Clinical systems · Interoperability / clinical data network
Particle Health
Particle Health is a clinical data network and API for digital health and mid-market providers. Buyers use it to pull longitudinal records through nationwide networks rather than standing up another point-to-point interface. It is not a single EHR-to-EHR interface shop like a classic integration engine project.
Strong fit
- Digital health and mid-market providers that need longitudinal records through a clinical data network
- Teams building care or risk workflows that stall without outside-chart history
- Buyers who can staff integration and data-quality ownership after the first connection
Weak fit
- Organizations that only need a single EHR-to-EHR interface Redox-style
- Teams without privacy, BAA, and data-governance bandwidth
- Buyers expecting network coverage to be complete for every geography and record type on day one
Bottom line
Particle Health earns a Recommend for buyers who need clinical history from a network rather than another point-to-point interface project. Outcomes look strongest when product and care teams use retrieved records in a live workflow, not when the connection sits unused. Product value is the network and distillation layer; that is a different job than Redox's integration fabric. Implementation still means security review, identity matching diligence, and ongoing data-quality work. Pricing is custom and should be modeled against query volume and use cases. Score reflects solid interop product marks with ordinary network-coverage and governance caveats.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Particle Health sells access to a clinical data network so product and care teams can pull longitudinal history instead of waiting on another one-off interface. That is adjacent to Redox, not a twin: Redox is integration fabric; Particle is network retrieval and distillation for clinical records.
Buyers get value when retrieved charts actually change a workflow: intake, risk, care management, or clinical decision support. A live API with no owner for match quality and exceptions becomes shelfware.
Coverage and record completeness still vary by geography and source. Diligence should include match rates, failure modes, and who remediates bad links after go-live. Score sits slightly above Redox on our clinical-systems board for network-shaped buyers.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Particle Health | 7.4 | 7.0 |
| Redox | 7.1 | 6.8 |
| Canvas Medical | 7.5 | 7.2 |
Pricing
| Item | Detail |
|---|---|
| Model | Platform and usage-style contracts tied to network access and query patterns. |
| What usually drives cost | Volume of record retrieval, environments, and professional services for first production use cases. |
| What to ask in diligence | Modeled cost at your expected query volume and which use cases must work in year one. |
| Published pricing | Public list price: not published. Expect a custom quote; confirm total cost at your volume. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Particle Health to function | |
| A production use case that needs outside-chart history | Network access without a workflow owner becomes shelfware. |
| Privacy, BAA, and data-governance bandwidth | Clinical network access fails diligence without owners. |
| Integration capacity for identity matching and exceptions | Bad matches create clinical and ops risk. |
| Engineering or analytics owners after go-live | First connection is not the end of data-quality work. |
| Clear year-one query volume and use-case scope | Unbounded retrieval drives cost and noise. |
| What will maximize your value | |
| Measure match rates and failure modes in production | Coverage claims mean little without operational metrics. |
| Wire retrieved records into a live intake or care workflow | Unused APIs do not create outcomes. |
| Name who remediates bad links after go-live | Exceptions otherwise pile up silently. |
| Start with one geography or population where coverage is strong | National marketing averages hide local gaps. |
| Revisit query economics quarterly | Volume growth without use-case discipline burns budget. |
| Deal-breakers | |
| You only need a single point-to-point EHR interface (wrong tool shape). | |
| No privacy or governance owner will complete BAAs and reviews. | |
| No one owns identity matching and exception remediation. | |
| You expect complete coverage for every record type on day one. | |
| Engineering cannot support production monitoring after the first connection. | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract signed → kickoff | 2–6 weeks (BAAs, security review, first use-case scoping) |
| 2 | Kickoff → first live workflow | 6–14 weeks for a first production retrieval path on a defined use case |
| 3 | First live workflow → steady value | 3–6 months as match quality, monitoring, and additional use cases mature |
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category actually buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.