Imagine with me:
Once successfully enrolled in your study, a clinical subject is issued login credentials for a proprietary mobile app he downloads onto his phone. Over the course of his participation in your study, he receives push notifications to remind him to log periodic diary entries. In the app, he follows interactive prompts that ensure he’s submitting his diary entries completely and on time. The app even rewards him for doing so.
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.
His diary entries are automatically transmitted to your EDC system and other clinical systems. Additionally, the app makes use of the other functions of his phone to submit environmental data, such as exercise and sleep data. All the while, third party contextual analysis software is monitoring his diary entries.
One day, the investigator receives a notification that the content of one of the subject’s diary entries seems like a potential adverse event, along with a prompt to ask whether he should be asked to come in for a visit. The investigator clicks “accept” and the subject receives a notification on his phone, along with the option to schedule his appointment online. He completes the scheduling process and makes his way into the site for his appointment.
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.