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.