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Often overlooked in early evaluation of RBM are the tools and technologies required for successful implementation. Without the right tools to track and prioritize the shift from traditional monitoring to RBM, successful implementation is doubtful, posing detrimental consequences not only for the monitors, but also the overall trial.

5 Essential Cornerstones of RbM Technology

That's because the discussion shifted away from one focused on changing monitoring methods and doing reduced source document verification (SDV) to 'intelligent monitoring.'

An Alternative to Risk-Based Monitoring May Prove to be Beneficial

With so many sponsors and CROs focused on finding the right technology solution for implementing risk based monitoring, a key element often gets overlooked--the risk assessment.

Is Your RBM Strategy Missing a Critical Link?

Video interview with Sepehr Shojaei, VP, Design Solutions, Lightship

Lightship's Approach to Accessible, Representative, and Impactful Clinical Trials

Lightship prioritizes patient accessibility and centricity in clinical trials, ensuring a seamless and positive experience for every participant. Learn more about:

Lightship's mission and core purpose as a company

How Lightship’s mobile nursing teams and home visits enhance study participation and retention

The role of mobile research units in the community and how they enhance participant education, accessibility, and convenience

Sepehr Shojaei, VP of Design Solutions at Lightship, talks about how the company works with pharma and contract research organization (CRO) partners to support clinical trials that are patient-first and accessible.

Latest Conference Coverage

Lightship’s Approach to Accessible, Representative, and Impactful Clinical Trials

Video interview with David MacMurchy, CEO of Lightship

Accelerate Inclusive Enrollment and Delivery with Lightship

Lightship focuses on patient accessibility and centricity in clinical trials, ensuring the patient experience is a positive one. Learn more about:

Lightship's impact on achieving diversity in clinical trials by engaging underrepresented patient populations

How Lightship utilizes hybrid and decentralized trial designs to reduce patient burden

The impact a patient centric model has on clinical trial participation and outcomes

David MacMurchy, Chief Executive Officer at Lightship, offers some insights into the company's mission toward improving equity and diversity in clinical trials. 

In this video interview, Michel van Harten, MD, CEO, myTomorrows, discusses where the clinical research industry currently stands with AI adoption. 

Bridging Innovation and Trust with Artificial Intelligence

, Michel van Harten, MD, CEO, myTomorrows, discussed the integration of artificial intelligence (AI) in clinical trials, highlighting its potential to reduce costs, accelerate timelines, and improve inclusion. Looking forward, advancements with AI are expected to speed up drug discovery, predict drug efficacy, and develop digital twins for personalized treatment simulations, potentially reducing trial risks and costs.

ACT: Where do you think the clinical trials industry currently stands with AI integration?

I think in general, there's a lot of optimism, especially on the industry side. Sponsors and also CROs (contract research organizations) are recognizing the potential of AI to reduce costs, accelerate timelines, and also improve inclusion. I think, for example, AI is already being actively used to design trials and protocols to identify trial sites with access to specific populations, and also to match eligible patients faster and enroll them faster.

On the healthcare provider side, besides early adopters, there's sometimes hesitancy. In general, in healthcare, you must be very careful with introducing technology. I think first, the quality of the data is everything. For example, for us, we work with sensitive patient and clinical data, and although we do not advise physicians and patients, we do give them the options, the pre-approval treatment options, so clinical trials, that are out there and they need to be correct, so our AI models are as good as the data we feed them. We invest also heavily in cleaning and validating the data before the AI part can even begin.

Then second, I think transparency and explainability is important, and they're crucial in building trust. In healthcare, patients and providers have to understand how decisions are made, so we always show them the reasoning in a very detailed way. In addition, we provide them the option to correct the AI if they disagree, so the physician or the patient is always in the driver's seat. Until the AI is perfect, you have to build in checks and balances and develop, together with those physicians and key opinion leaders to remove skepticism early on.

In this video interview, Krinx Kong, chief commercial officer, Cognivia, discusses how trial design and timelines could be impacted by a potential mandate for placebo use across all vaccine trials.

How Vaccine Trials Could be Affected by Potential Policy Changes

, Krinx Kong, chief commercial officer, Cognivia, discussed the potential impacts of policy shifts on vaccine trial design and patient perceptions. Stricter regulations could increase trial complexity, affecting recruitment and retention. Kong emphasized behavioral insights as crucial to understanding patient motivation and mitigating dropouts. Looking forward with the potential for policy changes in vaccine trials, clinical operations professionals must focus on flexibility, behavioral visibility, and effective communication to ensure success.

ACT: US Health and Human Services Secretary Robert F. Kennedy Jr. recently 

 on supporting placebo-controlled trials for all new vaccines. How could a potential policy shift in this area affect trial design and timelines?

First, it's important to clarify that placebo-controlled trials are already the gold standard in vaccine development, and are required by both the FDA and EMA when no approved alternatives exist. Therefore, the notion that these aren't already in use is misleading, or at least lacks context. Now, if a policy shift were to mandate placebo use in all vaccine trials, even where active comparators might be more appropriate, we'd likely see significant impacts on both design complexity and timelines. First, ethics boards may push back. If an effective vaccine already exists, withholding it in favor of placebo could be viewed as unethical. This leads to delays, extra review cycles, and potential public concern. Next, placebo-controlled designs often require longer follow ups, which can prolong timelines by months or even years. Lastly, these designs are frequently more difficult to recruit for, patients and parents may be reluctant to participate if there's a genuine chance of receiving no active protection. If policy does shift, having tools that can adapt trial design without compromising data integrity or patient trust will be essential.

A pharmaceutical company aimed to launch a Phase III clinical trial for a new oncology drug, focusing on efficient and effective patient recruitment across diverse regions. They evaluated patient populations by analyzing demographics, social determinants of health, and geography. Investigator profiling included practice details, affiliations, referral networks, and clinical trial experience. The final step involved selecting and extracting target lists for implementation.



Optimizing Phase III Oncology Trial Recruitment with Data-Driven Insights

Clinical trials are crucial for drug development but often face delays due to poor site selection, which can lead to protocol violations, poor data quality, and increased costs. Ninety percent of clinical trials fail to meet their timelines, and issues like inadequate staffing and patient recruitment are significant challenges. Traditional site selection methods are inefficient, and the COVID-19 pandemic has further complicated the process. Data-driven insights can optimize site selection by evaluating current data on patient populations, investigators, and site capabilities.



Courtney McBean

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