Precise Enrollment Projections - IMPOSSIBLE Without Quantitative SoC Insights

In the dynamic world of clinical trials, where speed and accuracy are paramount, an often-overlooked factor can play a pivotal role in a trial’s success or failure: the local standard of care (SoC). While global clinical trial strategies are crafted with broad frameworks, nuances in healthcare delivery, treatment expectations, and disease prevalence vary significantly by region. Quantitative insights into local SoC can empower researchers, trial sponsors, and CROs to design trials that are more inclusive, effective, and efficient. So, let’s dive into why these insights are essential and how they can help reshape clinical trial outcomes.

The Status Quo: How Standard of Care Insights Are Gathered Today

Right now, gathering Standard of Care insights happens in two main phases:

1. “Desk” Research: Experts working on early trial design and protocol development do some research to get a broad understanding of where they might find eligible patients and high-level barriers. This is called basic patient feasibility. It’s essential not to skip this step, as deciding on country prioritization early on will later save you tons of time with local KOLs and investigators to refine specifics about local treatments.
2. Field Insights from KOLs and Investigators: After setting an overall strategy, teams turn to KOLs and investigators for insights into the local diagnostic and treatment pathways. Local pharma/CRO affiliates or medical affairs professionals typically handle this step.

The Challenges

There are three main challenges with the current approach:

1. Inefficiencies in “Desk” Research: Desk research often leaves gaps in local SoC knowledge. Experts either rely on claims data (mostly available in the U.S.) or sift through Health Technology Assessments. HTAs are valuable documents recommending treatments as SoC, but they don’t always reflect what’s actually happening in the country.
2. Inconsistent Local Investigator Insights: Gathering insights from investigators is the most effective way to understand local SoC today, but it’s no easy task. It requires a lot of back-and-forth with doctors, ideally those you already know. Even then, the insights are often logged as emails, conversation summaries, or Excel files, making them hard to keep up-to-date.
3. The Limitations of Current Prioritization and Projection Tools: Large companies have invested in platforms to prioritize countries and project enrollment to support trial and budget strategies early on. These platforms are mostly built on historical data, using various algorithms on top of internal and third-party datasets. They aim to predict which countries will be feasible for finding eligible patients and project recruitment speed, timelines, and budgets. But here’s the catch: these tools are based on past performance without considering the current SoC.

If you are interested in how they think about such platforms, you can listen to my episode with Travis Caudill , Vice President, Feasibility & Clinical Informatics at ICON plc: Building the Batman Belt

Imagine basing projections on recruitment rates from trials conducted before biologic drugs were mainstream, and expecting similar rates today even though biologics are now widely available. This can be misleading; projections need to consider not just what happened in the past but also today’s SoC context.

Another relevant video that I can recommend you watch is Human Data & AI: Training Our Machines Better Than We Trained Our Grandfathers with Lisa Moneymaker from the last Innovation Network Gathering .

Qualitative vs. Quantitative SoC Insights

Most of these platforms work best with data they can analyze in graphs and charts. That makes it tough for reports and free-text data to factor into projections and prioritizations. This barrier limits Data Science teams from integrating “current” SoC into trial projections, making their analysis less precise. Some companies have tapped into RWD and pharmacy sales volume datasets, but these datasets’ limited geographical coverage introduces bias.

Enter GenAI

This is where GenAI steps in. At TrialHub, we’ve used AI algorithms to gather a wide range of SoC information in real-time, including qualitative data traditionally collected in report form. Using GenAI, we transform these qualitative datasets into quantitative insights, providing a clear, tabular view of critical questions like:

• Where are eligible patients?
• How do they become eligible?
• What motivates them to join a trial?

Our data covers the globe and every indication.

We recently won the Whale Tank award for this innovation, and here’s my 3-min Whale Tank pitch.

Why We Do What We Do

For those who’ve followed our journey, you know we’re all about bringing research closer to patients. Our goal is to make trials faster and more aligned with patient realities. Updating global SoC insights into a format the industry can easily use helps us avoid unnecessary trial delays, bringing trials to the patients who need them most. Let’s GO!

If this resonates with you and want to support me, I’d love to hear your thoughts in the comments or have you share this article with your network. Thank you! ??????