Ben Argeband, Founder & CEO of Heartbeat.ai — Operational: what’s inside, how to use it, how to measure outcomes.
What’s on this page:
Who this is for
This is for physician recruiters and locums teams who build outreach lists and need faster connectability without burning time on wrong numbers, dead inboxes, or duplicate identities.
If you’re considering buying a static list, I get why. Buying static lists is risky because of decay. The modern standard is access + refresh + verification + suppression.
Myth bust: More records does not mean more conversations. Fresh verification and clean suppression usually beat a giant export.
Scope note: This page is the evaluation + measurement layer. If you only need channel execution, use the phone workflow page or the email workflow page linked in Next steps.
Quick Answer
- Core Answer
- A physician contact database is useful when it’s NPI-anchored, contact fields are verified (line tested for phone, validated for email, suppressed after opt-out/bounce), and performance is tracked by connect and deliverability—not record count.
- Key Insight
- NPI is stable identity; phone and email are volatile fields. Refresh cadence and suppression discipline usually matter more than how many records you can export.
- Best For
- Physician recruiters + locums teams building outreach lists.
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.
Framework: The “Useful Database” Test: Coverage × Connectability × Refresh
Recruiting teams don’t lose searches because they lacked “data.” They lose because the data doesn’t connect, doesn’t refresh, or doesn’t map to the right person.
- Coverage: Can you reliably find the physicians you recruit (specialty, geography, setting) with a stable identity anchor?
- Connectability: When you dial or email, do you reach a human physician (or at least a valid inbox) at a rate that supports your workflow?
- Refresh: How quickly does the dataset correct itself when physicians change groups, rotate numbers, or switch inboxes?
Practical data model (how to think about it): Start with NPI as identity, then apply license matching to keep state credentials aligned, then attach practice context (specialty, location), then attach volatile contact fields (phone/email), then close the loop with refresh + suppression so bad outcomes don’t repeat.
Healthcare-specific datasets tend to reduce false positives because they’re built around healthcare identity (NPI) and practice context (specialty, location, credential signals). General B2B datasets can look big, but they often break when you need the right clinician at the right location.
If you want the product-level view of what’s included, see what’s inside Heartbeat.ai data coverage.
Diagnostic Table:
Use this to evaluate any physician contact database (including your current stack). It’s designed for recruiting operations: speed-to-submittal, connectability, and workflow fit.
| What you’re evaluating | What “good” looks like | How to test it fast | Why it matters |
|---|---|---|---|
| Identity anchor | Every record ties to NPI and supports license matching | Pick 25 known physicians; confirm NPI alignment and license state | NPI prevents wrong-person outreach and duplicate identities |
| Phone quality | Numbers are line tested and tagged by recency | Run a 100-dial pilot; track connected calls and wrong-party outcomes | Bad numbers waste call blocks and slow submittals |
| Email quality | Emails are validated (format/domain checks and bounce suppression); opt-outs persist | Send a small pilot; track delivered vs bounced; confirm suppression | Protects domain reputation and keeps outreach scalable |
| Refresh cadence | Refresh cadence is explicit and prioritized over record count | Ask: “How often do you re-verify phone/email and update practice moves?” | Decay is the hidden cost; refresh beats “big export” marketing |
| Workflow fit | Exports/CRM sync; recruiter notes; suppression writeback | Export a 50-record cohort; confirm you can write back outcomes and opt-outs | Prevents rework and keeps suppression consistent across the team |
| Compliance controls | Clear consent posture, opt-out workflow, and suppression lists | Request documentation of opt-out + suppression handling | Prevents repeat-contact mistakes and reduces risk |
Definitions for evaluation: Line tested means the number has been checked for validity as a working line (not just “present in a record”). Recency means you can see when a contact field was last verified or observed as active.
Healthcare-specific vs general B2B: side-by-side buyer table
| Buyer question | Healthcare-specific dataset (what to expect) | General B2B dataset (common failure mode) |
|---|---|---|
| How do you prevent wrong-person matches? | NPI-anchored identity; better de-duplication across locations | Name/company matching creates false positives and merged identities |
| Can I segment by clinical reality? | Specialty + practice context is usually more consistent | Titles/roles often misclassify clinicians or mix admin roles |
| How do you handle practice moves? | Updates tied to provider identity and practice signals | Company-based updates can lag or attach to the wrong record |
| What reduces wasted outreach? | Line testing + recency + suppression reduces dead attempts | Large exports with stale fields increase wrong numbers and bounces |
| What should I optimize for? | Refresh cadence and connectability metrics | Record count and “coverage” claims that don’t translate to connects |
Buyer scorecard note: Require reporting on connect rate per 100 dials and deliverability per 100 sends for your pilot cohort. Don’t accept a generic “accuracy” claim without definitions.
Step-by-step method
Step 1: Build your target cohort using stable identity first
Start with identity, not contact fields. In healthcare, the stable anchor is NPI. Use NPI to de-duplicate and to keep outreach tied to the correct physician across locations.
- Define the cohort: specialty, geography, setting, and must-have constraints.
- Attach NPI and run license matching where state alignment matters.
- Only then evaluate phone/email fields for outreach.
Step 2: Treat phone and email as volatile fields (plan for decay)
NPI is stable; phone/email are volatile. Physicians change groups, rotate coverage, and switch inboxes. Your workflow should assume decay and rely on verification + refresh.
- Prefer phone data that is line tested and tagged by recency.
- Prefer email data with validation, bounce handling, and suppression.
- Ensure opt-outs persist across recruiters and campaigns.
Heartbeat.ai includes ranked mobile numbers by answer probability so recruiters can start with the most likely-to-connect option when time is tight.
Step 3: Run a pilot that mirrors your real recruiting workflow
Don’t pilot on easy segments. Pilot on the cohort you actually struggle to reach.
- Pull 100 physicians you would genuinely recruit this month.
- Have one recruiter run a consistent call/email sequence for 3–5 business days.
- Track outcomes using the metric definitions below so you can compare apples-to-apples.
The trade-off is… a smaller, fresher cohort with verified fields usually beats a massive stale export because it reduces wasted attempts and speeds up first conversations.
Step 4: Use canonical metric definitions (so your reporting is real)
Use these definitions consistently across vendors, tools, and recruiters:
- Connect Rate = connected calls / total dials (report per 100 dials).
- Answer Rate = human answers / connected calls (report per 100 connected calls).
- Deliverability Rate = delivered emails / sent emails (report per 100 sent emails).
- Bounce Rate = bounced emails / sent emails (report per 100 sent emails).
- Reply Rate = replies / delivered emails (report per 100 delivered emails).
Mobile accuracy (definition): the share of first-listed mobile numbers that are valid and reach the intended physician when dialed, measured on a defined sample and time window.
Email accuracy (definition): the share of emails that are valid for the intended physician and result in delivery (not bounce) when sent under normal sending practices, measured on a defined sample and time window.
Deliverability (definition): the share of sent emails that are delivered (not bounced). Delivered does not guarantee inbox placement.
Step 5: Operationalize suppression and opt-out as a system
Opt-out cannot be a “campaign setting.” It must be enforced across your entire team and toolchain.
Ops requirement: Your CRM/ATS should store opt-out and suppression outcomes at the physician identity level (NPI-linked where possible) so a new recruiter doesn’t restart outreach on a previously suppressed record.
- Centralize opt-out and suppression lists.
- Write back outcomes (wrong number, best call window, gatekeeper notes) to improve future attempts.
- Segment by setting so you don’t apply the same cadence to every physician.
Weighted Checklist:
Score each item 0–5, multiply by weight, and compare totals across options (including “do nothing”). This is built for recruiting ops decisions.
| Category | What to look for | Weight | Score (0–5) | Weighted score |
|---|---|---|---|---|
| Identity integrity | NPI anchored; supports license matching; clear de-duplication rules | 5 | ||
| Phone connectability | Line tested; recency visible; wrong-party outcomes tracked | 5 | ||
| Email hygiene | Validation + suppression; bounce handling; sending guidance exists | 4 | ||
| Refresh cadence | Explicit refresh cadence; updates frequent enough for recruiting cycles | 5 | ||
| Workflow fit | Exports/CRM sync; recruiter notes; team-level opt-out enforcement | 4 | ||
| Compliance posture | Consent posture documented; opt-out honored; audit trail available | 4 | ||
| Healthcare specificity | Built around physician identity + practice context (not generic B2B) | 4 |
Outreach Templates:
Short, respectful, and easy to opt out. Personalize with specialty + location + role specifics. Use only where appropriate and honor opt-out immediately.
Template 1: First email
Subject: Quick question about [Specialty] coverage in [Region]
Hi Dr. [Last Name] — I’m [Your Name]. I recruit physicians for [Facility/Group Type] in [Region]. Are you open to a brief conversation about a [role type] need?
If you’re not interested, reply “opt out” and I’ll stop.
— [Your Name]
Template 2: Voicemail (15–20 seconds)
Hi Dr. [Last Name], this is [Name]. I’m recruiting for a [Specialty] role in [Region]. If you’re open to a quick call, my number is [Callback]. If not, tell me and I’ll close the loop. Thanks.
Template 3: SMS (only where appropriate)
Dr. [Last Name] — [Name] recruiting [Specialty] in [Region]. Open to a 5-min call this week? Reply STOP to opt out.
Template 4: Gatekeeper-friendly opener
Hi, I’m trying to reach Dr. [Last Name] regarding a [Specialty] opportunity in [Region]. What’s the best way or time to connect, or is there a preferred contact route?
Common pitfalls
Pitfall 1: Optimizing for record count instead of outcomes
Record count is not an operating metric. If your team can’t show connect rate per 100 dials and deliverability per 100 sends for the last cohort, you can’t manage performance.
Pitfall 2: Confusing “delivered” with “inbox”
Deliverability rate is delivered emails per 100 sends. Inbox placement is separate and depends on sender reputation and sending practices. Monitor bounces and complaints so your future campaigns don’t get harder.
Pitfall 3: Identity drift (wrong person, wrong location)
If you don’t anchor to NPI, you’ll merge the wrong people, duplicate the same physician across locations, or mis-assign specialties. That creates false positives and damages trust fast.
Pitfall 4: Treating opt-out as optional
Opt-out must persist across recruiters and tools. If a physician opts out and gets contacted again, you’ve created a preventable compliance and brand problem.
Pitfall 5: Accepting “accuracy” claims without definitions
Require definitions (mobile accuracy, email accuracy, deliverability) and run your own pilot with your own denominators. If a vendor won’t define terms, you can’t compare them.
How to improve results
1) What you get vs what you don’t (so expectations are correct)
- You get: a way to find the right physician identity (NPI), attach contact fields, and run measurable outreach.
- You don’t get: guaranteed reachability, guaranteed inbox placement, or a permanent list that stays accurate without refresh.
2) Sequence channels to reduce attempts per conversation
Prioritize the contact path most likely to connect quickly (often mobile first when appropriate, then email, then office line). Your goal is fewer attempts per booked conversation.
3) Use the BENCHMARK_TABLE worksheet (uniqueness hook)
This worksheet is designed for weekly cohort comparisons so you can see whether refresh and verification are improving outcomes in your real segment.
| Cohort (specialty + region) | Time window | Total dials | Connected calls | Connect rate (per 100 dials) | Human answers | Answer rate (per 100 connected calls) | Sent emails | Delivered emails | Deliverability (per 100 sends) | Bounced emails | Bounce rate (per 100 sends) | Replies | Reply rate (per 100 delivered emails) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| [e.g., Hospitalist, Midwest] | [Week of ____] | ||||||||||||
| [Same cohort] | [Week of ____] |
4) Measurement instructions (required)
Measure this by… running a weekly report on the last 100 dials and last 200 emails for the same cohort (same specialty + region), using the canonical 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)
5) Pilot acceptance criteria (no guesswork, no invented targets)
- Keep: wrong-party outcomes trend down week-over-week, suppression is enforced, and recruiters report fewer wasted attempts per conversation.
- Investigate: connect rate is flat but answer rate drops (often call timing, gatekeepers, or sequencing).
- Fix operations: deliverability is unstable or bounce rate spikes (often sending setup and suppression hygiene).
- Switch: identity drift persists (wrong person/location) even after NPI anchoring and de-duplication checks.
Then tie these to recruiter outcomes you already track: conversations booked, CVs received, submittals, and starts. If connect rate improves but starts don’t, your issue is likely targeting, offer, or follow-up—not the database.
6) First-party benchmarks (only if you can timestamp them)
If you maintain internal benchmarks, label them as Heartbeat observed typicals with a specific time period (for example, “Qx YYYY”) and cohort definition. Do not treat benchmarks as guarantees.
7) Tighten deliverability operations so valid emails can perform
Even valid emails won’t perform if your sending setup is sloppy. Monitor bounces, complaints, and domain reputation, and keep suppression lists clean.
Legal and ethical use
Recruiting outreach should be legitimate, respectful, and auditable:
- Use contact data for legitimate recruiting outreach only.
- Honor opt-out requests immediately and permanently via suppression.
- Minimize access to the recruiters who need it; log usage where possible.
- Be transparent in messaging: who you are, why you’re reaching out, and how to stop.
- Follow applicable local data laws and your organization’s policies. Heartbeat.ai does not provide legal counsel.
Evidence and trust notes
We anchor provider identity to NPI because it’s the standard identifier system for healthcare providers and is maintained through official channels. Using NPI as the baseline identity reduces wrong-person matching and supports consistent de-duplication across locations.
For deliverability fundamentals and monitoring references:
For how Heartbeat.ai evaluates data quality and communicates trust, see our trust methodology for sourcing and verification.
For definitions and practical implications, see what contact data accuracy means in recruiting operations.
FAQs
What should a physician contact database include for recruiting?
At minimum: NPI-anchored identity, specialty and location context, phone and email fields with verification/recency signals, and compliance controls like opt-out and suppression.
How do I compare two databases without getting fooled by record count?
Run the same pilot cohort through both and compare connect rate per 100 dials and deliverability per 100 sends using the canonical definitions. Refresh cadence and suppression discipline usually explain the difference.
What metrics should my team track weekly?
Connect rate (connected calls / total dials), answer rate (human answers / connected calls), deliverability rate (delivered / sent), bounce rate (bounced / sent), and reply rate (replies / delivered). Report each per 100 using the denominators.
How should opt-out and suppression work across a recruiting team?
Opt-out should be stored centrally and enforced across recruiters, campaigns, and tools. Suppression should include opt-outs and known bad outcomes (like repeated bounces) so the team doesn’t re-contact the same physician after a stop request.
How do I reduce false positives in outreach?
Anchor identity to NPI, de-duplicate across locations, and require practice context fields that match clinical reality. Then validate contact fields and enforce suppression so you don’t repeat mistakes.
How do I start testing Heartbeat.ai?
Use a real cohort you’d recruit this month, run a small pilot, and track connect and deliverability outcomes with the worksheet above.
Next steps
- Phone-first workflow: physician phone number lookup for recruiters.
- Email workflow: how to use physician email responsibly for recruiting.
- Identity cleanup: NPI and license matching.
- Product coverage: what’s inside Heartbeat.ai data.
If you want to validate connectability in your market, start free search & preview data and run the pilot with the benchmark worksheet.
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.
