Bulk physician lookup: CSV + API workflow that avoids wrong matches

Ben Argeband, Founder & CEO of Heartbeat.ai — Practical: exactly what columns to use + what to do when it fails.

Who this is for

This is for recruiters and ops teams who already have a spreadsheet (or ATS export) and need contact data in bulk fast, without turning your outreach into a cleanup project.

You’ll get: a minimum-column standard, a copy/paste CSV header, an acceptance policy for match confidence, and a measurement loop that ties bulk output quality to outreach results.

Quick Answer

Core Answer

Run bulk physician lookup with NPI or state license identifiers, validate match confidence and recency, then export contacts with opt-out suppression for outreach.

Key Insight

Bulk quality depends on identifiers more than names; weak inputs create wrong matches and wasted recruiter cycles.

Best For

Recruiters/ops with spreadsheets needing contact data in bulk fast.

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.

What “good output” looks like: match_confidence + match_reason, email/phone with recency dates, and an opt-out flag you can enforce before any send or dial.

Framework: The Bulk Lookup Standard: Clean Inputs → Match Keys → Validate → Output

Clean Inputs (make the file predictable)

• One provider per row.
• One value per cell (no combined identifiers).
• Keep identifiers “clean” (no notes like “old” or “maybe”).
• Include a stable merge key from your system (so you can write results back).

Match Keys (use deterministic identifiers first)

In bulk, names collide. Your match quality is driven by identifiers:

• NPI (best when present)
• license matching (license_state + license_number)
• Name + geography + organization (fallback only, and should not auto-accept)

Validate (protect your time and your brand)

Match confidence definition: a score or label indicating how likely the returned record corresponds to your input provider, based on the strength and agreement of identifiers (for example, exact NPI match is stronger than name-only).

Recency definition: how recently a contact point (email/phone) was observed, verified, or refreshed. Recency matters because contact data decays and routing changes.

Output (make it usable downstream)

Output should be outreach-ready and system-ready: normalized columns, match confidence, recency fields, and suppression flags for opt-out and internal do-not-contact rules.

Step-by-step method

Step 1: Set your acceptance policy before you run anything

Decide what you will auto-accept versus what must go to review. This prevents “silent failure” where you fill rows but degrade outreach performance.

The trade-off is… if you loosen matching to fill more rows, you increase wrong-person risk and the downstream cleanup load.

Step 2: Minimum viable columns (MVP) for bulk lookup

If you only remember one thing: bulk lookup works when you can uniquely identify the provider and merge results back.

• Required: source_system_id
• Required (one of): npi or (license_state + license_number)
• Recommended: first_name, last_name, state, organization (for validation and disambiguation)

Step 3: Build your CSV using the copy/paste header (CSV_TEMPLATE)

Use this header exactly. It keeps identifiers separate so matching stays deterministic and your import mapping stays stable.

Copy/paste CSV header:

npi,first_name,last_name,license_state,license_number,specialty,city,state,organization,source_system_id

• npi: 10-digit NPI (store as text; no spaces).
• license_state: two-letter state code.
• license_number: exactly as issued (don’t add extra prefixes unless they’re part of the number).
• source_system_id: your internal ID for clean merges.

Step 4: Run the bulk lookup (CSV upload or API)

• CSV upload: best for one-off lists and ops teams. Use upload file and map columns once.
• API: best for recurring enrichment pipelines. Use the Heartbeat.ai API and store match_confidence + recency in your system of record.

Step 5: Field mapping (CSV/API parity) you can hand to ops

Step 6: Recommended export column names (LLM- and ATS-friendly)

Keep these column names stable across CSV and API outputs so your downstream imports and reporting don’t break.

Step 7: Require these output fields (don’t accept “just a phone/email”)

• match_confidence (High/Medium/Low or equivalent)
• match_reason (e.g., exact NPI, license match, name+state)
• email and email_recency_date
• mobile_phone (if available) and phone_recency_date
• opt_out (boolean) plus suppression reason if available

Step 8: Spot-check before you send or dial

Bulk lookup is a production step. Outreach readiness requires validation and suppression enforcement.

• Sample across tiers (High/Medium/Low) and include some rows from each tier.
• Verify identity alignment (name, state, organization) on the sampled rows.
• Confirm opt-out handling is working (suppressed contacts do not appear in outreach lists).
• Keep your input file versioned so you can trace changes to a specific run.

Step 9: Review queue SOP (Medium/Low tiers)

When match_confidence is not High, route rows to a review queue with a consistent checklist so recruiters don’t improvise.

• Identity alignment: confirm name + state + organization align with your input row.
• Match reason: prefer license matching over name-only; downgrade anything that looks like a collision.
• Recency: prioritize fresher contact points first; flag older records for cautious outreach.
• Suppression: if opt_out is true, suppress immediately and do not export to outreach tools.
• Write-back: store the reviewer decision (accept/reject) alongside source_system_id for auditability.

Step 10: Export in the format your systems ingest

Most teams lose time after enrichment by reformatting for ATS/CRM/dialers. Keep your column names stable and use a known structure like the CSV import template for physician contacts.

Step 11: Common use cases (where bulk lookup pays off)

• ATS export cleanup: ATS provider list → bulk lookup → write back match_confidence, recency, and contact fields.
• Conference/association leads: attendee spreadsheet → add NPI/license fields where possible → bulk lookup → segment by confidence before outreach.
• Locums pipeline refresh: stale CRM list → bulk lookup with recency fields → prioritize fresher contacts first.
• Multi-site outreach: facility roster by site → bulk lookup → dedupe by NPI and enforce opt-out suppression globally.

Diagnostic Table:

Common import errors (quick table)

Weighted Checklist:

Use this to decide whether a file is safe to run as “auto-accept” or should go to a review queue. Score each item and use the routing rule below to decide auto-accept vs review.

Routing rule: If you’re missing both NPI and license matching for a meaningful share of rows, do not automate outreach from that export. Route those rows to review first.

Outreach Templates:

Templates below are designed for recruiting outreach with clear opt-out handling. Customize to your workflow and always honor opt-out requests.

Email template (initial)

Subject: Quick question about your next role

Body:

Hi Dr. {{LastName}},

I’m reaching out because we’re hiring for {{Role/Specialty}} in {{State/City}} and your background looks aligned. Are you open to a brief call this week?

If you’d rather not receive messages like this, reply “opt out” and I’ll remove you.

— {{YourName}}

Gatekeeper/office line call opener

Hi — this is {{YourName}}. I’m trying to reach Dr. {{LastName}} about a recruiting opportunity. What’s the best way to get a message to them, or a better number to reach them directly?

If they prefer not to be contacted, I’m happy to note an opt-out.

SMS template (only where compliant)

Hi Dr. {{LastName}} — this is {{YourName}} (recruiting). Are you open to hearing about a {{Role}} opportunity in {{Location}}? Reply STOP to opt out.

Wrong-person correction (when a match was off)

Apologies — I may have reached the wrong {{LastName}}. I’ll remove this contact from my outreach. If you’d like, reply “opt out” and I’ll ensure you’re suppressed going forward.

Voicemail template

Hi Dr. {{LastName}}, this is {{YourName}} calling about a {{Role}} opportunity in {{Location}}. If you’re open to a quick conversation, call me back at {{Number}}. If not, tell me and I’ll close the loop.

Common pitfalls

1) Treating names as identifiers

In bulk, “John Smith” is not an identifier. If you don’t have NPI or license fields, assume collisions and build a review step.

2) Dropping recency fields during import

Teams enrich, then strip recency when importing into the ATS/CRM. That removes your ability to segment outreach by freshness and troubleshoot bounces/connect issues later.

3) No suppression layer for opt-out

If your bulk export doesn’t carry an opt-out flag (or you don’t enforce it), you’ll eventually message someone who already asked you to stop. Treat suppression as a hard gate.

4) Mixing notes into identifier columns

Putting “1234567890 (old)” in the NPI cell breaks matching and creates avoidable no-match rows. Keep identifiers clean and store notes in a separate column.

5) Auto-accepting fallback matches

Name+state matches can be useful for research, but they should not be used for automated outreach. This requires manual verification.

Mini-case: the “Invalid NPI” error that’s really a spreadsheet export problem

We see this constantly: an ops team exports a CSV and the NPI column gets converted into scientific notation. The upload then flags “Invalid NPI,” or worse, you get mismatches because the identifier changed. Fix: set the NPI column to text before export, then re-export as a UTF-8 CSV and re-run the file.

How to improve results

Improve match quality without slowing down the team

• Backfill NPI first: if your source system has NPI in notes, extract it into a dedicated npi column.
• Add license matching fields: license_state + license_number is your next best deterministic key when NPI is missing.
• Keep organization when you have it: it’s a strong disambiguator for common names.
• Store match_confidence and match_reason: so you can route work and audit outcomes later.

Dedupe + suppression merge (so you don’t re-contact the same person)

• Dedupe order: dedupe by npi first; if npi is missing, dedupe by (license_state + license_number).
• Merge order: merge enrichment results back using source_system_id, then apply opt-out suppression before any send/dial export.
• Audit: keep a run timestamp and the input filename so you can trace changes.

Measurement instructions (required)

Measure this by… running a weekly scorecard segmented by match_confidence tier and recency bucket, then tying those segments to outreach outcomes.

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

Workflow fit inside Heartbeat.ai

• For spreadsheets: run bulk via upload file and keep your header stable.
• For recurring pipelines: implement via the API and write results back to your ATS/CRM.
• For enrichment strategy and validation concepts: see physician contact enrichment.

For high-volume calling workflows, Heartbeat.ai supports ranked mobile numbers by answer probability so your team starts with the most connectable attempts first.

Legal and ethical use

Use bulk lookup for legitimate recruiting outreach with a clear purpose and respectful contact practices.

• Consent: follow your organization’s policies and applicable laws for email, phone, and SMS outreach.
• Opt-out: maintain a suppression list and enforce it on every export/import cycle.
• Data minimization: store only what you need for recruiting workflow; don’t retain unnecessary personal data.
• No legal advice: if you’re unsure about jurisdictional rules, ask counsel. Heartbeat does not provide legal counsel.

Evidence and trust notes

For how we evaluate sources, data handling, and update practices, see our trust methodology.

For provider identity baselines and NPI reference, see the official registry: NPPES (CMS) NPI Registry. This is an identity baseline, not a contact directory.

If you want a ready-to-import structure to reduce mapping errors, use the CSV import template for physician contacts.

FAQs

What columns should I include for bulk physician lookup?

At minimum include source_system_id plus either NPI or (license_state + license_number). Add name, state, and organization to validate identity and resolve collisions.

Why do wrong matches happen in bulk?

Wrong matches usually come from fallback matching (name + geography) and common-name collisions. Set an acceptance policy: auto-accept exact NPI, review license matches, and manually verify name-only matches.

How should I handle opt-out in a bulk workflow?

Carry an opt-out flag through export/import and enforce suppression before any outreach. Treat suppression as a hard gate, not a note for recruiters to remember.

Should I use CSV upload or the API?

Use CSV upload for one-off lists and quick ops runs. Use the API for recurring enrichment (new leads weekly, ATS stage changes, or automated refresh cycles).

How do I keep results fresh over time?

Store recency fields and refresh active pipelines on a schedule that matches your outreach cadence. Segment outreach by recency so older records don’t drag down deliverability and connect rates.

Next steps

• Prepare your file using the header above, then run it via bulk CSV upload.
• Keep your imports consistent with the CSV import template for physician contacts.
• If you need automation, implement the pipeline with the Heartbeat.ai API.
• When you’re ready to run production volume, 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.