Definitive Healthcare alternative

Ben Argeband, Founder & CEO of Heartbeat.ai — Factual and simple; show where each fits.

Who this is for

You already have facility intelligence (systems, hospitals, clinics, affiliations). But you still can’t consistently reach the clinician: no direct physician mobile numbers, emails that bounce, and duplicates in your ATS/CRM because identity isn’t anchored.

This is for recruiters who bought facility intelligence but still lack physician mobiles/emails—and need a workflow that turns “targets” into real conversations.

Quick Answer

Core Answer

A Definitive Healthcare alternative is clinician-level contact data (mobiles/emails) tied to identity keys like NPI and license matching, so you can reach providers directly.

Key Insight

Facility records describe organizations; recruiting outcomes depend on clinician identity resolution plus reachable channels you can measure and suppress when wrong.

Best For

Recruiters who bought facility intelligence but still lack physician mobiles/emails.

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.

TL;DR: use which tool when

Important: An “alternative” can mean replacing facility intel for certain workflows, or complementing it when you need direct clinician outreach.

Pilot validation checklist (run this before you migrate anything):

• Identity anchor: Can you store NPI on the clinician record and use it to merge duplicates?
• Duplicate rate: How many “new” records are actually the same clinician already in your ATS/CRM?
• Phone outcomes: Track Connect Rate and Answer Rate on a fixed cohort (same specialty + geo).
• Email outcomes: Track Deliverability Rate, Bounce Rate, and Reply Rate on the same cohort.
• Suppression: Are opt-outs, bounces, and wrong numbers suppressed so they don’t re-enter future exports?

Framework: The “Facility vs Clinician” Framework: What you’re actually buying

Most “database disappointment” in recruiting comes from buying the wrong unit of value.

If you’re comparing facility intelligence vs clinician contact data, you’re really deciding whether your next bottleneck is account targeting (facility) or direct outreach (clinician).

• Facility data helps you understand organizations: locations, affiliations, ownership, and where care is delivered. Definitive Healthcare is primarily facility/org intelligence.
• Clinician data helps you reach people: the individual provider identity (often anchored by NPI and license matching) plus contact channels (mobile, email) that actually connect. Heartbeat.ai is clinician person-level contact data.

The trade-off is… facility intelligence accelerates account targeting, while clinician contact data accelerates speed to conversation because you can reach providers directly instead of routing through a main line.

MYTH_BUST: “If I know the facility, I can find the doctors.” In practice, that turns into hours of switchboards, directory loops, and stale web pages—especially when clinicians split time across sites or own private practices.

Step-by-step method

Step 1: Decide what you’re trying to ship this week

Pick one outcome and build the data stack around it:

• Outcome A: Build a target account list (systems, hospitals, clinics, service lines) → facility data is primary.
• Outcome B: Submit candidates fast (reach clinicians, book calls, move to CV) → clinician data is primary.
• Outcome C: Do both → pair tools with a clean handoff (next step).

Step 2: Use a pairing workflow (facility targeting → clinician outreach) when you need both account context and direct clinician reach in the same req

1. Start with facility data to pick targets: service line, ownership, locations, and affiliations.
2. Extract the clinician universe you actually recruit: specialty, role, geography, and affiliation signals.
3. Resolve identities so each clinician becomes one record in your ATS/CRM (NPI first; license matching when needed).
4. Append contact channels (mobile + email) for outreach and track outcomes by channel.
5. Suppress bad channels (bounces, wrong numbers, opt-outs) so you don’t keep paying the penalty.

Step 3: Lock identity keys before you buy more “contacts”

Identity resolution (definition): the process of matching records that refer to the same real-world clinician across sources using stable identifiers (for example, NPI) plus supporting attributes (name, specialty, address history, and license matching).

Practical identity keys for healthcare recruiting:

• NPI as the primary anchor for clinicians
• State license and license number to disambiguate common names or fill gaps
• Location history to confirm you’re contacting the right person

Minimum fields to store on the clinician record (for clean merges and reporting):

• NPI (primary clinician key)
• License state + license number (secondary key)
• Specialty and recruiting segment tags
• Primary practice location (and prior locations if you track them)
• Suppression flags: email suppressed, phone suppressed, opted out

Step 4: Run a small connectability pilot before you migrate anything

Don’t evaluate tools by UI. Evaluate by whether your team can connect with clinicians quickly.

1. Pick one specialty and one geography you actively recruit.
2. Pull 100 clinicians you would genuinely contact.
3. Run your normal outreach sequence for 5 business days.
4. Log outcomes in one place (ATS/CRM activity + dialer logs + email platform results).

Heartbeat.ai includes ranked mobile numbers by answer probability so recruiters can prioritize dials when time is tight.

If you want to validate fit without committing, you can start free search & preview data in Heartbeat.ai and run the pilot above.

Diagnostic Table:

Weighted Checklist:

Scoring rubric: score any “Definitive Healthcare alternative” you’re considering (including Heartbeat.ai). Total 100 points.

• Identity keys (30)
  • NPI present and usable as a primary key (15)
  • License matching supported for disambiguation (10)
  • Clear merge + suppression workflow for duplicates (5)
• Reachability (35)
  • Mobile numbers intended for recruiting outreach (15)
  • Email deliverability support (bounce handling + suppression) (10)
  • Clear measurement plan for connect/reply outcomes (10)
• Workflow fit (20)
  • Export/API into your ATS/CRM with stable IDs (10)
  • Team can operationalize quickly with clear export/API + stable IDs (10)
• Facility context (15)
  • Affiliations/locations useful for targeting (10)
  • Org-level filters that reduce wasted outreach (5)

Scoring guidance: if a tool scores under 70 on Identity + Reachability combined, it will look fine in a demo and disappoint in production.

Outreach Templates:

Template 1: First-touch SMS (direct clinician outreach)

Use when: you have a mobile number and a clean clinician identity (NPI/license matched).

Message: “Hi Dr. {{LastName}}—{{YourName}} here. Recruiting for a {{Specialty}} role near {{City}}. Are you open to a 5-min call this week? Reply STOP to opt out. If I have the wrong number, tell me and I’ll remove it.”

Template 2: Email (facility context + clinician focus)

Subject: Quick question about your {{Specialty}} plans

Body: “Dr. {{LastName}}, I’m reaching out because your practice/location in {{Area}} aligns with a role we’re filling. If you’re open, I can share comp + schedule in 2 minutes. If not you, who’s best to speak with? (If you prefer I stop outreach, just say so.)”

Template 3: Gatekeeper call opener (when you only have facility routing)

Use when: you’re stuck with facility numbers and need a transfer.

Script: “Hi—can you connect me to Dr. {{LastName}}’s direct line or voicemail? I’m calling with a recruiting question and don’t want to tie up the main line.”

Operational note: If you’re relying on Template 3 for most outreach, you’re using facility data for a clinician problem. Fix the stack, not the script.

Common pitfalls

Pitfall 1: Assuming facility intelligence includes clinician reachability

Facility datasets are built to describe organizations. Clinician contact channels are a different collection problem with different decay patterns. If you buy one expecting the other, your team pays in time.

Pitfall 2: Treating names as IDs

“John Smith, MD” is not an identifier. Without NPI and license matching, you’ll merge the wrong people, double-message the right people, and poison your CRM.

Pitfall 3: Measuring success by “records exported”

Exports don’t place clinicians. Connections do.

Pitfall 4: No suppression loop

If you keep dialing wrong numbers and emailing bounces, your team’s output drops and your channels degrade. Suppression is not a nice-to-have; it’s how you stop repeating the same mistakes.

How to improve results

1) Track the metrics that move placements (with consistent denominators)

Measure this by… running a weekly scorecard on a fixed cohort (same specialty + geo) so you can compare week-over-week without changing the denominator.

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

2) Put measurement into the workflow (not a spreadsheet nobody opens)

• Calls: use dialer logs for total dials and connected calls; tag “human answer” as a disposition so Answer Rate is measurable.
• Email: use your email platform for sent/delivered/bounced; log replies back to the clinician record in your ATS/CRM.
• ATS/CRM: store NPI as the stable clinician key; store suppression flags (email suppressed, phone suppressed, opted out).

3) Run a weekly suppression + merge routine

• Hard bounces → suppress email
• Wrong number / disconnected → suppress phone
• Opt-out requests → suppress across channels
• Duplicate clinician records → merge using NPI + license matching

4) Use the COMPARISON_TABLE worksheet to choose “replace vs pair” (Uniqueness Hook)

This comparison matrix is the fastest way I know to stop tool overlap and get a clean handoff from facility targeting to clinician outreach.

Legal and ethical use

Use clinician contact data for legitimate recruiting outreach with clear intent and respectful frequency. Always:

• Honor opt-outs immediately (SMS and email).
• Follow applicable privacy and marketing laws in your jurisdiction.
• Limit access internally (need-to-know) and log exports.
• Keep your suppression list as a first-class dataset.

Heartbeat.ai does not provide legal counsel. If you operate across states/countries, get your compliance review done before scaling outreach.

Evidence and trust notes

Vendor positioning for Definitive Healthcare is referenced from their official site: Definitive Healthcare. We avoid uncited claims and do not publish competitor pricing or competitor performance assertions.

For how Heartbeat.ai evaluates data quality, verification, and sourcing practices, see: Heartbeat trust methodology.

Related reading for clinician contact data workflows:

• Provider contact data: what recruiters should actually validate
• NPI + license matching for cleaner identity resolution

FAQs

Is Definitive Healthcare a bad fit for recruiting?

No. It’s a fit when your problem is facility and organization intelligence. It becomes a mismatch when your bottleneck is direct clinician reachability (mobiles/emails) and identity resolution.

What should I look for in a Definitive Healthcare alternative?

Clinician-level identity keys (NPI and license matching), contact channels intended for outreach, and an operational suppression loop so your results improve over time.

Can I use both facility data and clinician data together?

Yes. Use facility data to choose targets and understand context, then use clinician data to reach the actual providers and keep one clean clinician record in your ATS/CRM.

How do I test whether clinician contact data works for my team?

Run a pilot on a fixed cohort (same specialty + geo). Track Connect Rate, Answer Rate, Deliverability Rate, Bounce Rate, and Reply Rate using the denominators defined above. Compare against your current baseline.

What’s the fastest way to validate Heartbeat.ai for this use case?

Pick one specialty and geography, then start free search & preview data to confirm you can identify clinicians by NPI and reach them via mobile/email in your existing workflow.

Next steps

• If your bottleneck is account targeting, keep facility intelligence in your stack and tighten your handoff into clinician outreach.
• If your bottleneck is connecting with clinicians, prioritize clinician-level contact data with NPI + license matching and a suppression loop.
• If you need both, implement the pairing workflow and keep facility and clinician objects separate in your CRM.

When you’re ready to validate fit quickly, start free search & preview data in Heartbeat.ai and run the 100-clinician pilot described above.

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.