Particle Health vs Tennr (2026)
Two answers to the question of how clinical information reaches an organisation that does not already have it. Tennr reads what arrives, applying a proprietary vision language model to the faxes, forms and scanned referrals that still carry most inbound healthcare communication, and turning them into routed actionable referrals. Particle Health goes and gets what was never sent, querying the national interoperability networks through a single bidirectional interface with an enrichment layer on top. The distinction matters because organisations misdiagnose this constantly: a practice drowning in unprocessed faxes has an intake problem, while a practice whose patients received care elsewhere has a retrieval problem, and buying the wrong one leaves the actual gap untouched. Many organisations have both, in which case these are complementary rather than competing purchases.
- Referral and document intake is the specific target and the vision language model reads unstructured inbound material directly, which is where the work actually is.
- The output is a routed actionable referral rather than extracted text, so the intake team gets a decision rather than a data set.
- For a practice losing referrals to intake friction, this addresses the revenue leak rather than the filing problem.
- It is retrieval first, querying the national health information networks through a single bidirectional interface, so the data arrives from outside the organisation rather than from the fax machine.
- The enrichment layer sits on top of genuine interoperability infrastructure, which is a harder asset to build than a document reader.
- For an organisation whose gap is records held elsewhere rather than documents arriving unread, this is the product that closes it.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Particle Health and Tennr are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI Health Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Fact | Particle Health | Tennr |
|---|---|---|
| Primary category | Healthcare Administrative Automation | Healthcare Administrative Automation |
| Headquarters | New York, New York | New York, New York |
| Website | particlehealth.com | tennr.com |
Side by Side
Each record in one paragraph
Written to be quoted whole. Each paragraph states what the AI Health Index verified about the vendor, with the caveats attached. Generated from this pair’s live capability grades, so it moves when a grade moves.
The AI Health Index awards Particle Health its top capability grade on EHR and Interoperability Depth. Set against Tennr, Particle Health grades higher on FDA and Regulatory Status, AI Liability and Recourse and EHR and Interoperability Depth. Its thinnest published disclosure sits on Model Supply Chain Disclosure. Grades reflect evidence the AI Health Index could verify at the last review, so a low grade records disclosure the vendor has not published rather than a capability it has been shown to lack.
Source: AI Health Index, August 2026
The AI Health Index awards Tennr its top capability grade on AI Centrality. Set against Particle Health, Tennr grades higher on several axes, including AI Centrality, Model Supply Chain Disclosure and Clinical and Operational Evidence. Grades reflect evidence the AI Health Index could verify at the last review, so a low grade records disclosure the vendor has not published rather than a capability it has been shown to lack.
Source: AI Health Index, August 2026
Questions buyers ask
Should we choose Particle Health or Tennr?
On the axes where the AI Health Index separates them, Particle Health grades higher on FDA and Regulatory Status, AI Liability and Recourse and EHR and Interoperability Depth, and Tennr grades higher on several axes, including AI Centrality, Model Supply Chain Disclosure and Clinical and Operational Evidence. Tennr leads on the greater share of scored axes, but the split means the decision turns on which constraint is binding rather than on an overall winner.
Where do Particle Health and Tennr differ most?
The widest separation the AI Health Index records between Particle Health and Tennr is on AI Centrality, where Particle Health grades C and Tennr grades A. That axis sits in the AI Capability group, so it should carry the most weight for a buyer whose binding constraint is how much of the work the model itself is trusted to do.
Where do Particle Health and Tennr grade the same?
The AI Health Index grades Particle Health and Tennr the same on several axes, including Autonomy and Oversight Model, Model and Technology Transparency and Security Certifications and Trust Center. Neither holds an advantage the index can evidence on those axes, so they should not carry weight in a selection between these two.
What have Particle Health and Tennr not disclosed?
At the last review, at least one of Particle Health and Tennr published thin or absent detail on Model Supply Chain Disclosure. The AI Health Index treats an absent disclosure as a gap in the public record rather than a failure of the product, so these are the axes to get in writing during diligence instead of inferring from the grade.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Healthcare Administrative Automation page.
These solve adjacent problems and a buyer should establish which one they have before comparing: unread inbound documents is an intake problem, while missing outside records is a retrieval problem, and the same organisation frequently has both. Neither vendor publishes a touchless completion rate in comparable form, which is the measure that matters for intake, nor a match rate for retrieval. Neither publishes pricing.
For any product ingesting inbound clinical documents, establish retention and whether customer documents improve models, since the sender of a fax has consented to nothing and the patient in it even less.