Using AI to Accelerate Clinical Trials | Deloitte US has been saved
By Nick Lingler, managing director, and Siddharth Karia, principal, Deloitte Consulting, LLP
Artificial intelligence (AI)-enabled data collection and management can be a game changer for life sciences companies in the drug development process.
Once the stuff of science fiction, AI has made the leap to practical reality. Yet, to date, most life sciences companies have only scratched the surface of AI’s potential. One area that holds particular promise: digital data flow automation for clinical trials. With the power of AI, companies can rapidly digitize clinical-trial processes so they can complete studies faster. That means life-saving medicines and treatments can get to patients more quickly—and life sciences companies could gain a competitive edge. In fact, according to Deloitte’s life sciences digital innovation survey, 76% of respondents are currently investing in AI for clinical development.
Let’s take a closer look at how available and scalable AI technologies can speed and improve clinical trials—from both internal-process and patient-engagement perspectives.
Key data limitations
Despite the lightning speed at which COVID-19 vaccines were developed, research from the Deloitte Centre for Health Solutions suggests that it often takes 10 to 12 years to bring a new drug to market. The clinical-trial phase averages five to seven years. This timeline is due to the traditional flow of data across the clinical-trial life cycle, which can be a complicated maze of manual effort, rework, and inefficiency. As one life sciences executive summed up, “We still use the same processes that we used over 50 years ago. It feels like it’s 1972, not 2022.”
Consider these key data-related limitations of the traditional clinical trials process:
Life sciences companies can also face numerous patient-related challenges that can limit their ability to collect trial data in the first place:
Collecting data—and making it flow
Thankfully, AI can help CIOs overcome these challenges. AI technologies can be used to create structured, standardized, and digital data elements from a range of inputs and sources. For example, CIOs can implement new AI-powered tools to automate data management across the trial lifecycle. These tools intelligently interpret data elements, feed downstream systems, and auto-populate required reports and analyses. These tools can leverage existing systems to seamlessly integrate the data flow—providing a single, collaborative touch point for all interactions during a clinical trial. They can even use AI to generate insights from past and current trials to inform and improve future trials. Additional AI opportunities and use cases include:
The AI advantage
With AI, life sciences and health care organizations can likely gain significant benefits—both in collecting trial data and promoting digital data flow:
With AI, life sciences organizations can shift from large teams working across dozens of data systems to a single AI-enabled, standards- and metadata-driven backbone that requires minimal user input. By reducing the time and effort required for clinical trials, AI-enabled data collection and management can accelerate the drug development process and help companies get new treatments to market more quickly. That puts power in the CIO’s hands to get patients faster access to safe medicines that can change—and even save—their lives.
Acknowledgements: Amy Cheung and Suman Kumar