In the final part of this three-part conversation, Liam and Ted close things out with Scott Burgher, Director of Patient Operations at Quest Diagnostics, looking ahead at where AI genuinely moves the needle in recruitment, and where the industry still gets feasibility wrong.

Scott breaks AI's near-term impact into two practical buckets. The first is administrative: compressing study startup from six to twelve months down to weeks by automating document collection, regulatory submissions, IRB correspondence, and budget negotiations, work that's repetitive by nature and ripe for automation. The second is data-driven: using EHR and lab data to flag genuinely qualified patients rather than relying on broad disease prevalence, cutting down what used to take a coordinator six weeks of manual review.

They Explore:

1. Where AI can realistically compress study startup timelines, and where it can't
2. Why mining EHR and lab data beats broad disease-prevalence estimates for site and patient feasibility
3. Why the human element becomes the differentiator as software gets commoditized
4. Why most feasibility processes are still solving the wrong problem

Scott's closing thoughts on data, predictive medicine, and the road to digital twins. The conversation closes on feasibility, which Scott calls broken at its core, working backward from protocol specifics to find the real "needle in the haystack" instead of the whole haystack. A thoughtful close to the series.

In Part 2 of this three-part conversation, Liam and Ted continue with Scott Burgher, Director of Patient Operations at Quest Diagnostics, going deeper into why recruitment keeps falling short.

Liam reframes the problem from the ground up: the industry treats recruitment as a marketing function obsessed with filling the top of the funnel, when the real work is moving patients all the way through it. Scott connects this to his experience running end-to-end mock trials years ago, testing the full patient journey, not just whether an ad worked, but whether someone could realistically sit through a four-hour infusion or navigate a handoff between site staff who'd never met them.

The conversation turns to site economics, which Scott calls structurally broken: sites invest in staff and training months before seeing payment, and when that investment doesn't pay off, they quietly stop prioritizing the study. As Scott puts it, that's not a performance problem, it's a structural one, though it shows up in the data looking exactly like one.

Ted pushes the conversation toward what actually closes that gap: a real feedback loop between sponsors and sites, where issues get surfaced, acted on, and communicated back, not just logged and forgotten.

A grounded look at what it actually takes to fix recruitment from the inside.

In Part 1 of this three-part conversation, Liam and Ted sit down with Scott Burgher, Director of Patient Operations at Quest Diagnostics, who brings over 20 years of patient recruitment experience spanning healthcare marketing, clinical trial agencies, large consulting, and big tech, to talk about the good, the bad, and the ugly of patient recruitment.

In this episode, Scott breaks down why recruitment keeps missing the mark. He argues that sponsor decision-making cycles are a massively underestimated blocker, a recruitment strategy can be fully approved by the study team and still sit untouched in legal or procurement for months while the clinical timeline keeps ticking, often defaulting back to digital and social media ads as a last resort. Liam connects this to a separate problem he's seen across budgets: an "iceberg" of recruitment costs sitting below the surface, the centralized strategy and patient-processing work that rarely gets line-itemed alongside site costs.

They Explore:

1. Why the 86% missed-enrollment-deadline statistic hasn't moved in over a decade, even as industry awareness has grown
2. How sponsor decision-making cycles quietly stall recruitment strategy for months before outreach even begins
3. The "iceberg" problem: recruitment costs that never make it into the visible budget
4. Why most patients never hear their doctor recommend a clinical trial, and what that gap costs
5. Where AI is genuinely useful in patient identification, and where it's being oversold as a fix
6. The role of primary care physicians in patient recruitment

A candid look at the structural issues behind patient recruitment.

In this special episode, we mark Ted Trafford's 31st year in clinical trials with a candid reflection on what three decades in this industry actually teaches you.

It started with a LinkedIn post Ted wrote where is says that after 31 years, he said he'd learned nothing. Listen in to hear what he meant by this. Liam and Ted go on to discuss the more experience you accumulate, the more you realise how much you still don't know. Liam frames this through the circle of competence: as your knowledge grows, so does your awareness of everything sitting outside it. Thirty-one years in, Ted feels that more than ever, and both hosts agree that the willingness to say "I don't know, can you explain that to me" is what separates people who keep growing from people who stop.

The conversation also turns to recruitment, where Ted has been quietly building a list of every factor that affects clinical trial enrollment. One item on that list stands out: story. Most sponsors, Ted argues, don't have a compelling narrative around their molecule or their trial, and that gap costs them more than they realise. Liam connects this to something much older than clinical research. Humans have always been moved by story before they're moved by data.

This episode is a grounded, honest conversation about what it means to spend a career in this industry, and still be learning.

Every trial starts with a number on a slide, patients needed, months allotted, a clean straight line on a chart.

Liam and Ted pull that line apart in this episode, starting with Lasagna's Law: the 1970s observation that eligible patients seem to vanish the moment a trial opens and reappear the moment it closes. They dig into why recruitment forecasts go wrong not because the math is bad, but because the assumptions underneath the math are never tested, tracked, or even written down.

Liam reframes recruitment forecasting using a sat-nav analogy: the plan is your estimated arrival time, the forecast is what updates as you actually drive. Ted brings a wildlife photographer's lens, literally, arguing most sponsors are zoomed in on the wrong details and missing the bigger picture entirely.

A grounded, practical conversation for anyone responsible for building a forecast around best-case, worst-case, and base-case scenarios instead of one optimistic line.

In this episode, Liam and Ted explore one of the most underexamined problems in clinical trial recruitment: patient motivation is not static.

Drawing on real-world experience from a late-night participant funnel audit to an insomnia study no-show Liam introduces two interconnected frameworks: the Decision Threshold Matrix and the Trial Value Equation.

The matrix maps patient motivation across four quadrants defined by disease severity and perceived treatment adequacy. It breaks down how patients unconsciously calculate whether participation is worth it and why trial teams consistently inflate the denominator (burden) without strengthening the numerator (benefit).

Together, these tools offer a behavioral science-grounded lens for understanding when, why, and how patients engage with research and what teams can do about it.

This episode is for anyone working in patient recruitment, site management, or protocol design.

In the final episode of our three-part series with Jill Fikowski, founder and CEO of Changemark Research + Evaluation, we get into one of the most persistent problems in clinical research: retention.

Jill unpacks why participants drop out and it's rarely what sponsors assume. From there, we get into the pressure that lands on research coordinators when enrollment is behind, the practices that pressure produces. We also look at what happens after the last visit particularly in psychedelic trials and whether researchers have a responsibility that doesn't end when the database closes.

Retention problems, Jill argues, are almost always inception problems. And solving them starts with a question most teams never ask.

Part 3 of 3.