Carenostics vs Navina
Both read the record to tell a clinician something they did not know about the patient in front of them, and the difference is what happens next. Carenostics finds people who already have chronic disease that nobody has diagnosed, and the output is a clinical finding that should lead to a workup. Navina assembles a consolidated patient portrait at the point of care from the record, exchanges, claims and care gap files, surfacing suspected conditions with the evidence attached, and the output frequently ends in documentation and risk adjustment as well as care. Both are legitimate. The distinction matters because in a demonstration they look identical. Ask Carenostics what proportion of surfaced patients get diagnosed and treated, and ask Navina what proportion of suspected conditions clinicians confirm. The two answers describe different products.
- It surfaces undiagnosed chronic disease from existing record data, which is a detection problem rather than a documentation one, and it owns the model doing it.
- The target is a patient whose disease is present and unrecorded, so the output is a clinical finding rather than a coding opportunity.
- Model ownership means accuracy questions have a single accountable party rather than routing to a partner.
- It assembles a consolidated patient portrait at the point of care from the record, exchanges, claims and care gap files, with the evidence attached to every insight.
- Named and typed certifications, plus native bidirectional integration, mean the insight reaches the clinician inside the appointment rather than in a worklist afterwards.
- For a risk bearing group, working the whole panel through the visit is broader than detecting one class of disease.
Side by Side
| Axis | C Carenostics |
N Navina |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model and Technology Transparency | ||
| Clinical and Operational Evidence | ||
| AI Safety and PHI Stewardship | ||
| HIPAA and BAA Posture | ||
| Security Certifications and Trust Center | ||
| FDA and Regulatory Status | ||
| AI Governance and Bias Disclosure | ||
| EHR and Interoperability Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Setting and Specialty Coverage |
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Clinical Decision Support page.
The incentive difference between these two is worth stating precisely. Detecting undiagnosed disease and surfacing suspected conditions for documentation look similar in a demonstration and are not the same product: one ends in a diagnosis and a treatment plan, the other can end in a code. Ask Navina what proportion of surfaced suspected conditions clinicians confirm, and ask Carenostics what proportion of surfaced patients are subsequently diagnosed and treated. Neither publishes a subgroup analysis. Carenostics publishes no retention or secondary use position; Navina publishes no hosting, residency or governance framework.