Over the past six years, I have worked on developing and acceptance testing of the applications for conducting and supporting clinical trials. Applications of various sizes and complexity, big data, a huge number of visualizations and views, data warehousing, ETL, etc. The products are used by doctors, clinical trials management and people who are involved in the control and monitoring of research.
For the applications that have or can have a direct impact on the life and health of patients, a formal acceptance testing process is required. Acceptance test results along with the rest of the documentation package are submitted for audit to the FDA (Food and Drug Administration, USA). The FDA authorizes the use of the application as a tool for monitoring and conducting clinical trials. In total, my team has developed, tested and sent to the production more than thirty applications. In this article, I will briefly talk about acceptance testing and improvement of tools used for it.
Note: I do not pretend to be the ultimate truth and completely understand that most of what I write about is a Captain Obvious monologue. But I hope that the described can be useful to both the entry level and the teams that encounter this in everyday work, or at least it may make happy those who have simpler processes.