Good4Patients: Timely Perspective from Rx4good

The 4-Second Skim

• Patients weigh clinical trials against the realities of their daily lives: work, family, care support, and potential uncertainty.
• Advocate input too often comes after protocol decisions are already largely set, leaving teams to solve recruitment challenges with technology or communication tools.
• When advocates help shape the trial journey earlier, technology (including AI) can help patients find, understand, and consider trials designed with patient realities in mind.

A Promising Trial Meets Real Life

Elena is 29 and living with Fabry disease. She receives intravenous enzyme replacement therapy every two weeks — a treatment that is familiar, but sometimes inconvenient. While exploring other treatment options, she uses an AI-enabled tool that helps her find a Phase 3 trial of an oral therapy at a site 90 minutes from home for which she might be eligible. On paper, the trial looks promising. A pill could mean her life no longer revolves around infusion days.

But Elena’s decision is not only about whether she matches the protocol. It is about whether the trial can realistically fit into her life: missed work, caregiver support, and the uncertainty of changing from a treatment she knows to an investigational therapy. It is also about whether the study measures outcomes that matter to her and explains participation in language she can understand and trust.

Seeing Positive Impact in Patient Input

Across our patient advocacy, engagement and intelligence work, we often see teams wrestle with the same challenge: they are trying to improve recruitment after key decisions have already been made.

By the time a study is ready for outreach, the visit schedule, assessment burden, eligibility criteria, site model, and support plan may already be largely set. At that point, teams are often left trying to use communication tools to solve structural problems. We have seen the potential impacts when patients are brought in early:

• In one council, families broadened a team’s understanding of trial burden beyond travel to include missed school, parents’ work schedules, testing anxiety, and whether adolescents felt part of the participation decision.
• In another, patients explained that periodic study measures would not fully capture the daily variability of living with their condition, leading the team to consider a patient diary or complementary patient-experience study.
• And for a cancer trial, patients made clear that tumor response alone does not tell the full story of treatment impact, leading to a recommendation to measure and report quality of life as part of the trial.

Build the Trial Journey Before Recruitment Starts

For biopharma leaders, the opportunity is to design the trial journey around patient participation, with recruiting strategies supporting decisions already informed by patient and advocate input. That starts with gathering their intelligence early enough to have an impact on decisions that reduce obstacles for patients and save sponsors time and money.

In the Elena example, that might mean having patients or advocates review the visit schedule before it is final, testing whether the consent and study explanation answer real patient questions, and equipping site staff with plain-language tools to discuss real-life tradeoffs of participation.

Use AI Where It Helps Patients Most

With that foundation in place, AI can play a valuable role in recruitment. AI-enabled tools may help identify potential participants, translate complex information into plain language, summarize requirements, or help patients prepare questions for a site team. Used well, AI can make the trial easier to understand and navigate, better supporting the human context patients may need as they decide what participation would mean for their lives.

Use patient and advocate input to pressure-test:

• What parts of the protocol may be hardest for patients or care partners to manage?
• Which eligibility criteria, assessments, or visit requirements may be confusing or burdensome?
• Do endpoints reflect outcomes patients recognize as meaningful?
• What practical supports could make participation more realistic?
• What should AI explain, and where should a trained person step in?