Imagine with me:
A potential clinical subject comes into a site for her screening visit. When she checks in, she is issued an iPad for use during the visit. On the iPad, she finds an interactive app that leads her through the screening process, step by step. It moves her through questions dynamically, using a decision-tree format that adjusts based on her answers.
This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
When it comes to screening tests, the app moves her through a circuit of digital devices, each designed to perform different tests – a digital scale, a digital blood pressure machine, a digital toilet. Her iPad even contains a second app that makes use of the camera to visually analyze spots on her skin.
When the potential subject has completed all of the questions and tests, she submits her results and returns the iPad to the front desk. All of her data is automatically transmitted to the site personnel and your EDC system. The data is then automatically analyzed and issued a tentative status of “pass,” which triggers a notification to the site investigator.
Later that day, when the investigator has a moment, he reviews the potential subject’s results and approves her participation in the study. As soon as her status changes to approved, she receives a welcome notification and a request to complete the informed consent process.
The process we just imagined makes use of modern business process management (BPM) technology. Want to see it in action? Send us an email and we’ll show you.