OSP spoke to Frontsin about why the CROs are under greater pressure to deliver measurable results quickly at lower costs. He said the company’s growth initiative enables CROs to evaluate the power of Oracle technology with their potential sponsors. As part of the CRO Growth Initiative, many CRO professionals have also taken advantage of the program’s accelerator that provides go-to-market benefits, technical enablers, and incentives that empower CROs to plan, execute, measure, and reward success in delivering expert services to their end customers.
OSP: Why are multiple data sources (clinical, real-world, genetic) critical in clinical research?
MF: Combining clinical research with real-world data provides deeper insights into the natural history of diseases and the performance of healthcare interventions in medical settings. Real-world data (RWD) sources are pivotal in enhancing clinical trial execution, generating drug safety and efficacy evidence, and drug reimbursement strategies. The ability to have access to multiple sources of RWD is critical from the point of care to trials and beyond. RWD enables providers and researchers to follow patients over the long term to learn and make discoveries. Trials and “research” should not end with drug approval.
While it can be difficult for trial administrators and doctors to manage multiple sources of data, technology can address this challenge by aggregating data from any source for data processing activities, such as data review and cleansing, reconciliation, discrepancy management, and provisioning it in various formats to support analysis and reporting.
OSP: What steps can sponsors take to expand access to clinical trials, especially for patients who do not have access to major research and health centers. How can technology help this process?
MF: The Achilles heel of clinical trials has been access and awareness. The industry has been looking for ways to break through several barriers that have inhibited inclusivity. For example, sites may be more likely to contact their existing patient pool, which may not represent a diverse population. This also includes not adapting their recruitment process to suit diverse populations (e.g. involving families or community groups in the recruitment process).
However, technology is underpinning much of the change afoot. Processes like patient matching are enabled by advanced algorithms and data analytics based on medical records. There is also potential to use data from other sources such as electronic health records (EHRs), lab results, imaging, etc. to connect patients to trials. For example, based on a patient’s genetic sequencing and the type of cancer they have, we could identify a specific trial for the patient. With technology and the ability to use matching within the clinical workflow, we can connect patient care and trials seamlessly.
Automation can help streamline processes from patient recruitment and enrollment to monitoring and reporting, which makes it easier for patients to participate. Many of these tasks are done remotely, as we saw during the pandemic when telehealth and remote monitoring exploded. By putting the patient first and aligning decisions with their needs and preferences, we can lower the bar for patient participation.
OSP: What impact can patients play in clinical trial design, enrollment, retention, and trial data. Why is it so critical to engage them from the start?
MF: Poorly designed trials often crumble when exposed to patient scrutiny and real-life pressures. For instance, a treatment demanding daily medical visits may succeed under ideal trial conditions, but if we apply the same constraints to patients who are balancing jobs, families, and comorbidities in the real world, often adherence drops to unsustainable levels. So, to optimize trial meaningfulness and success, researchers must engage with patients and caregivers as experts in their conditions through authentic conversations from the very beginning of trial design.
This engagement can be achieved through a comprehensive elicitation process that contextualizes the trial within the everyday lives of patients. One way to do this is to host meet-and-greets with the patients, where researchers are deeply immersed in the individual stories of a condition. Researchers may also benefit from self-completion exercises, where patients capture their personal condition timelines and explore techniques to express the conscious and unconscious manifestations of the condition.
It’s critical to engage patients throughout the clinical trial process, not just at the very beginning. That’s because patients and clinicians weigh treatment success and outcomes differently. Patients, who live the daily realities of their conditions, are better able to weigh and determine which outcomes matter most than traditional experts. To that end, patients must also be engaged in trial endpoints to capture how they feel during the trial. Experiences like the impact of the treatment on functional limitations, emotional well-being, and social function provide additional perspectives on the effectiveness of treatment.
OSP: What impact will AI on the future of clinical trials?
MF: AI, machine learning, and related technologies have the potential to transform the entire drug development life cycle. For example, AI is helping discover novel molecular structures and designs, forecast drug-target interactions, and accelerate in-silico drug discovery. These improvements can significantly reduce the time and cost of bringing new treatments to market. AI will also continue to play an important role in analyzing real-world evidence, which is crucial in risk assessment and understanding the effectiveness and safety of treatments in clinical settings, but it could also apply to clinical trial design.
In addition, AI can help make significant progress toward reducing the burden on providers, making it easier for providers to participate in trials at the point of care. Currently, providers must search for clinical trials for their patients, but through expanded interoperability of and access to data, they could obtain the information they need at the point of care.
AI can also impact the less glamorous but critical business of automating cumbersome, manual, and costly processes that still sit at the heart of the life sciences industry. This automation can alleviate many of the manual tasks that exist today, such as staff having to move data from the EHR to the electronic data capture (EDC), therefore enabling them to focus on other important tasks.
Example: Oracle Health Data Intelligence, an open, continuously learning platform to unify data from thousands of sources to create longitudinal patient records. Health Data Intelligence helps transform vast datasets into actionable insights, helps to bridge the gap between clinical research and care, and empowers stakeholders to make informed decisions across the entire healthcare continuum.
Built-in analytics and discovery paradigms, like generative AI capabilities, can help identify trends, and patterns, and generate evidence that can aid in guiding clinical decisions. To date, more than 2,750 data connections have been made and 560M+ longitudinal records created. These metrics and data sources are growing rapidly each day.
OSP: What steps can sponsors and CROs take to maximize their investment in clinical trial technology?
Most importantly, the industry should focus on building automated cloud-based platforms and tools that provide a holistic 360-degree view of the patient, site performance, and overall view of the study. These systems help create a more flexible clinical trial model by reducing the importance of physical location. This eases the patient burden and makes it easier to build a diverse pool of clinical trial candidates from nearly anywhere.
These platforms also offer doctors a more comprehensive, instant view of patient care by connecting external and internal patient data through the cloud. Data that may have taken months to collect can now be accessed in just seconds. With such precise data and analysis, providers can provide increasingly personalized care plans for their patients. Oracle is uniquely positioned to address this because we have the full spectrum from clinical trial software to the electronic medical record (EMR).
OSP: What are some of the key challenges you are hearing from your pharma customers?
MF: CROs and pharmaceutical companies are under greater pressure to deliver measurable results quickly, safely, and at lower costs. They are seeking an integrated platform, fair pricing, and overall expertise. They are looking for experienced technology partners that can deliver integrated high-quality data management platforms that provide comprehensive insights that meet their needs and exceed their expectations.
OSP: What are the key trends shaping the clinical trial market for the remainder of the year?
MF: It’s an exciting time in life sciences and healthcare overall. The growth and impact of AI and Gen AI on the entire clinical trial process and industry at large will remain a critical focus. A recent IDC report predicted that by 2025, transformative patient experiences will be led by 30% of life sciences firms that used GenAI to optimize trial design, hyper-personalize content, and orchestrate empathetic interactions.
Data – how we gather it, organize it, share it, and apply it – will have a significant impact on the future. Being able to connect the right data to the patient at the right moment in time will be imperative to improving the clinical trial process.
We expect to continue to deliver better ways to serve our customers, and keep the patient front and center by finding new and innovative ways to involve patients and providers in clinical trial design and implementation from the very start.
