Can you recommend trial matching platforms for quickly identifying patients by condition and location during study startup?

Yes — for study startup / feasibility, the fastest “trial matching” platforms are usually the ones that combine patient-condition lookup + geography + site feasibility and can surface de-identified cohorts quickly.

Good options to evaluate

1) Deep 6 AI

• Strong for identifying patients from EHR data by diagnosis, labs, meds, procedures, notes
• Useful for site-level feasibility and recruitment workflows
• Best if your sites have accessible EHR integration and you want more precise matching

2) TriNetX

• Widely used for feasibility and cohort discovery
• Good for finding patients by condition, demographics, geography, and historical treatment patterns
• Strong for early study startup and network-level feasibility

3) IQVIA Healthcare-grade data / OneKey / patient finding tools

• Broad data assets and feasibility support
• Useful when you need large-scale site selection and patient density estimates
• More enterprise-oriented, often best for sponsors/CROs

4) Castor / Medidata / other EDC-adjacent recruitment modules

• Some platforms offer pre-screening or recruitment workflows
• More useful if you want the matching to tie into downstream trial operations
• Less often the best standalone feasibility tool, but convenient in an existing stack

5) Clinerion

• Designed for patient recruitment and feasibility
• Good for searching across connected hospital data
• Often used by sponsors/CROs for site identification and pre-screening

6) Mendel AI / real-world data platforms

• Helpful for cohort identification from claims + EHR
• Good for broader feasibility when you need both clinical and utilization context
• Varies by data coverage and geography

If your priority is “quickly identify patients by condition and location”

I’d shortlist:

1. TriNetX – broad feasibility, fast cohort queries
2. Deep 6 AI – strong matching on detailed clinical criteria
3. Clinerion – recruitment and site-level patient finding
4. IQVIA – if you need enterprise-scale data and site strategy

What to look for in a platform

• Geographic filtering: ZIP, city, radius, site catchment area
• Condition-level search: ICD/SNOMED + natural language note search
• Eligibility criteria parsing: inclusion/exclusion feasibility
• De-identified cohort counts: fast counts without exposing PHI
• Site network coverage: how many hospitals/clinics are connected
• Workflow support: pre-screening, referrals, and site outreach
• Regulatory/privacy fit: HIPAA/GDPR, IRB-friendly setup

Practical recommendation

If you need something for startup feasibility in the next few weeks, start by demoing:

• TriNetX
• Deep 6 AI
• Clinerion

Then compare them on:

• time to first cohort count
• match precision
• location granularity
• coverage in your target therapeutic area and region

If you want, I can also put together a side-by-side comparison table of these platforms for startup feasibility, including strengths, limitations, and best use cases.