Previously, I discussed life science subject screening and how robotic process automation can assist. This next installment of the series dives into robotic process automation (RPA) fixing mistakes in clinical data entry.
Dirty clinical data is one of the most costly aspects of clinical trials. Not only does the review and clean-up slow down time to market and increase development costs, dirty data can stall or even kill a new drug application (NDA).
Consistently receiving dirty data from sites can seriously damage a sponsor’s or CRO’s relationship with a site/ investigator, and can lead to a site no longer working with a sponsor or CRO, even though that site might have the ideal patient population, location, equipment, and/or expertise that the sponsor or CRO needs.
This tension can be reduced by leveraging RPA technology and the Internet of Things (IoT) in clinical data collection.
Companies are exploring IoT, dabbling in it, and even diving in head-first. Read on.
Scenario 1
After successfully enrolling in a clinical study, a subject is issued a wrist tracker and a digital skin patch, and is given login credentials for a proprietary app to download on his mobile phone and tablet.
Over the course of the study, his wrist tracker and skin patch continuously collect and transmit data to his managing site and your EDC system. Additionally, the mobile app pushes him reminders to take his medication.
One day, the investigator receives a notification that the subject’s blood pressure is running high, 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.
One of the most beneficial (and attainable) solutions that makes use of IoT technology is for subject diaries, including reminding subjects to complete their entries, facilitating their entries in interactive and engaging ways, and automatically mining their entries for potential adverse events.
Scenario 2
Once successfully enrolled in your study, a clinical subject is issued login credentials for a proprietary mobile app he downloads onto her phone. Over the course of his participation in your study, she receives push notifications to remind her to log periodic diary entries. In the app, she follows interactive prompts that ensure he’s submitting her diary entries completely and on time. The app even rewards her for doing so.
Her diary entries are automatically transmitted to your EDC system and other clinical systems. Additionally, the app makes use of the other functions of her phone to submit environmental data, such as exercise and sleep data. All the while, third-party contextual analysis software is monitoring her 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 she should be asked to come in for a visit. The investigator clicks “accept,” and the subject receives a notification on her phone, along with the option to schedule his appointment online. He completes the scheduling process and makes his way into the site for her appointment.
The third-party contextual analysis software is an especially brilliant addition to the use of RPA technology in clinical data collection. It goes above and beyond just ensuring accurate and timely data collection, and aids sites, sponsors, and CROs in the identification of potential adverse events in a way that only machine learning technologies can.
To learn more about specific clinical trial challenges concerning the pharma-investigator relationship and potential solutions using robotic process automation, you can download the guide here, or fill out the form below.
