Clinical systems · Shared patient record & data platform
Zus Health
Zus Health is a shared health data platform that gathers a patient's outside records into one continuously updated record for care teams and the software they use.
Strong fit
- Value-based care groups that want outside records in front of clinicians before a visit
- Digital health companies that would rather build on a shared FHIR patient record than write their own data plumbing
- Teams working on risk adjustment and quality gap closure from network data
Weak fit
- Organizations that only need a point-to-point HL7 interface
- Health systems that only need the outside records their EHR's built-in exchange already returns
- Buyers who need a published price list
Bottom line
Zus Health is a health data company founded in 2021 by athenahealth co-founder Jonathan Bush. It pulls a patient's records from national networks, labs, pharmacies, and payer systems and keeps them as one continuously updated record. Care teams use that record to see outside history before a visit and to find risk-adjustment and quality gaps, and software companies build their products on its data platform.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Zus Health launched in June 2021 with a $34 million Series A led by Andreessen Horowitz. Jonathan Bush, who co-founded athenahealth, leads the company. Its network finds a patient's records in EHRs, national exchange networks, labs, pharmacies, imaging centers, and payer systems, removes duplicates, and keeps one longitudinal record that the customer's software can read through APIs or inside the clinician's workflow.
In April 2025 Bush put contracted annual recurring revenue at about $10 million. Care teams use the shared record for pre-visit review and for risk-adjustment and quality work.
Compare Metriport when an engineering team wants an open-source records API, Health Gorilla when the team needs a QHIN for TEFCA access, and Redox when the work is mainly moving data between two systems.
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 |
| Health Gorilla | 6.9 | 6.8 |
| 1upHealth | 6.7 | 6.5 |
Pricing
| Item | Detail |
|---|---|
| Model | Enterprise subscription quoted by Zus, with terms set by the patients covered and the data services used. |
| What usually drives cost | Number of patients, which data sources are pulled, and whether risk-adjustment and quality tools are added. |
| What to ask in diligence | Annual price at your patient count, what each data source costs, and fees for EHR or app integration. |
| Published pricing | No public list price. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Zus Health to function | |
| Patient roster and identity data | The network needs clean demographics to find records. |
| Use cases picked first | Pre-visit review and risk adjustment need different setups. |
| Clinical workflow owner | Records only help if someone reads them before the visit. |
| Engineering or EHR app plan | Data has to show up where clinicians work. |
| Data use agreements signed | Network rules apply to every query. |
| What will maximize your value | |
| Start with pre-visit preparation for one clinic | Clinicians see the value quickly. |
| Measure gaps closed per month | Ties the data to revenue and quality. |
| Retire duplicate record-retrieval vendors | Two feeds cause conflicting records. |
| Deal-breakers | |
| No clean patient roster | |
| Only needs one HL7 interface | |
| No owner for clinical workflow | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Data agreements and roster load | 3-6 weeks |
| 2 | First use case live | 4-8 weeks |
| 3 | Expand use cases | 2-3 months |
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.