Upload a physician list for enrichment

By Ben Argeband, Founder & CEO of Heartbeat.ai — Clear steps and common errors; operational tone.

Who this is for

This is for recruiters with spreadsheets who want enrichment without formatting issues. If your team is losing time to bad matches, duplicate rows, or upload rework, this playbook is meant to cut that friction and keep outreach moving.

• Primary user: Recruiters working from CSV exports (ATS/CRM, conference lists, referrals).
• Outcome: cleaner matching, fewer duplicates, and a file your team can reuse on the next refresh.
• What this avoids: wrong-person outreach, broken phone/email fields, and “why did this row match?” confusion.

Quick Answer

Core Answer

Prepare a CSV with stable identifiers (prefer NPI), dedupe to one row per physician, map columns cleanly, upload, then review match strength and recency before outreach.

Key Insight

Unique keys prevent wrong-person matches; clean columns prevent mapping errors; stamping a refresh date keeps teams from working stale contact data.

Best For

Recruiters with spreadsheets who want enrichment without formatting issues.

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 Upload Prep Checklist: Clean → Dedupe → Identify → Upload

1. Clean: make the file structurally predictable (headers, one row per physician, no merged cells).
2. Dedupe: remove duplicates using stable keys (NPI first).
3. Identify: ensure each row has at least one strong identifier; add context fields that reduce collisions.
4. Upload: map fields, validate a sample, run enrichment, then review outputs (including recency) before outreach.

Step-by-step method

Step 1) Start with a “one row = one physician” CSV

Before enrichment, make the file predictable so matching and mapping behave the way you expect.

• Export to CSV (avoid merged cells and hidden formatting).
• One header row; no blank header names.
• One physician per row (don’t mix organizations and individuals).
• Keep multi-location details out of the main file; store extra locations in a separate file if needed.

Common mistake: mixing practice entities and physicians in the same upload. Split them. Identity keys and matching logic differ.

Step 2) Use stable identifiers (why NPI beats name)

Names collide. Even “First + Last + City” collides constantly in provider data. If you want fewer wrong-person matches, use stable identifiers.

• NPI (best): a national identifier designed for provider identity resolution.
• License number + state (helpful): supports license matching when NPI is missing, but formats vary by board/state.
• Email/phone (context, not identity): useful for outreach, but not a reliable identity key by itself.

Match key definition: a match key is the field (or combination of fields) used to decide whether your row corresponds to a specific real-world physician record. Strong match keys are stable over time and unique per person (e.g., NPI). Weak match keys change or collide (e.g., name-only).

Step 3) Dedupe before upload (or you’ll create downstream mess)

Duplicates inflate outreach volume, distort performance metrics, and create “why did they get two messages?” problems. Dedupe in this order:

1. Exact NPI match → keep the most complete row.
2. License number + license state → keep the most complete row.
3. Name + specialty + city/state → only if you must; flag these rows as “needs review.”

If you want a deeper dedupe workflow, use how to dedupe a provider list using NPI.

Step 4) Required columns (minimum vs recommended)

Use the minimum set to support clean matching when NPI is present. Use the recommended set to reduce review time and keep your team aligned on recency and suppression.

Step 5) Standard header pattern and mapping tips

Clean columns prevent bad mapping and reduce support tickets. Use a consistent header pattern so every recruiter’s export maps the same way.

npi,first_name,last_name,credential,specialty,primary_city,primary_state,license_number,license_state,organization_name,work_email,personal_email,phone,source,source_date,notes,refresh_date

• npi: numeric, no dashes/spaces.
• license_number + license_state: keep as text (some boards use letters/leading zeros).
• phone: keep as text to preserve “+1” and avoid scientific notation.
• Don’t put multiple emails/phones in one cell. Put the best one in the main column and the rest in notes.
• Use consistent state abbreviations (e.g., “TX” not “Texas”).

Step 6) Handling multiple practice locations

• Keep identity stable: use the same NPI for the physician across all rows/files.
• Pick a primary location: store one primary_city/primary_state in the main file so matching stays consistent.
• Store extras separately: keep additional locations in a separate file or a structured notes field, then attach after identity is confirmed.

Step 7) Upload and map fields

When you upload file into Heartbeat.ai, your job is to ensure the system reads the file the way you intended: correct row count, correct column mapping, and clean identifiers.

Step 7A) Pre-flight validation (2 minutes)

• Row count check: confirm the system reads the same number of rows you expect.
• Header check: confirm NPI, license_number, and license_state are mapped correctly (not swapped with names).
• NPI format check: spot-check a few NPIs for extra spaces or non-numeric characters.
• Phone-as-text check: confirm phone values didn’t lose “+1” or get converted to scientific notation.
• Delimiter/encoding check: confirm the delimiter is a comma and the file is UTF-8 encoded.
• Five-row spot check: pick five physicians you recognize and verify the matched identity looks right before you proceed.

Step 8) Run enrichment and review outputs (what to expect)

After enrichment, expect outputs you can use operationally:

• Enriched contact fields (where available) plus indicators you can use to prioritize.
• Ranking and prioritization so your team can prioritize records that may be more likely to connect first, including ranked mobile numbers by answer probability.
• Unmatched/needs-review rows when identifiers are weak or conflicting. Treat these as a separate queue before outreach.
• Recency tracking via your refresh_date field (or your internal process) so you can work the newest data first.

Recency definition: recency is how recently a contact field (email/phone) was observed, verified, or refreshed. Operationally, recency is what tells you whether a record is likely still reachable today.

The trade-off is… if you allow weak identifiers to match automatically, you’ll move faster today but you’ll spend that time back later fixing wrong-person outreach and cleaning your CRM.

Diagnostic Table:

Weighted Checklist:

Use this to decide if your file is ready to upload today. Score each item; if you’re under 80, fix the file first.

• (30 pts) NPI present for most rows (or a clear plan to add it). If not, you’re relying on weak identifiers.
• (15 pts) One row per physician; no organizations mixed in.
• (15 pts) Dedupe completed (NPI exact match first). Document your dedupe rule in notes.
• (10 pts) License fields split into license_number + license_state (supports license matching when NPI is missing).
• (10 pts) Phone and email columns contain only one value per cell; extras moved to notes.
• (10 pts) source and source_date populated (even if approximate).
• (10 pts) refresh_date populated (or you have a post-enrichment step to stamp it).

Uniqueness hook (CSV_TEMPLATE worksheet): Create a dedicated “mapping” tab in your spreadsheet with two columns: your_header → target_header (using the standard header pattern above). Require recruiters to update the mapping tab before exporting. This prevents header drift across teams and reduces remapping errors on every upload.

Outreach Templates:

These are designed for legitimate recruiting outreach with clear opt-out handling. Customize specialty/location and keep it short.

Template 1: First email (work email)

Subject: Quick question about your next role

Hi Dr. {{LastName}},

I’m reaching out about a physician opportunity in {{State}}. Are you open to a brief call this week, or should I stop contacting you?

If you prefer not to receive messages, reply “opt out” and I’ll suppress your info.

— {{YourName}}, {{Title}} | Heartbeat.ai

Template 2: Text (mobile)

Hi Dr. {{LastName}} — {{YourName}} here (physician recruiting). Are you open to hearing about a {{Specialty}} role in {{City/State}}? Reply STOP to opt out.

Template 3: Voicemail

Hi Dr. {{LastName}}, this is {{YourName}}. I’m calling about a {{Specialty}} opportunity in {{State}}. If you’re open to a quick conversation, call me at {{CallbackNumber}}. If not, tell me and I’ll take you off my list.

Common pitfalls

Pitfall 1: Treating “name” as an identifier

If your list is mostly First/Last with a city, you’re setting yourself up for wrong matches. Fix it by adding NPI wherever possible and using license_number + license_state as a secondary identifier.

Pitfall 2: Uploading duplicates and then blaming enrichment

Duplicates usually come from your source exports (ATS, CRM, event lists). Dedupe before upload, then keep a “dedupe rule” note in the file so the next recruiter doesn’t reintroduce duplicates.

Pitfall 3: No refresh discipline

If you don’t track recency, you’ll work stale records and conclude “the data is bad” when the real issue is age. Stamp refresh_date and work newest first.

Pitfall 4: Opt-outs buried in notes

Opt-out needs to be a first-class field in your workflow. If a physician opts out, suppress them across future uploads and campaigns. Don’t bury it in free-text notes.

How to improve results

Once your upload is clean, improvement comes from measuring reachability, segmenting by match strength, and closing the loop with suppression and refresh.

1) Track reachability metrics by list source and refresh_date

Measure this by… tracking these rates per campaign and per list source (ATS export vs referral list vs conference list). Always keep the denominator consistent:

• 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).
• Connect Rate = connected calls / total dials (per 100 dials).
• Answer Rate = human answers / connected calls (per 100 connected calls).

2) Segment by match strength before outreach

• Tier A: matched on NPI.
• Tier B: matched on license_number + license_state.
• Tier C: matched on name/location only (review queue before outreach).

This keeps workflow fit tight: your fastest recruiters work Tier A first, while Tier C gets reviewed so you don’t burn reputation or annoy the wrong physician.

3) Operationalize suppression (simple SOP)

1. Capture: store opt-out and wrong-person reports in a dedicated field (not notes) in your CRM/ATS.
2. Apply: apply suppression before every upload and before every outreach send.
3. Audit: spot-check a small sample of suppressed records to confirm they are excluded from outreach.

Suppression key tip: use NPI as the primary suppression key when available. If NPI is missing, suppress by the channel used (email address and/or phone number) so the same person doesn’t get re-contacted on the next upload.

4) Refresh on a cadence tied to active recruiting

Don’t treat enrichment as a one-time event. Stamp refresh_date and re-enrich on a schedule that matches your outreach volume. Work newest refresh_date first.

Legal and ethical use

• Consent: Use outreach channels consistent with your organization’s policies and applicable laws.
• Opt-out: If someone asks you to stop, stop. Maintain an opt-out list and apply it to future uploads.
• Data minimization: Don’t collect sensitive personal data you don’t need for recruiting.
• Auditability: Keep source and source_date so you can explain where a record came from.

If you’re unsure about local requirements, involve counsel. Heartbeat.ai does not provide legal advice.

Evidence and trust notes

When we say “NPI is the best identifier,” we’re aligning to the healthcare identity system designed for that purpose. Match outcomes depend on identifier quality (NPI or board license vs name-only). For how we evaluate sources, matching, and data handling, see our trust methodology.

• NPPES (CMS) — NPI registry baseline
• CMS — National Provider Identifier (NPI) overview

FAQs

What columns do I need to upload a physician list for enrichment?

Use NPI whenever possible, plus name and location context. At minimum: npi (preferred), first_name, last_name, primary_state. Add license_number + license_state when NPI is missing, and include source_date and refresh_date for auditing and freshness.

Why does NPI beat name for matching?

NPI is designed to identify providers uniquely across systems. Names collide and change; NPI is a stable identifier, which reduces wrong-person matches and cleanup work.

What should I do if my list doesn’t have NPI?

Add license_number + license_state if you have it, and include specialty and primary city/state to reduce collisions. Then prioritize collecting NPI going forward in your intake workflow.

What happens to rows that don’t match cleanly?

Treat them as a review queue. Weak identifiers (name-only, partial location, conflicting fields) increase collision risk, so review identity before outreach and improve the match keys for the next refresh.

Why did my phone numbers lose “+1” or turn into scientific notation?

This usually happens when spreadsheets auto-format phone columns as numbers. Store phone as text, re-export to CSV, and re-check a few rows in the pre-flight validation step before you run enrichment.

Should I include work email, personal email, or both?

Include what you have, but keep one value per cell and respect consent and opt-out. If a physician opts out on any channel, suppress them so they aren’t contacted again on future uploads.

Next steps

• Run your first file: upload your CSV and map columns.
• Preview before you commit: start free search & preview data.
• Standardize dedupe: use NPI-based dedupe rules for provider lists.
• If you’re building automation, use the CSV import format guide for physician contacts to align your integration fields.

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.