcare
An introduction to what software automation means in a healthcare and research context, which processes are worth automating, and the tools you will use throughout the course.
Estimated workload: 4 hours.
0/6
Topic 2 — Healthcare Data and Interoperability (copy)
Covers the kinds of data found in clinical and laboratory work, the standards that let systems exchange it (HL7 and FHIR), how to check data quality, and how to build a first automated data pipeline.
Estimated workload: 6 hours.
0/7
Topic 3 — Process and Robotic Process Automation (RPA)
How to model a process, choose an RPA approach, script repetitive tasks, and schedule them to run on their own.
Estimated workload: 6 hours.
0/7
Topic 4 — Testing, CI/CD and DevOps for Health Software
Practices that keep health software reliable as it changes: automated testing, version control, continuous integration and delivery, and basic monitoring.
Estimated workload: 6 hours.
0/7
Topic 5 — AI-based Automation
How machine learning fits into an automation workflow, how to add a model to a pipeline, how to check that it works, and how to use it responsibly.
Estimated workload: 6 hours.
0/7
Topic 6 — Security, Compliance and Capstone Project
Protecting health data, automating security checks, keeping work reproducible, and a final project that brings the course together.
Estimated workload: 8 hours.
0/7