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 companies and mid-sized providers that need a patient's full history through a clinical data network
- Teams building care or risk programs that stall without records from outside providers
- Buyers who can staff the connection work and own data quality after the first link
Weak fit
- Groups that only need one EHR-to-EHR interface, the kind Redox builds
- Teams without time for privacy, BAA, and data rules work
- Buyers expecting complete coverage for every region and record type on day one
Bottom line
Particle Health is worth using for product and care teams that need patient clinical history from a data network instead of building another one-off interface. It suits teams that will use retrieved records in a live workflow and own match quality. Buyers who need integration plumbing should look at Redox.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Particle Health sells access to a clinical data network. Product and care teams can pull a patient's history instead of waiting on another one-off interface.
That is related to Redox but not the same. Redox builds the connections between systems, while Particle pulls and condenses clinical records from a network.
Buyers get value when the records they pull change how work gets done, such as intake, risk, care management, or clinical decision support.
A live API with nobody owning match quality and exceptions goes unused.
Setup still means security review, checks on patient identity matching, and ongoing data quality work.
Coverage and how complete records are vary by region and source. Buyers should ask about match rates, how it fails, and who fixes bad links after go-live.
Pricing is custom, so estimate it against how many lookups you will run and for what.
Among the clinical systems we review, Particle scores slightly above Redox for buyers who need a network.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Particle Health | 7.4 | 7.0 |
| Kno2 | 7.3 | 7.3 |
| Zus Health | 7.2 | 7.0 |
| Metriport | 7.2 | 7.2 |
| Redox | 7.1 | 6.8 |
| Smile Digital Health | 7.1 | 6.6 |
| Onyx | 7.0 | 7.0 |
| Health Gorilla | 6.9 | 6.8 |
| 1upHealth | 6.7 | 6.5 |
Pricing
| Item | Detail |
|---|---|
| Model | Platform and usage contracts tied to network access and how you run lookups. |
| What usually drives cost | How many records you pull, environments, and services to get your first uses live. |
| What to ask before you buy | Estimated cost at the lookup volume you expect, and which uses must work in year one. |
| Published pricing | Particle Health does not publish prices. Expect a custom quote, and confirm the total cost at your volume. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Particle Health to function | |
| A real use that needs records from outside providers | Network access with no owner goes unused. |
| Time for privacy, BAA, and data rules work | Network access fails review without owners. |
| Staff to handle identity matching and exceptions | Bad matches create clinical and operations risk. |
| Engineering or analytics owners after go-live | The first connection is not the end of data quality work. |
| A clear year-one lookup volume and list of uses | Pulling records without limits drives up cost and noise. |
| What will maximize your value | |
| Track match rates and failures in real use | Coverage claims mean little without real-world numbers. |
| Feed pulled records into a live intake or care process | Unused APIs do not create results. |
| Name who fixes bad links after go-live | Otherwise exceptions pile up unseen. |
| Start with one region or patient group where coverage is strong | National averages hide local gaps. |
| Review lookup costs every quarter | More lookups without clear uses burn budget. |
| Deal-breakers | |
| You only need one direct EHR interface (the wrong kind of tool). | |
| No privacy or data rules owner will complete BAAs and reviews. | |
| Nobody owns identity matching and fixing exceptions. | |
| You expect complete coverage for every record type on day one. | |
| Engineering cannot monitor the live system after the first connection. | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | From signing to kickoff | 2-6 weeks for BAAs, security review, and defining the first use |
| 2 | From kickoff to first use | 6-14 weeks until records flow for the first defined use |
| 3 | From first use to steady results | 3-6 months as match quality, monitoring, and more uses improve |
Leadership
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 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.