Data-Driven Principal Investigator Identification: A Smarter Way to Enable Clinical Trial Success

Written by -Rohit Madaan,Ishani Shah

The Problem

Sponsors still choose investigators largely through personal networks and self-reported feasibility data. According to the 2025 WCG Clinical Research Site Challenges Report, physician interest is waning, patient recruitment is harder, and trial start-up keeps slipping.

Waning physician interest: administrative burden and competing clinical demands make fewer physicians willing to serve as PIs.
Self-reported feasibility: site estimates on patient availability and staffing are rarely validated against real-world evidence.
Fragmented insight: manual spreadsheets and disconnected data leave sponsors with limited visibility into a PI's actual workload or track record.

A Data-Driven Way to Find the Right PI

ProcDNA's Principal Investigator Scoring Engine replaces guesswork with evidence. It pulls together claims, prescription, CMS Open Payments, affiliation, and clinical trial data to build an objective picture of every investigator, then scores and ranks them against the needs of a specific trial.

A comprehensive PI database: built from claims, prescription, CMS Open Payments, affiliations, and trial history, then enriched with academic and social signals.
A composite scoring index: each PI is scored across patient potential, clinical value, academic affinity, and social affinity to produce one comparable ranking.
A ranked shortlist: sponsors get a clear, ranked list of investigators aligned to trial needs, not just the usual familiar names.

Built for the Teams Who Choose Your Investigators

If you work in clinical operations, feasibility, or site selection, this paper addresses a problem you already know well.

Stop relying on who you know. Get access to diverse, emerging, and high-potential investigators that a personal network alone would miss.
Trust the feasibility numbers you're given. Score PIs against real claims and enrollment data instead of self-reported estimates.
Cut delays before they start. Identify investigators with the patient volume and trial experience to hit enrollment targets on time.
Defend your site selection with evidence. Give sponsors and CROs a ranked, data-backed shortlist instead of a name from a personal network.

What Changes

Less network bias in how investigators get chosen
Validated feasibility, checked against real claims and enrollment data
Stronger enrollment predictability across the trial
Lower operational risk from delayed or mismatched sites

These outcomes come from replacing a manual, relationship-driven process with an evidence-based one.

What You'll Learn

The four-step methodology behind ProcDNA's PI Scoring Engine, from database build to final shortlist.
How the Principal Investigator Index turns patient potential, clinical value, academic affinity, and social affinity into one composite score.
Why self-reported feasibility data alone cannot predict enrollment.
What a data-backed PI profile looks like in practice, built from a real-world example.
Where this approach is headed next, including protocol-specific enrollment forecasting.

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