Modernizing clinical data review processes with AI, ML, and advanced analytics

A clinical data reviewer plays an integral part within the overall biometrics team, and against a backdrop of increasing decentralization with more data collected away from traditional sites, the role is becoming increasingly instrumental. Alongside this growing importance, the availability and adoption of artificial intelligence (AI) and machine learning (ML) is ushering in promising opportunities to make the clinical data review process more efficient and effective, while accelerating the shift from data management to data science. In this latest Biometrics Services Spotlight blog, we sit down with Diane Lacroix, VP of Clinical Data Management at eClinical and Gnanesh Narasimhamurthy, Associate Director of Data Management to explore the immediate impact of this evolution, and the opportunities and challenges that lie ahead.

The evolution of the clinical data reviewer role

While inter-related roles, clinical data reviewers and clinical data managers make distinct contributions to the team. Typically holding a medical or clinical background, the clinical data reviewer is responsible for reviewing data for completeness and accuracy, identifying data inconsistencies, and collaborating with the clinical trial team to resolve issues and help them to make informed decisions. At eClinical Solutions, our dedicated clinical data review team forms part of biometrics services delivery, supporting sponsors of all sizes with the data components of their trials.

In today’s speed-driven and data-centric development landscape, the stakes are high and there is an overarching imperative for clinical data teams to adapt across processes, people, and technology to make sense of data chaos and ensure that clinical teams have the right information to make informed decisions.

In this context, leveraging technology to enhance the clinical data review process is a priority for the eClinical Solutions biometrics function. Gnanesh explains how visualization and analytics capabilities within elluminate^® are helping to unlock even greater value from his role. “Performing data review and safety data reconciliation can be a highly labour-intensive activity,” he says. “Manual processes are not scalable with the trajectory towards greater data volume.” For Gnanesh and his team, having access to analytics applications within elluminate are invaluable for enabling greater focus on discrepancies to provide more informative insights for clinical counterparts.

He notes that, “We perform data review using data visualizations, custom data listings and graphical patient profiles. These visualizations help us to focus directly on the outliers or issues so we can more easily look at the data from an aggregate perspective then drill down as required.”

These efforts to streamline the data review process, he concludes, save time and effort while enhancing data quality.

Diane adds, “The overarching goal of our clinical data review team is to add value as part of the data management team, by performing review from a clinical perspective which complements the data management review, and ultimately deliver a higher quality dataset for client decision making. ”

Risk-informed perspectives

For Diane, the evolution of the clinical data reviewer role, dovetails seamlessly with the growing necessity for adoption of risk-informed approaches across the trial life cycle. “The management of risk belongs to everyone within the clinical trial,” she notes. “Sponsors need and expect a layered approach to risk management.” That sense of risk co-ownership is one of the factors that has driven Gnanesh and Diane to invest in the clinical data review team as part of the wider data management team and assure the quality of overall deliverables by incorporating a clinical perspective.

As the industry is driven to focus its efforts on the truly critical data, the eClinical Solutions biometrics team is leveraging the tools available to look across all the data sources available including and beyond EDC (Electronic Data Capture) – from wearables, sensors, and other external data, and identify important trends. Diane notes, “This holistic combination of data management, statistics, and clinical review, coupled with elluminate and AI and ML enabled technologies such as eIQ Review, is unique. Being able to pull the data together more effectively and interrogate it increases data quality and mitigates risks. The goal is to provide our clients with better information to make data-driven decisions.”

Ongoing transformation

Both Gnanesh and Diane highlighted opportunities for further clinical data review transformation, through continued application of artificial intelligence and machine learning technologies. Gnanesh notes that he anticipates an expansion from tactical use of automation for specific tasks, towards more sophisticated approaches. He said, “Currently, it’s possible to automate some of the simpler, repetitive activities like query posting or performing reconciliation. Now, we can take that next step forward in exploring the use of AI/ML technology to achieve actionable or and guided review, identify data patterns and make better informed decisions. “

Ultimately, adds Diane, this forms part of an overarching strategy towards building centralized monitoring capabilities and reflecting an industry drive accompanying the acceleration of decentralized and hybrid trials.

She says, “Site-centric data collection is decreasing and at eClinical Solutions we are particularly well positioned with our technology, people and processes to ingest and handle that non site data.”

This, she says, reflects a wider priority of the biometrics services team to adopting innovations that will help our customers.

“Our focus is to stay ahead of the clinical trials evolution, and we innovate in a practical way through a phased approach. The addition of AI and ML to the toolkit is a power boost in the transformation to a modernized data management approach and journey towards clinical data science.”

eIQ Review, part of Data Central in the elluminate Clinical Data Cloud, provides AI-enabled data review capabilities for data managers & clinical data reviewers to ensure data integrity in a more efficient, scalable way. Learn more about eIQ Review.

eClinical’s Biometrics Services combine top talent with best-in-class technologies and processes to deliver high-quality clinical data solutions for today’s complex, speed-driven trials. Learn more about our Biometrics Services.

Key Takeaways

With a move away from site-centric data collection, clinical data review is an increasingly vital function within data management.

Adoption of technologies such as AI and ML can significantly enhance the clinical data review process, leading to higher quality and efficiency.

With the availability of technologies like eIQ Review , clinical data reviewers are empowered to identify trends and outliers across aggregated data for studies and programs.

Author

Liz Cole

Director, Product Marketing, eClinical Solutions

Liz Cole has over 25 years’ experience in commercial roles within the biopharmaceutical sector with a particular focus on clinical data transformation, innovative trials, and advanced analytics. At eClinical Solutions, Liz is responsible for Biometrics Services’ marketing strategy, planning, product messaging, and content.