Physician database API: an ops playbook for always-fresh recruiting outreach

Ben Argeband, Founder & CEO of Heartbeat.ai — Ops-friendly: clear “what to pass in,” table, steps.

Who this is for

This is for recruiting ops teams building programmatic enrichment and refresh for physician outreach—so recruiters spend time on conversations, not chasing bad numbers and bounced emails.

• Primary audience: Recruiting ops teams that want enrichment/refresh programmatically.
• Systems in scope: ATS/CRM, dialer, email platform, sequencing tool, data warehouse.
• What you’ll implement: stable identity keys (NPI/license), validation + suppression, ranked contacts, refresh triggers tied to outreach outcomes.

Quick Answer

Core Answer

Use a physician database API keyed by NPI/license to validate, rank, and refresh phone/email continuously so outreach stays connectable and reporting stays consistent.

Key Statistic

Heartbeat observed typicals: mobile accuracy 82% (first mobile); email accuracy 95%. Output guidance: include confidence + refresh date per field.

Best For

Recruiting ops teams that want enrichment/refresh programmatically.

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 “Always Fresh” Workflow: Sync → Validate → Rank → Outreach → Refresh

Physician identity is relatively stable; contact points are not. Your pipeline should treat NPI and license matching as the spine, and treat phone/email as attributes that must be validated, ranked, and refreshed.

1. Sync: send stable identifiers (NPI, license, specialty, state) from your systems to the API.
2. Validate: run phone validation and email verification; normalize; suppress opt-outs and known bad contacts.
3. Rank: return best next contact options first, with confidence and recency metadata.
4. Outreach: route contacts into dialer/sequences with time-of-day and channel rules.
5. Refresh: re-check on schedule and on events (bounce, wrong-party, repeated no-answer) so decay doesn’t compound.

Registry vs enrichment: NPPES is a baseline for provider identity (NPI) (see NPPES). It is not designed to keep recruiting-grade phone/email current. That’s why ops teams separate identity matching from contact enrichment and refresh.

Step-by-step method

Step 1: Lock your identity key strategy (start with NPI/license, not phone/email)

Data pipelines need stable keys. For US physicians, NPI is the default identity key. When NPI is missing or you’re reconciling state records, use license matching (license number + state) as a secondary identity path.

• Primary key (recommended): NPI
• Secondary identity path: license number + license state
• Helpful disambiguators: name, specialty, practice/organization, city/state

Design your internal record so NPI/license is the join key across systems. Phone/email should be versioned and refreshable without changing the identity record.

Step 2: Define the minimum API contract (inputs, outputs, and what ops must store)

If you want a recruiting-grade physician database API, don’t just store “a phone number.” Store the metadata that lets you route outreach, audit quality, and debug failures.

Minimum fields to pass and persist

Implementation note: store phone and email as separate records (or separate history rows) keyed to the physician identity (NPI/license) with their own refreshed_at. That prevents “one stale field” from forcing a full record overwrite.

Step 3: Define quality terms once (so ops and engineering measure the same thing)

These definitions keep your dashboards honest and your refresh logic consistent.

• Mobile accuracy definition: percent of returned “mobile” numbers that reach the intended physician when dialed (denominator: per 100 returned mobile numbers dialed).
• Email accuracy definition: percent of returned emails that belong to the intended physician (denominator: per 100 returned emails used in outreach where identity is confirmed via reply/candidate confirmation).
• Confidence definition: a numeric or categorical score indicating likelihood the contact is correct for the matched physician identity, based on available signals.
• Recency definition: the date the contact field was last refreshed/observed; use it to decide when to re-check.

Step 4: Put validation + suppression in the pipeline (not in recruiter behavior)

Recruiters shouldn’t be the ones discovering bad data. Build guardrails into the workflow:

• Phone validation: normalize to E.164, tag validation status, and store phone_validated_at.
• Email verification: store email_verification_status (e.g., verified/risky/unknown (use your platform’s taxonomy)) and email_verified_at so you can segment outcomes.
• Suppression: centralize opt-outs and wrong-party reports; apply suppression before any outreach.

The trade-off is… stricter suppression and verification can reduce your “available contacts” count, but it usually increases real connects and protects deliverability.

Step 5: Rank contacts for workflow fit (connectability beats “more fields”)

Ranking is where ops teams win time back. Instead of returning a flat list, return the best next action for outreach. For Heartbeat.ai, this includes ranked mobile numbers by answer probability so dialers start with the most likely pickup path.

• Route by time-of-day: mobile-first after hours; office-first during clinic hours if you expect gatekeepers.
• Route by confidence + recency: newer and higher-confidence contacts go earlier in the sequence.
• Fallback logic: if a contact fails (wrong-party, bounce), suppress it and pull the next ranked option.

Step 6: Choose API vs CSV upload based on how you operate (COMPARISON_TABLE)

Both workflows can work. The difference is whether you need continuous refresh, automated suppression, and structured failure handling.

If you’re deciding between workflows inside Heartbeat.ai, you can start with the CSV upload workflow for a quick pilot, then move to the API integration when you want always-on refresh and automated suppression.

Step 7: Implement failure handling (required)

Failure handling is what keeps recruiters from wasting cycles and what keeps your metrics honest. Build these behaviors into your integration:

• Hard failures: invalid NPI/license, no match, or ambiguous match → return a structured error and route to a “needs review” queue.
• Soft failures: contact exists but low confidence or old recency → return it, but flag it so outreach uses a safer channel first.
• Outreach feedback loop: dispositions like “wrong number,” “left practice,” “email bounced,” “opt-out” should write back to suppression and trigger refresh.
• Idempotency: same input should not create duplicate provider records; key on NPI/license and use an idempotency key for writes.

How to evaluate a physician database API (ops questions)

• Identity: Does it support NPI and license matching, and does it return match_status (matched/ambiguous/no_match)?
• Quality metadata: Do you get match_confidence plus refreshed_at per field (phone/email), not just a single “last updated”?
• Validation: Are phone validation and email verification statuses returned in a way you can gate sequences?
• Suppression: Can you write back opt-outs and wrong-party dispositions so they stay suppressed across refreshes?
• Operations: Are retries, logging, and idempotent writes supported so failures don’t create duplicates?

Use cases ops teams actually run

• Bulk physician lookup API use case: enrich a batch of NPIs from a new req intake, then route top-ranked contacts into sequences.
• Physician data refresh API use case: refresh only the records that bounced, were marked wrong-party, or have old recency—without reprocessing your entire database.
• Recruiter workflow routing: office-first during clinic hours, mobile-first after hours, with suppression enforced across channels.

Diagnostic Table:

Use this to diagnose why outreach isn’t connecting and what to change in your API workflow.

Weighted Checklist:

Score your current approach (0–2 each). Total helps you decide whether you’re ready for API-first or should pilot with upload first.

• (2) Stable identity: Every physician record is keyed on NPI or license matching, not name.
• (2) Match outcomes: You store match_status (matched/ambiguous/no_match) and route ambiguous to review.
• (2) Field-level recency: You store refreshed_at per phone/email field (recency).
• (2) Confidence stored: You persist match_confidence and use it to route outreach (confidence definition documented).
• (2) Validation gates: phone validation and email verification happen before sequences launch.
• (2) Suppression loop: Opt-outs and wrong-party dispositions write back to a suppression list used by all tools.
• (2) Refresh triggers: Bounce/wrong-party/no-answer patterns trigger refresh; scheduled refresh exists for aging records.
• (2) Idempotent writes: Your integration prevents duplicate provider/contact rows on retries.

Interpretation: 0–8 = fix foundations; 9–12 = pilot; 13–16 = API-first is likely worth it.

Outreach Templates:

These templates assume you’re using ranked contacts with confidence + recency stored. Keep them short and operational, and ensure your opt-out handling matches your channel rules and local requirements.

Template 1: First text to a physician mobile (after-hours)

Message: “Hi Dr. {{LastName}} — this is {{RecruiterName}} recruiting for {{Role}} in {{City/State}}. Is it okay to text you details, or is there a better number/email? If you prefer no texts, reply STOP (where supported), and we’ll suppress this number.”

Template 2: First email when verification is strong

Subject: “{{Role}} in {{Location}} — quick fit check”

Body: “Dr. {{LastName}}, I’m reaching out about a {{Role}} opportunity in {{Location}}. If you’re open to a 5-minute call, what’s a good time? If not, reply ‘no’ and I’ll close the loop.”

Template 3: Gatekeeper-friendly office call opener (clinic hours)

Script: “Hi — I’m trying to reach Dr. {{LastName}} about a professional opportunity. What’s the best way to get a message to them, or is there a preferred time to call back?”

Common pitfalls

• Matching on name only: duplicates and wrong-party outreach. Fix: key on NPI/license and store match_status.
• Not storing field-level recency: you can’t tune refresh. Fix: refreshed_at per phone/email field.
• Flat contact lists: recruiters guess which number to try. Fix: rank contacts and route by time-of-day.
• No write-back loop: wrong numbers and opt-outs don’t propagate. Fix: dispositions write back to suppression and trigger refresh.
• Overwriting contacts without history: you lose auditability. Fix: keep contact history rows keyed by NPI/license.

How to improve results

Improvement comes from measuring the funnel, then using those measurements to trigger refresh and routing changes.

Outreach metrics (canonical definitions)

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

Measurement instructions (required)

Measure this by… instrumenting your pipeline so every outreach attempt is tied back to (1) the physician identity key (NPI/license), (2) the exact contact used, and (3) that contact’s confidence + recency at send time.

• Calls: log total dials, connected calls, and human answers; store phone_type and phone_validation_status for the dialed number.
• Email: log sent, delivered, bounced, and replies; store email_verification_status and email_verified_at for the used address.
• Refresh triggers: bounce → refresh email; wrong-party → suppress + refresh phone; repeated no-answer → try next ranked contact and refresh if recency is old.

Operational “time math” you can run with your own data

If your team makes X dials per day and your Connect Rate increases from A to B after ranking + refresh, incremental connected calls per day = X × (B − A). Multiply by your average recruiter minutes per connected call to estimate time saved or redeployed into submissions.

Refresh strategy that doesn’t break reporting

• Don’t overwrite blindly: keep a “current” contact plus a history table keyed by NPI/license and timestamp.
• Refresh windows: refresh contacts when recency exceeds your internal threshold, and immediately on negative signals (bounce, wrong-party).
• Confidence-aware routing: low-confidence contacts can be used for softer channels first while you refresh phone.

Legal and ethical use

• Legitimate recruiting outreach only: use contact data for bona fide professional recruiting.
• Honor opt-outs: maintain suppression lists and apply them before any outreach.
• Minimize data: store what you need for operations (identity key, contact, recency, confidence, suppression reason).
• Local laws vary: requirements vary by jurisdiction and channel (call/text/email). Coordinate with counsel for your specific use case.

Evidence and trust notes

For baseline provider identity data, the US NPI registry is a primary reference point: NPPES (CMS) NPI Registry. In practice, recruiting workflows still require enrichment because contact fields change and aren’t consistently available in baseline registries.

Note: The links below are for operational awareness, not legal advice.

For channel rules and opt-out expectations, start with official guidance: FTC CAN-SPAM Compliance Guide and FCC Telemarketing & Robocalls overview.

How we think about data quality at Heartbeat.ai (matching, validation, suppression, and refresh): see our trust methodology for provider contact data.

FAQs

What should I pass into a physician database API?

Pass stable identifiers first: NPI when you have it, or license number + state for license matching. Add specialty and name/location hints to reduce ambiguity.

What should the API return besides phone and email?

Return match_status and match_confidence, contact type (mobile/office), phone validation and email verification statuses, and field-level refreshed_at so ops can route outreach and trigger refresh.

How often should we refresh physician contact data?

Use a hybrid approach: scheduled refresh for aging records plus event-based refresh on bounces, wrong-party dispositions, and repeated no-answer patterns. Store recency per field so you can tune this.

Is an API better than uploading a CSV?

An API is better when you need continuous refresh, automated suppression, and failure handling inside your systems. Uploads are fine for pilots and one-off campaigns, but they’re harder to keep current.

How do we know if ranking is working?

Track Connect Rate (connected calls / total dials) and Answer Rate (human answers / connected calls), and compare before/after by phone_type and recency bands. For email, track Deliverability Rate, Bounce Rate, and Reply Rate segmented by email_verification_status.

Next steps

• Map your fields: review the Heartbeat.ai API overview and map inputs/outputs to your ATS/CRM and warehouse.
• Pilot fast: if you want a quick test, start with uploading a file for enrichment, then graduate to API refresh once your keys and suppression are stable.
• Related resource: align stakeholders with how a physician contact database fits into recruiting ops.
• Try it now: start free search & preview data and validate confidence + refresh date outputs against your current outreach outcomes.

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.