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The future of automation and digital transformation in late phase research

Digitalisation's ootential to shorten timelines and reduce Costs

It’s been nearly two decades since Electronic Data Capture (EDC) technology was introduced into clinical trials – one of the first significant steps in automating the trial process. Now, with the availability of robotic process automation (RPA), cognitive analytics, and artificial intelligence (AI), it’s time to press on with further digitalisation of clinical development. Gartner defines digitalisation as “the use of digital technologies to change a business model and provide new revenue and value-producing opportunities.[1]
Even though clinical development is, by its very nature, a “people business,” there is ample opportunity to use these new technologies to perform manual tasks, reducing the opportunity for error, freeing staff for higher-value tasks and speeding processes. Ultimately, automation can help us dramatically shorten development timelines and realise significant cost savings. 

Many sponsor companies and contract research organisations (CROs) are already working to automate steps in Phase II and Phase III research programs, but less emphasis has been placed on ways to automate aspects of late phase research. These studies are also ripe for digitalisation, although not always in the same ways. 

Prime targets for automation

From our vantage point, there are three areas within late phase research that have the greatest untapped potential to benefit from digitalisation: site activation activities, study management responsibilities, and patient engagement efforts.

Late Phase Fit-for-purpose Site Activation 

Many of the requisite steps in preparing to activate sites are currently one of the biggest bottlenecks in studies. These include but not limited to negotiating site contracts and assembling all of the necessary documents, ensuring that they are complete and submitted for approval. When examined closely, these activities follow a very defined – albeit complicated – path that can be easily managed in an automated workflow within a digital platform. Although site activation in late phase research is naturally faster than that of early phase research, automation provides a framework to cut down on cycle times even further.

Late Phase Study Management

Automation, of course, supports all aspects of site engagement and management, particularly remote and risk-based monitoring, a practice that is particularly well suited to late phase studies. But, there’s also the potential to use automation to further enhance and streamline site engagement at the individual site level such as through tailored call campaigns, query management, and remote and on-site close-out activities. Another area worthy of innovation via technology is reducing the burden of on-site staff so that they can dedicate more time to patient care. This is especially important in late phase research that involves a larger percentage of community physicians whose focus is on providing the standard of care to their patients.

Late Phase Patient Engagement

Much progress has already been made in this area, with digital solutions for recruiting, consenting, educating, retaining, and supporting patients in clinical research.  With Google’s recent preview of a digital assistant for mobile devices with a human-like voice capable of conversing back and forth to accomplish specific tasks, such as making an appointment, the app’s capabilities will be expanded, and it boggles the mind to imagine what might be possible. For instance, and as applicable, engaging a digital assistant with patients to remind them of their upcoming appointment, manage their travel schedules or even provide them with standard medical information details about approved drugs or devices.

The value of a centralised approach

At ICON, we’re finding that we can adopt a much more comprehensive and strategic approach to automation (and provide a seamless experience for sites and patients in late phase studies) by tackling it from a centralised function. We’ve created a real world evidence hub infrastructure with service hubs in every region of the world. Each provides end-to end site and direct-to-patient services (separated by a firewall), so that sites and patients alike have but one point of contact with us for the duration of the study. This is important when dealing with healthcare providers who are not accustomed to the rigors of trial participation. Similarly, patient engagement services are integrated, with the goal of simplifying the patient’s journey while being part of clinical research. This hub structure allows us to develop digital solutions to improve the entire study process, end to end, using lean and innovative design. 

 We believe that twenty years into automating clinical trial processes, the industry is on the cusp of another transformation that promises to have a profound effect on study timelines and costs. The benefit of the next wave of automation, made possible through RPA, cognitive analytics, and AI, will, of course, be magnified when these innovations are adopted across studies and research programs.

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