Using AI to accelerate clinical trials

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:

• Fragmented data and disconnected systems: Inputs for trial artifacts are often scattered across dozens of systems and formats.
• Extensive manual effort: Artifact creation requires manual data transcription from documents and systems.
• Rework and repetition: Although trials typically reuse data components, the same work is often repeated across trials. In the words of one executive, “Databases are still being built from the ground up for most trials. We end up building the same database 400 times.”
• Challenges in enabling innovative trial models: Complexities and limitations related to integrating data from new sources can create challenges with virtual trial designs.

Life sciences companies can also face numerous patient-related challenges that can limit their ability to collect trial data in the first place:

• Patient recruitment and enrollment: The process of traveling to trial sites can be burdensome and time consuming for trial participants, which can negatively affect enrollment.
• Patient monitoring, medical adherence, and retention: Frequent visits to trial sites may become invasive and unpleasant, leading participants to drop out of trials.
• Clinical-trial diversity: Companies often struggle to enroll diverse populations in clinical trials because trial sites may be inaccessible to underrepresented populations. Recent research identified access as one of the biggest barriers to enrolling diverse trial participants.

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:

• AI-enabled study design could help optimize and accelerate the creation of patient-centric designs. This could help to reduce patient burden, decrease the number of amendments, increase the likelihood of success, and improve overall efficiencies.
• AI is driving more innovative ways of collecting clinical-trial data and reducing reliance on in-person trial sites. For example, by capturing data from body sensors and wearable devices such as bracelets, heart monitors, patches, and sensor-enabled clothing, researchers can monitor a patient’s vital signs and other information remotely and less invasively. AI algorithms, in combination with wearable technology, can reveal real-time insights into study execution and patient adherence.
• Coupling AI with robotic process automation can harmonize and link data across different modalities of data collection.
• Machine learning applied to clinical data could help illuminate complex relationships between different data domains—and enable automated data management.
• Auto-generating content (using natural language generation) for trial artifact creation can streamline and accelerate the regulatory document-authoring process.

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:

• Tapping more participants and more diverse populations: AI-powered wearable devices can lessen the need for participants to travel to a physical site, which can enable organizations to recruit patients and diversify clinical trials participation.
• Boosting participant retention: Remote patient monitoring allows patients to participate in clinical trials with fewer potential hassles. AI algorithms can also be used to understand individual patient behaviors or needs, resulting in more patient-centric interactions and better retention.
• Producing faster trials at lower cost: Using AI, life sciences companies can reduce the cost and time required to process clinical-trial data through smart automation, improved efficiency, and less need for rework.
• Increasing reusable data: Organizations can use AI technologies to intelligently reuse existing data based on standards and metadata, reducing the need to start from scratch across trials.

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

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