Richard Young, Vice President, Veeva Vault CDMS Strategy09.25.23
Over the last 12 months, regulators have sought new ways to foster more ambitious global clinical research. In the U.S., the U.S. Food and Drug Administration (FDA) will soon require companies seeking approval for new drugs to submit a diversity plan for study enrollment to increase the number of trial participants from under-represented groups.¹ In Europe, EU CTR aims to harmonize clinical trial applications and make multi-country studies more efficient, while the U.K.’s regulator is focused on faster approvals of trial submissions.
New regulations, patient needs, and cost pressures are pushing us toward faster, more ambitious clinical research. Study teams are running the relay race for all of us and need closer collaboration between patients, sites, and data managers to pass the data baton quickly to regulatory and beyond. Success hinges on lining up the data journey with advances in patient choice and experience in an era where a trial is measured in billions of data points.
As we enter an era centered on patient choice, we should let patients decide how they contribute to research. They should be able to participate in person or through digital methods based on their profile, therapeutic area, daily health status, and personal preferences. Delivering this standard of patient-centricity could be transformational for long-term study enrollment, participation, and retention.
More flexible study designs could expand a patient’s options: for instance, Stage 4 cancer study participants may benefit from a hybrid approach, enabling them to stay at home or visit a center based on their well-being, symptoms, need for advice, or how they feel that day. Alternatively, a tech-savvy teenager might prefer to avoid travel or daily disruption and opt to take part more remotely in an acne study. Wearable technology, electronic Patient Reported Outcomes (ePRO), and remote monitoring devices could all help track patients’ health statuses.
Successful patient onboarding heavily depends on sites adopting and embracing technology. However, in recent years, efforts to reduce the patient burden during trials have had the seesaw effect of increasing site effort. Site users now spend extensive time training staff to navigate multiple platforms introduced by different sponsors, with limited support. Disconnected solutions such as eConsent, electronic clinical outcome assessments (eCOA), and ePRO have helped reduce the paper barrier but force sites (and patients) to duplicate their data entry because these systems don’t share data.
To achieve operational excellence during clinical trials, we must end patient-site tradeoffs and account for the collective needs of both critical constituents. By adopting a connected approach to data and systems, we can lighten the site and data management burden without detracting from the patient experience. Replacing siloed applications with a platform approach would remove sites’ integration challenges.
Common-sense provisioning (including ‘Bring Your Own Device’ [BYOD] policies) would be popular with sites and patients by streamlining data collection during site visits. As well as reducing costs, BYOD avoids common delays to trial data transfer, like issues with device shipping, storage, or connectivity. When BYOD is not feasible, offering alternative fully integrated solutions can extend a trial’s reach and broaden patient engagement.
Indeed, 75% of critical trial data may come from outside of EDC. Delays in aggregating data from sources such as wearables, ePRO, and remote monitoring devices hinder decision-making and lead to a decline in decision confidence. We need solutions that enable complete and concurrent data review. They should also equip data managers with the tools to aggregate, prepare, and clean the high-volume, diverse data sets that modern trials present. This requires systems capable of capturing different data formats with next to no integration. Only then can we tackle the five Vs of clinical data: volume, velocity, variety, veracity, and value.
During the pandemic, quality and safety requirements were set to a high bar, but speed was the primary focus. Two key questions mattered to trial outcomes: “Does it work?” and “does it hurt?” Cost was a secondary, perhaps even tertiary, consideration.
Today, we are experiencing a tremendous bounce back. Study teams need to focus on operational excellence as much as scientific rigor and quickly produce evidence that treatments are economically viable and significantly better than existing options. This means they need more time to evaluate data against a broader set of indications. Data review has to be comprehensive but also smart and efficient. Automating time-consuming daily tasks (e.g., data cleaning, medical coding, safety signals) could make reconciliation faster and less difficult. As artificial intelligence plays a bigger role in data review, study managers may one day query their databases using natural language systems.
Finally, there’s more we can do during study follow-up to improve low levels of trial participation. If we want to foster a broader interest in contributing to research, we need to give something back to patients who volunteer their time to progress potential treatments—for example, sharing data or trial outcomes to build trust in the results. Greater transparency would not only boost repeat participation but also increase public trust in clinical research.
Doug Bain, chief technology officer at KCR, noted, “The regulators have an opportunity to base conditional approval on patient follow-up. For safety reasons, more Phase 3B studies could automatically roll on to long-term follow-up within the same data environment.”
The stakes are high during a clinical trial and reaching the finishing line requires seamless information exchange from clinical to regulatory to safety. For this to happen, study teams need access to clean data as trials take place. Only then will they be equipped to make confident decisions faster, without dropping the baton.
Reference
Richard Young is vice president of strategy for Veeva Vault CDMS and a 25-year veteran in life sciences. He is responsible for defining the strategy and direction for Vault CDMS, especially concerning clinical data management. Richard brings Veeva customers a keen executive vision and proven operational experience in data management, eClinical solutions, and advanced clinical strategies.
New regulations, patient needs, and cost pressures are pushing us toward faster, more ambitious clinical research. Study teams are running the relay race for all of us and need closer collaboration between patients, sites, and data managers to pass the data baton quickly to regulatory and beyond. Success hinges on lining up the data journey with advances in patient choice and experience in an era where a trial is measured in billions of data points.
Ending Patient-Site Tradeoffs
We’ve seen impressive gains in patient engagement beyond what could have been imagined when the industry piloted digital and cloud technology in 2010. New digital ways to engage in research became a top priority over the past three years out of necessity. Digital technologies are now mainstream, enabling medical companies to engage patients in trials quickly, cost-effectively, and across more diverse locations. However, for these advances to become truly game-changing, we must find ways to deliver studies that are real-world ready, including for people living with rare and serious diseases who find it impossible to attend research sites while their health deteriorates.As we enter an era centered on patient choice, we should let patients decide how they contribute to research. They should be able to participate in person or through digital methods based on their profile, therapeutic area, daily health status, and personal preferences. Delivering this standard of patient-centricity could be transformational for long-term study enrollment, participation, and retention.
More flexible study designs could expand a patient’s options: for instance, Stage 4 cancer study participants may benefit from a hybrid approach, enabling them to stay at home or visit a center based on their well-being, symptoms, need for advice, or how they feel that day. Alternatively, a tech-savvy teenager might prefer to avoid travel or daily disruption and opt to take part more remotely in an acne study. Wearable technology, electronic Patient Reported Outcomes (ePRO), and remote monitoring devices could all help track patients’ health statuses.
Successful patient onboarding heavily depends on sites adopting and embracing technology. However, in recent years, efforts to reduce the patient burden during trials have had the seesaw effect of increasing site effort. Site users now spend extensive time training staff to navigate multiple platforms introduced by different sponsors, with limited support. Disconnected solutions such as eConsent, electronic clinical outcome assessments (eCOA), and ePRO have helped reduce the paper barrier but force sites (and patients) to duplicate their data entry because these systems don’t share data.
To achieve operational excellence during clinical trials, we must end patient-site tradeoffs and account for the collective needs of both critical constituents. By adopting a connected approach to data and systems, we can lighten the site and data management burden without detracting from the patient experience. Replacing siloed applications with a platform approach would remove sites’ integration challenges.
Common-sense provisioning (including ‘Bring Your Own Device’ [BYOD] policies) would be popular with sites and patients by streamlining data collection during site visits. As well as reducing costs, BYOD avoids common delays to trial data transfer, like issues with device shipping, storage, or connectivity. When BYOD is not feasible, offering alternative fully integrated solutions can extend a trial’s reach and broaden patient engagement.
Establishing Trust in Study Outcomes
Adaptive trial design is focused on making confident decisions faster but the reality is often different. During data review, study teams need data from all sources, not just traditional electronic data capture (EDC).Indeed, 75% of critical trial data may come from outside of EDC. Delays in aggregating data from sources such as wearables, ePRO, and remote monitoring devices hinder decision-making and lead to a decline in decision confidence. We need solutions that enable complete and concurrent data review. They should also equip data managers with the tools to aggregate, prepare, and clean the high-volume, diverse data sets that modern trials present. This requires systems capable of capturing different data formats with next to no integration. Only then can we tackle the five Vs of clinical data: volume, velocity, variety, veracity, and value.
During the pandemic, quality and safety requirements were set to a high bar, but speed was the primary focus. Two key questions mattered to trial outcomes: “Does it work?” and “does it hurt?” Cost was a secondary, perhaps even tertiary, consideration.
Today, we are experiencing a tremendous bounce back. Study teams need to focus on operational excellence as much as scientific rigor and quickly produce evidence that treatments are economically viable and significantly better than existing options. This means they need more time to evaluate data against a broader set of indications. Data review has to be comprehensive but also smart and efficient. Automating time-consuming daily tasks (e.g., data cleaning, medical coding, safety signals) could make reconciliation faster and less difficult. As artificial intelligence plays a bigger role in data review, study managers may one day query their databases using natural language systems.
Finally, there’s more we can do during study follow-up to improve low levels of trial participation. If we want to foster a broader interest in contributing to research, we need to give something back to patients who volunteer their time to progress potential treatments—for example, sharing data or trial outcomes to build trust in the results. Greater transparency would not only boost repeat participation but also increase public trust in clinical research.
Doug Bain, chief technology officer at KCR, noted, “The regulators have an opportunity to base conditional approval on patient follow-up. For safety reasons, more Phase 3B studies could automatically roll on to long-term follow-up within the same data environment.”
Running a Faster Relay Race
To meet the new standard of clinical research ambition, we need goals that reflect our trial needs today—not just speed but also scientific accuracy, reduced patient burden, and operational excellence. Holding ourselves accountable requires a more diverse mix of voices around the proverbial table, contributing to everything from study design to patient follow-up. One example of how to do this effectively comes from recent history, when data management adopted a front-and-center position in the race to deliver COVID-19 research. Global decision-making became data-driven decision-making—the perfect illustration of the need for good data and excellent data management.The stakes are high during a clinical trial and reaching the finishing line requires seamless information exchange from clinical to regulatory to safety. For this to happen, study teams need access to clean data as trials take place. Only then will they be equipped to make confident decisions faster, without dropping the baton.
Reference
Richard Young is vice president of strategy for Veeva Vault CDMS and a 25-year veteran in life sciences. He is responsible for defining the strategy and direction for Vault CDMS, especially concerning clinical data management. Richard brings Veeva customers a keen executive vision and proven operational experience in data management, eClinical solutions, and advanced clinical strategies.