ZoomInfo for physicians: what works, what breaks, and how to evaluate it fast

Ben Argeband, Founder & CEO of Heartbeat.ai — Factual, recruiter-centered; practical evaluation.

Who this is for

Recruiters considering ZoomInfo who need reliable physician mobiles/emails. If you’re trying to move a physician from “seen your email” to “picked up the phone,” this is about workflow fit: placement speed, connectability, deliverability, and wrong-person risk.

This is also for teams that keep losing time to front-desk gatekeepers, clinic-hour call windows, and “same name, different doctor” mix-ups.

Quick Answer

Core Answer

ZoomInfo for physicians can help with broad B2B context, but physician outreach usually needs clinician identity keys, verification, and suppression to reduce wrong-person contact.

Key Insight

Physician records should resolve to the individual clinician using NPI and license matching, then be validated with line tested signals and opt-out enforcement.

Best For

Recruiters considering ZoomInfo who need reliable physician mobiles/emails.

Compliance & Safety

This method is for legitimate recruiting outreach only. Always respect candidate privacy, opt-out requests, and local data laws. Heartbeat does not provide medical advice or legal counsel.

• Identity anchor: store NPI and use license matching to prevent same-name collisions.
• Channel validation: treat phone and email as separate tests with separate pass/fail criteria.
• Verification: prioritize contacts with recent validation signals (including line tested where available).
• Suppression: enforce opt-out across sequences, exports, and recruiters at the clinician identity level.

TL;DR decision: If you need organization context (who owns the practice, who runs the group, how the org is structured), a broad B2B source can help. If you need clinician-direct outreach, prioritize NPI anchoring, verification, and suppression so you reach the right physician without burning the channel.

Myth-bust: “If it’s a big B2B database, it must be good for physicians.” In practice, physician outreach fails when the record is for the organization (practice, hospital department, billing entity) rather than the clinician. That mismatch shows up as low connects, wrong-person replies, and compliance headaches.

Framework: The “B2B vs Clinician” Filter: Who is the record actually for

Before you compare tools, run every data source through one filter: is the record anchored to the clinician, or to the business? ZoomInfo is widely known as a broad B2B dataset. That can be useful for employer mapping, org context, and business relationships. Physician recruiting is a different contact problem: you need to reach a licensed individual with a stable identity trail.

• B2B record: often anchored to a company domain, office location, or role at an organization. Great when the company is the target.
• Clinician record: anchored to a clinician identity key (e.g., NPI) and licensure footprint, then mapped to current practice sites and contact channels.

The trade-off is… broad coverage and general business context vs. clinician-specific identity resolution and verification that helps reduce wrong-person outreach.

Decision guide: when ZoomInfo is enough vs when you need clinician-first data

• ZoomInfo can be enough if your motion is employer mapping, practice ownership research, or finding non-clinical decision-makers tied to a healthcare organization.
• You likely need clinician-first data if your KPI is physician-level connects and replies, especially across private practices, multi-site groups, and common surnames.
• Clinician-first is non-negotiable when you must anchor outreach to NPI and license matching, enforce opt-out at the clinician identity level, and reduce wrong-person risk.

Required metric definitions (so your pilot isn’t hand-wavy)

• Identity resolution: the process of matching multiple records and signals to the same real-world person (here: a physician) using stable identifiers (e.g., NPI) and corroborating attributes (name, specialty, address history, licensure).
• Connect Rate: connected calls / total dials (per 100 dials).
• Answer Rate: human answers / connected calls (per 100 connected calls).
• Deliverability Rate: delivered emails / sent emails (per 100 sent emails).
• Bounce Rate: bounced emails / sent emails (per 100 sent emails).
• Reply Rate: replies / delivered emails (per 100 delivered emails).

Step-by-step method

This is the fastest way I’ve seen teams evaluate ZoomInfo for physicians without getting trapped in demos, vanity coverage, or spreadsheet theater. You’re building a small, controlled pilot that answers one question: Can we reliably reach the right physician, quickly, without burning the channel?

Step 1: Define the outreach unit (physician-first, not facility-first)

Pick one specialty and one geography you actively recruit. Build a list of 50–150 target physicians from your ATS, prior searches, or a trusted clinician directory. The key is that each row is a physician with an NPI (or a path to it), not a practice location.

If you don’t already have NPI on your records, add it via your normal enrichment process. If you want a healthcare-native approach, see NPI and license matching for provider contact data.

Step 2: Run the “record-for-who” check (clinician vs organization)

For each physician, capture what the tool returns as the primary record anchor:

• Is the record clearly tied to the individual physician (name + specialty + NPI/licensure signals)?
• Or is it tied to a practice entity (front desk, generic office line, shared inbox, corporate domain)?

In physician recruiting, the failure mode is predictable: you get a lot of reachable numbers/emails that reach the wrong person (office manager, scheduler, billing, or a different clinician with a similar name).

Step 3: Validate channels separately (phone vs email)

Don’t blend phone and email into one “contact found” metric. Track them separately because they fail differently.

• Phone: you care about direct-to-physician probability during realistic call windows (early morning, lunch, after clinic). Track Connect Rate per 100 dials and Answer Rate per 100 connected calls.
• Email: you care about deliverability and whether replies are from the physician vs staff. Track Deliverability Rate per 100 sent emails, Bounce Rate per 100 sent emails, and Reply Rate per 100 delivered emails.

Heartbeat.ai is healthcare-only with identity keys + verification, designed to help reduce wrong-person risk. In phone-first workflows, we support teams by ranked mobile numbers by answer probability so recruiters prioritize the dials most likely to reach the physician.

For how verification is handled, see data quality verification methods.

Step 4: Add suppression and opt-out handling before you send anything

Physician outreach is high-sensitivity. Build suppression rules up front:

• Respect opt-out requests immediately and globally across campaigns.
• Suppress role-based inboxes (e.g., info@, billing@) unless your workflow explicitly needs them.
• Suppress numbers that repeatedly connect to front desks when your goal is clinician-direct.

Also document your consent posture for outreach (what you rely on, how you honor opt-outs, and how you handle data subject requests). If you don’t have this written down, your team will improvise under pressure.

Step 5: Run a 5-day pilot with a fixed cadence

Keep the pilot tight so you can compare sources fairly:

1. Day 1: email 1 (short, role-based value proposition) + call attempt 1
2. Day 2: call attempt 2 (different time window)
3. Day 3: email 2 (one-line follow-up) + call attempt 3
4. Day 5: final call attempt + close-the-loop email

Log outcomes at the physician level: connected, human answer, wrong person, voicemail, gatekeeper, bounced email, reply from staff, reply from physician, opt-out.

Step 6: Decide using workflow impact, not “coverage”

At the end, you should be able to answer:

• How often did you reach the physician vs the office?
• How much recruiter time was spent cleaning, re-verifying, or chasing wrong contacts?
• Did the data source fit your ATS/CRM workflow without creating a manual mess?

Pilot success criteria (qualitative, no guesswork)

• Wrong-person outcomes trend down as you tighten NPI anchoring and license matching.
• Opt-outs are honored cleanly across sequences, exports, and recruiters (no repeat contacts after suppression).
• Email health stays stable (no sudden spike in bounces; replies are increasingly physician-direct vs staff-only).
• Recruiter time-to-first physician conversation improves because you’re dialing fewer dead ends and gatekeeper loops.

Diagnostic Table:

Use this compact table to compare “broad B2B” vs “clinician-first” data for physician recruiting. This implements the COMPARISON_TABLE uniqueness hook: it forces a decision on record anchoring and verification, not just “does it return a phone number.”

If you want to see how Heartbeat.ai positions healthcare-only identity keys and verification, review how our data is built for healthcare recruiting.

Weighted Checklist:

Score any tool (including ZoomInfo) against what actually moves physician recruiting forward. Use a 1–5 score per line, multiply by weight, and total it. This keeps the decision tied to placement speed and wrong-person risk.

Outreach Templates:

These are built for physician realities: short, respectful, and easy to forward. Customize the bracketed fields. Keep it compliant: honor opt-outs and don’t imply a relationship you don’t have.

Template 1: First email (physician-direct)

Subject: [Specialty] role near [City] — quick question

Hi Dr. [Last Name] — I recruit [Specialty] physicians. Are you open to hearing about a [perm/locums] role with [Key detail: schedule/call] near [City]?

If not you, who’s best to contact for your future plans? If you’d prefer I don’t reach out again, reply “opt out” and I’ll suppress you.

— [Your Name]

Template 2: Voicemail (10–15 seconds)

Hi Dr. [Last Name], this is [Name] recruiting [Specialty]. I’m calling about a [role type] opportunity near [City]. If you’re open to a quick chat, call me at [number]. If not, tell me and I’ll opt you out. Thanks.

Template 3: Gatekeeper-safe ask (front desk answers)

Hi — I’m trying to reach Dr. [Last Name] directly about a physician opportunity. What’s the best way to get a message to them, and is there a preferred time window?

If you can’t share direct contact, can you confirm whether email or voicemail is better for Dr. [Last Name]?

Template 4: Follow-up email (one line)

Dr. [Last Name] — circling back. Should I send details on the [Specialty] role near [City], or would you prefer I close this out?

Common pitfalls

• Counting “contacts found” instead of “physician reached.” A front desk number is still a number, but it doesn’t move submittals.
• Mixing business records with clinician records. If the record is for the practice entity, you’ll get staff replies and low-quality connects.
• Not separating phone and email performance. One channel can look fine while the other burns your domain or wastes dials.
• Ignoring identity resolution. Same-name physicians are common; without NPI and license matching you’ll contact the wrong person.
• Weak suppression discipline. If opt-outs aren’t enforced across recruiters and exports, you’ll re-contact people who asked you not to.

How to improve results

If your pilot shows “some data, inconsistent outcomes,” don’t guess. Tighten the system in this order: identity anchoring, verification, then cadence.

1) Anchor every record to NPI + license matching

Make NPI the spine of your physician record. Then use license matching to confirm you’re dealing with the right clinician across states and name variants. This reduces wrong-person outreach and makes your suppression list durable.

Operationally: store NPI, license state(s), and a “last verified” timestamp in your ATS/CRM. If you need a walkthrough, start with NPI and license matching.

ATS/CRM fields to store (so your data stays usable)

• NPI (primary identity key)
• License state(s) and license status (for matching and disambiguation)
• Specialty (as used in your searches)
• Practice site(s) (current and recent)
• Phone fields: number, type (mobile/office/unknown), and any line tested/last-validated note
• Email fields: address, source, and last deliverability check date
• Suppression: opt-out flag, opt-out date, and scope (global vs campaign)
• Source attribution: where the contact came from and when it was pulled

2) Instrument the pilot so it’s comparable across sources

Measure this by… tracking outcomes per 100 attempts, not vibes, and keeping the cohort and cadence fixed:

• Use the same physician cohort across sources.
• Use the same email copy and call script across sources.
• Keep send times consistent (don’t change windows mid-test).
• Log outcomes at the physician level (physician reached vs staff vs wrong person).

Track these metrics with denominators:

• Connect Rate = connected calls / total dials (per 100 dials).
• Answer Rate = human answers / connected calls (per 100 connected calls).
• Deliverability Rate = delivered emails / sent emails (per 100 sent emails).
• Bounce Rate = bounced emails / sent emails (per 100 sent emails).
• Reply Rate = replies / delivered emails (per 100 delivered emails).

3) Treat verification and suppression as first-class workflow steps

Verification can improve Connect Rate when it reduces wrong numbers and wrong-person contacts. In a healthcare-only workflow, you want validation signals like line tested phone and recent confirmation, plus suppression that follows the physician identity (NPI) rather than a single email address.

For how Heartbeat.ai approaches verification and quality controls, see data quality verification.

4) Tighten your “time math” without inventing numbers

Here’s the time math that matters, without pretending we know your exact rates: if a recruiter spends time dialing numbers that repeatedly reach gatekeepers or wrong people, that time can’t go to candidate conversations, submittals, and offer closes. Your pilot should quantify:

• Average minutes spent per physician to reach a real decision-maker (the physician)
• Number of attempts before a physician-level connect
• Time spent cleaning/exporting/reconciling records in ATS

When you compare sources, the winner is the one that reduces wasted attempts and wrong-person loops while keeping compliance clean.

Legal and ethical use

Use provider contact data for legitimate recruiting outreach only. Build a documented process for:

• Consent: what your outreach relies on and how you communicate purpose.
• Opt-out: immediate suppression across all recruiters, sequences, and exports.
• Data minimization: store only what you need for recruiting workflow.
• Auditability: be able to show when a record was sourced/verified and when an opt-out was applied.

Heartbeat.ai does not provide legal counsel; if you operate across jurisdictions, have your counsel review your outreach and data handling policies.

Evidence and trust notes

Vendor positioning for ZoomInfo is referenced from its official site: https://www.zoominfo.com/. For how Heartbeat evaluates and communicates data trust, methodology, and verification concepts, see Heartbeat trust methodology.

For general definitions and operational guidance on email deliverability and bounces, see: https://mailchimp.com/resources/email-deliverability/ and https://mailchimp.com/resources/hard-bounce-vs-soft-bounce/.

To compare healthcare-only provider contact approaches, you may also want: physician contact database guide.

FAQs

Is ZoomInfo for physicians a fit for physician recruiting?

It can be, depending on whether the records you pull are anchored to the physician (not just the practice) and whether you can validate channels and enforce opt-outs. Pilot it with clinician-level outcomes.

What should I test first when evaluating physician contact data?

Start with identity anchoring (NPI + license matching), then phone connectability (Connect Rate per 100 dials and Answer Rate per 100 connected calls), then email deliverability (Deliverability Rate and Bounce Rate per 100 sent emails). Track wrong-person outcomes explicitly.

How do I reduce wrong-person outreach to physicians?

Use identity resolution anchored to NPI, corroborate with licensure and specialty/location, and maintain suppression at the physician identity level. Don’t rely on name-only matching.

What does “line tested” mean in a recruiting workflow?

It’s a validation signal that a phone line was tested for reachability. In practice, you still need to measure physician-level connects and tag staff/wrong-person outcomes.

Can I “start free search & preview data” and still run a real evaluation?

Yes—use the preview to build a small cohort, then run the same outreach cadence and measurement plan across sources. The goal is not volume; it’s physician-level reach and clean suppression.

Next steps

• If you want a healthcare-only approach with identity keys and verification, review Heartbeat.ai data and how it’s built for clinician outreach.
• Run the pilot using the Diagnostic Table and Weighted Checklist above, then compare outcomes at the physician level.
• When you’re ready to test in your workflow, create an account to start a free search & preview data in Heartbeat.

Reminder: static lists decay fast. Operationally, aim for Access + Refresh + Verification + Suppression.

About the Author

Ben Argeband is the Founder and CEO of Swordfish.ai and Heartbeat.ai. With deep expertise in data and SaaS, he has built two successful platforms trusted by over 50,000 sales and recruitment professionals. Ben’s mission is to help teams find direct contact information for hard-to-reach professionals and decision-makers, providing the shortest route to their next win. Connect with Ben on LinkedIn.