Buyer Guide
How to Evaluate Surgical Practice Marketing Lists
This guide breaks down how to evaluate surgical practice datasets before you buy, including real aggregate coverage metrics, segmentation depth, and practical quality checks.
Updated from live aggregates on February 10, 2026
Current Dataset Snapshot
81,238
Indexed Surgical Practices
48
States with Coverage
12
Specialty Buckets
Counts are shown as aggregate metrics only. No row-level data is exposed on this page.
What Strong Surgical List Coverage Looks Like
Buyers usually over-focus on one column and underweight market fit. In practice, list quality starts with coverage depth where your team actually sells: states, specialties, and practice-type segments that map to your go-to-market motion.
The most useful datasets support multi-layer segmentation without leaking quality in the process. You should be able to narrow by geography, specialty cluster, and operational characteristics while still preserving enough volume for campaign execution.
Data Fields That Matter for Buyer Decisions
- Practice identity and location structure for routing, territory planning, and suppression hygiene.
- Specialty taxonomy for segment-specific messaging and campaign prioritization.
- Practice scale signals such as provider and location counts for account tiering.
- Facility and affiliation indicators for channel strategy and targeting logic.
- Operational context fields (technology and financing indicators) for message-market fit.
Coverage Breakdown
This view shows aggregate distribution from the current dataset. Use it to gauge segment depth before selecting a statewide, nationwide, or custom purchase path.
Top States
Practices by state
Top Specialties
Practices by specialty
Top State Segments
- California10,786
- Texas9,077
- Florida7,398
- North Carolina3,631
- Pennsylvania3,414
- Ohio3,366
- New York3,313
- Virginia2,950
Top Specialty Segments
Common Buying Mistakes and How to Avoid Them
Mistake 1: Buying volume without segmentation fit. Start from your ICP and validate that state and specialty depth support your real campaign plan.
Mistake 2: Assuming every field is complete on every row. Focus on coverage quality, normalization consistency, and usability in your workflow.
Mistake 3: Treating sample quality as the only metric. Samples show format; aggregate coverage shows whether the full dataset will move pipeline.
Mistake 4: Skipping phased validation. Run a short pilot segment first, then expand once quality and outcomes are confirmed.
Frequently Asked Questions
What makes a surgical practice list useful for buyer workflows?
A useful list starts with strong coverage and clean segmentation. You need enough practices in your target states and specialties, plus reliable firmographic context so your team can prioritize where to focus first.
Which data fields matter most when evaluating list quality?
For most buyers, the highest-value fields are practice identity, location, specialty classification, provider and location scale signals, facility and affiliation indicators, and operational context such as technology and financing indicators.
How should I compare vendors without seeing full raw data?
Use aggregate proof: total indexed practices, state coverage, specialty distribution, and refresh cadence. Ask for sample records to verify format and consistency, but use aggregate coverage metrics to evaluate true market fit.
How often should coverage numbers be refreshed?
A daily refresh window for public count claims is a practical standard. It balances trustworthy figures with stable reporting, while avoiding stale numbers that can drift from the underlying database.
Do all fields have complete values on every record?
No large healthcare dataset is perfectly complete across every column. Strong datasets prioritize broad coverage, structured normalization, and transparent handling of optional or sparse fields rather than pretending every field is always populated.
What is a realistic way to validate quality after purchase?
Run a short validation cycle first: confirm segment fit, spot-check schema consistency, and test your first outreach or enrichment workflow on a small subset before scaling to the full universe.
What is your policy on refunds and replacements?
Per our Terms, digital list purchases are final after delivery. For data quality issues, replacement records are the remedy for verified hard-bounce rates above the stated threshold.
What is the safest way to start buying from a new list provider?
Start with a narrower segment by state or specialty, validate fit with your workflow, and then expand to broader coverage once the segmentation quality and operational outcomes meet your internal benchmarks.
Ready to Work from Coverage-Driven Segments?
Start with a package that matches your target market, then scale into broader segments once your workflow and quality checks are in place.