How to build a physician call list

By Ben Argeband, Founder & CEO of Heartbeat.ai — Simple: “columns, order, what to do when it fails.”

Who this is for

Recruiters and ops building physician call lists who are tired of dead numbers, duplicates, and wrong-person calls. If you care about speed-to-submittal, connectability, and clean tracking, this is the workflow.

Quick Answer

Core Answer

To build a physician call list, standardize a CSV, dedupe by NPI/license, validate phones, route by line type, then track call blocks and refresh after no-answer clusters (same source + same phone_validation_date cohort producing repeated no_answer outcomes).

Key Statistic

Heartbeat observed typicals (internal): Connect Rate varies widely by line type and list hygiene; use consistent call blocks to benchmark your own baseline and improve it.

Best For

Recruiters and ops building physician call lists tired of dead numbers/duplicates.

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 Call List Build System: Dedupe → Validate → Rank → Route → Track

A physician call list isn’t a database export. It’s a working queue designed to produce connects and qualified conversations without burning goodwill.

• Dedupe: collapse the same physician across sources using stable identifiers (NPI/license) so you don’t call the same person twice.
• Validate: run phone validation so you stop feeding disconnected/invalid numbers into your dial blocks.
• Rank: prioritize the best numbers first. Heartbeat.ai supports ranked mobile numbers by answer probability.
• Route: decide what to dial first based on line type (mobile vs direct dial vs office line) and your compliance posture.
• Track: log outcomes in a call block tracker so you can refresh intelligently after no-answer clusters.

TL;DR workflow

• Start with a strict CSV header (below) so every row is dial-ready.
• Dedupe by NPI/license, not by name.
• Validate phones, then route by line type.
• Dial in call blocks, then refresh the slices that decay.

Step-by-step method

Step 1) Define the list’s job (so “done” is measurable)

Before you build anything, define what the list is supposed to do:

• Use case: first-touch sourcing, reactivation, credentialing follow-up, or interview scheduling.
• Owner: who updates outcomes (recruiter) vs who maintains hygiene (ops).
• Cadence rule: how many attempts per number before you pause and refresh validation or switch numbers.

Call list quality beats size: reducing duplicates and wrong-person calls usually improves throughput faster than adding more rows.

Step 2) Start from a strict CSV template (so ops can enforce hygiene)

Use one CSV template across the team. If you don’t, you can’t dedupe, route, or measure consistently.

Copy/paste CSV header line (recommended order)

physician_full_name,npi,license_state,license_number,specialty,practice_name,phone_number,line_type,phone_validation_status,phone_validation_date,source,priority_score,owner,call_block_id,attempt_count,last_call_outcome,last_call_date,opt_out_status,notes

CSV header reference table

If you want a faster import path, use upload a CSV to map physician contacts and keep the same header going forward.

Step 3) Dedupe using stable identifiers (NPI/license), not names

Names collide and practices change. Your dedupe keys should be stable:

• Primary key: NPI (best for collapsing multiple sources into one physician record).
• Secondary key: license_state + license_number (useful when NPI is missing or inconsistent).

Operationally: create a physician_id in your system that is either the NPI or a deterministic ID built from license_state+license_number when NPI is absent. If you need help matching identifiers, see NPI and license matching for dedupe.

• Precedence rule: when NPI is present and passes your checks, treat NPI as the primary identity key.
• Conflict rule: if NPI and license disagree for the same physician_full_name, quarantine the row for review and do not dial until resolved.

The trade-off is… strict dedupe will reduce your row count, but it increases recruiter trust because you stop double-tapping the same physician across different sources and numbers.

Step 4) Validate phones before they hit a dial block

Phone numbers are volatile. If you don’t validate, you’ll waste blocks on disconnected/invalid numbers and inflate no_answer noise. Run phone validation and store both status and date so you can enforce recency.

• Gate rule: only dial numbers with phone_validation_status = valid (or explicitly allow unknown for a controlled test block).
• Refresh rule: if a block shows clustered no_answer outcomes, refresh validation before you keep retrying the same stale slice.

Implementation detail: validation is a workflow step, not a one-time enrichment. See phone validation for provider direct dials.

Step 5) Route by line type (so you don’t burn time on the wrong path)

Not all numbers behave the same. Your list needs a line type field and a routing rule. If you mix office lines with mobile in the same workflow, your connect expectations and scripts will drift.

Align definitions using office line vs direct dial vs mobile.

• Handling unknown: route unknown line_type into a separate test call block until it’s classified.

Step 6) Rank the queue so the first hour produces the most connects

Ranking is where speed-to-submittal shows up. Don’t randomize. Rank by:

• line_type (mobile → direct_dial → office → unknown)
• validation recency (newer first)
• prior outcomes (connected > voicemail > no_answer)
• time-of-day fit (office lines during office hours; keep mobile attempts tight and respectful)

Step 7) Track outcomes with a call block tracker (so ops can fix the list)

You need a batch concept so you can diagnose list issues quickly. That’s what a call block is.

Metric definitions (use these consistently)

• Connect Rate = connected calls / total dials (report per 100 dials).
• Call block = a defined batch of dials (a time window + a specific list slice) used to track outcomes and compare performance across sources, line types, and validation recency.

Minimum tracking fields per dial attempt:

• call_block_id
• dial_timestamp
• phone_number
• line_type
• outcome (connected / voicemail / no_answer / wrong_person / gatekeeper / do_not_contact)
• next_action (retry, switch number, refresh validation, suppress)

Diagnostic Table:

Weighted Checklist:

Use this to decide whether a list is dial-ready. Score each item 0–2 (0 = missing, 1 = partial, 2 = solid). Multiply by weight, then total.

Outreach Templates:

Phone-first openers designed for physician recruiting. Keep them short, confirm identity, and offer an easy opt-out.

Template 1: Mobile first-touch (identity + permission)

Opener: “Hi Dr. [Last Name]—this is [Name]. Quick check: did I catch you at an okay time for a 20-second question?”

If yes: “I recruit physicians for [Organization/Client]. Are you open to hearing about roles, or would you prefer I don’t reach out again?”

If no: “No problem—what’s a better time, or should I email instead?”

Template 2: Office line (gatekeeper-respectful)

“Hi—can you help me reach Dr. [Last Name]? I’m calling about a professional opportunity. What’s the best direct path for a brief message—direct line, voicemail, or email?”

Template 3: Voicemail (low detail, clear callback + opt-out)

“Dr. [Last Name], this is [Name] at [Company]. I’m calling about a physician opportunity. If you’d like details, call me at [Number]. If you prefer I don’t contact you again, tell me and I’ll opt you out.”

Common pitfalls

1) Treating “more rows” as progress

Big lists feel productive until your team spends the week dialing duplicates and wrong-person numbers. Quality beats size: dedupe and validation usually move outcomes faster than adding volume.

2) No stable dedupe key

If you dedupe on name + city, you will double-call physicians and miss merges. Use NPI/license matching as the backbone, then keep names as display fields.

3) Mixing line types in the same block without routing rules

Office lines and mobile behave differently. If you blend them, your Connect Rate per 100 dials becomes hard to interpret, and recruiters lose trust in the queue.

4) Not refreshing after “no-answer clusters”

When a slice of the list produces repeated no_answer outcomes, don’t just increase attempts. Refresh validation and/or swap to a different number for that physician. Phone numbers decay; your process has to assume it.

5) CSV_TEMPLATE mistake (uniqueness hook)

Common mistake I see: teams build a CSV where each row is “a phone number,” not “a physician contact attempt.” Then they can’t dedupe, can’t suppress correctly, and can’t route by line type. Fix it by making the CSV physician-centric (NPI/license + physician_id) and treating phone_number as an attribute with validation and line type.

How to improve results

Build a measurement loop ops can act on

Measure this by… running weekly call blocks that are intentionally comparable (same time window, same routing rules), then splitting results by source, line_type, and validation recency.

• Connect Rate = connected calls / total dials (report per 100 dials).
• Connects per hour (formula) = (dials per hour) × (Connect Rate). Use your own observed dials/hour and your own Connect Rate from comparable call blocks.

We don’t publish a single universal percentage here because your baseline depends on mix (line type, specialty, time window, and list hygiene). The point is consistency: comparable call blocks make improvements obvious.

Refresh triggers (instead of fixed schedules)

• If a source slice’s Connect Rate drops across two comparable call blocks, re-validate that slice.
• If wrong_person rises, audit dedupe keys and mapping rules (NPI/license alignment).
• If gatekeeper outcomes dominate, separate office lines into their own call blocks and adjust scripts.

Suppression and consent handling

Make opt-out and do_not_contact outcomes irreversible in the workflow. Your list should suppress across all sources and future imports. Track consent signals where applicable, and don’t “re-add” suppressed contacts because a new source file appears.

Make it easy to start and hard to break

• Lock required columns in your CSV template.
• Reject imports missing phone_number or opt_out_status; quarantine imports missing NPI/license or phone_validation fields for cleanup before dialing.
• Keep a single owner for list hygiene (ops) and a single owner for outcomes (recruiting).

If you want to move fast, you can start free search & preview data (access + refresh + verification + suppression, not a static list) and then export into the template once your routing fields are in place.

Legal and ethical use

This playbook is about legitimate recruiting outreach. Build your process around respect and suppression:

• Honor opt-out requests immediately and globally (across all lists and sources).
• Be transparent about who you are and why you’re calling.
• Keep notes professional; avoid storing sensitive personal data.
• Follow applicable phone/SMS rules and local data laws. For baseline U.S. guidance, review the TCPA overview from the FCC: Telephone Consumer Protection Act (TCPA).

Heartbeat.ai does not provide legal counsel; if you have edge cases (multi-state outreach, texting policies, consent language), get your counsel to bless the workflow.

Evidence and trust notes

This guide is designed to be auditable: you can trace each row to a source, see validation recency, and compare outcomes by call block. For how Heartbeat approaches data quality, verification, and suppression, see our Trust Methodology.

Related implementation resource: CSV import template for physician contacts.

External baseline reference used for compliance context: FCC TCPA overview.

FAQs

What columns do I need to build a physician call list that recruiters will actually use?

At minimum: physician name, NPI (or license_state + license_number), phone_number, line_type, phone_validation_status + date, source, call_block_id, attempt_count, last_call_outcome/date, and opt_out_status.

How do I dedupe physicians across multiple sources without losing good numbers?

Dedupe at the physician level using NPI/license, then keep multiple phone numbers as separate dial targets tied to the same physician_id with their own validation status, line type, and outcomes.

How often should I refresh phone validation?

Use triggers: refresh when you see no-answer clusters (same source + same phone_validation_date cohort producing repeated no_answer outcomes) or a source slice’s Connect Rate drops across comparable call blocks.

Should I dial office lines and mobile numbers in the same block?

Usually no. Separate blocks by line_type so your metrics are interpretable and your scripts match the channel. If you must mix, tag line_type and report Connect Rate per 100 dials by line_type.

What’s the fastest way to get a list into Heartbeat.ai?

Use a clean CSV template and map fields once, then keep the same header going forward. You can upload your file here to start mapping.

Next steps

• Implement the CSV header line above and enforce required fields (NPI/license, validation, line type, suppression).
• Run one controlled call block and split results by source and line_type to find the fastest fixes.
• Align your team on definitions and routing using line type guidance and NPI/license matching.
• When you’re ready to operationalize, create a Heartbeat.ai account and start free search & preview data.

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.