- No public price.
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Bayesian Health
by Bayesian Health · US
Sepsis flagging software that monitors EHR data continuously and shows a high-risk flag in the chart. FDA 510(k) cleared April 2026.
On this page
- Regulatory & Compliance18/28
FDA cleared (1 K-number on record)
- Clinical Integration0/26
No EHR integrations listed
- Evidence Strength8.4/30
1 peer-reviewed paper
- Vendor & Market6/18
Early-stage company
- Sentiment & Transparency1.3/11.5
Pricing on request, no public amounts
▸ Show all 11 dimensions▾ Hide dimension detail
- FDA clearance18/18
FDA cleared (1 K-number on record)
- HIPAA / SOC2 / BAA0/10
No public HIPAA/SOC2/BAA attestation
- EHR integrations (count)0/14
No EHR integrations listed
- Top-3 EHR coverage (Epic / Oracle / Athena)0/8
None of the top-3 EHRs covered
- Bidirectional write-back0/4
No bidirectional write-back documented
- Peer-reviewed papers8/21
1 peer-reviewed paper
- RCT / meta-analysis / systematic review0/9
No RCT, meta-analysis, or systematic review
- Funding & adoption signal6/12
Early-stage company
- Years in market0/6
Founded year not recorded
- Clinician sentiment (Reddit)0/9
No clinician sentiment data available
- Pricing transparency1/3
Pricing on request, no public amounts
Sepsis flagging software that monitors EHR data continuously and shows a high-risk flag in the chart. FDA 510(k) cleared April 2026.
No public price.
Bottom line
Bayesian Health sells a hospital platform that watches EHR data continuously and flags patients who need attention. Its sepsis flagging device received FDA 510(k) clearance in April 2026, and the company calls it the first continuous AI sepsis monitor to get one. It fits a hospital with an established sepsis programme and the EHR build capacity to act on a flag. A hospital without both should wait, and any buyer should treat the outcome claims, which are the vendor's and come from observational data, as hypotheses to test locally.
What is Bayesian Health and who sells it?
Bayesian Health Inc. is based at 2 River Terrace, New York, according to the FDA clearance letter. It is a Johns Hopkins spinout led by Suchi Saria, PhD, who is founder and CEO. The company's homepage describes a real-time clinical intelligence platform that monitors every patient continuously. Target areas beyond sepsis include readmissions, deterioration, palliative care, transitions of care, heart failure, hospital-acquired pressure injuries and malnutrition.
The sepsis feature descends from TREWS, a system built at Johns Hopkins, according to press coverage of the clearance. The company's own site does not mention TREWS. Healthcare Brew describes Cleveland Clinic as an investor or supporter, without an amount.
What exactly did the FDA clear?
The device is the Bayesian Health Sepsis Flagging Device, K250680, cleared on 30 April 2026 through a Traditional 510(k) (Class II, 21 CFR 880.6316, product code SAK). The company announced it on 12 May 2026, after an earlier Breakthrough Device Designation. The indication is continuous monitoring software to aid early detection or risk prediction of sepsis developing within 24 hours. It analyses EHR data and shows a "Sepsis Risk High" flag in the EHR, for adults 18 and over, and it must not be the sole basis for diagnosis.
The predicate was Prenosis Sepsis ImmunoScore, authorised through De Novo (DEN230036) in 2024. Bayesian's "first" therefore rests on the company's own wording: first continuous monitor with a 510(k).
Bayesian describes EHR integration as native and says it works within "leading EHR systems", naming Epic only as a comparison. It lists no live EHR sites, and no HIPAA, SOC 2 or HITRUST attestation.
What does the evidence say?
The key study is a 2022 Nature Medicine paper by Adams and colleagues, a prospective multi-site study of the TREWS system covering 764,707 encounters at five hospitals. The press release says alerts came 5.7 hours earlier, sensitivity was 82%, and in-hospital mortality was 18% lower when alerts were acted on. Those are the company's figures. The design was observational rather than a randomized trial, and whether the cleared device differs from the TREWS version in that paper is unknown.
The homepage adds adoption and outcome claims across real-world customers, including 81% adoption, a 5.5% absolute mortality reduction and 1.9 days shorter stay. It also claims alert volume 20 times lower than Epic's sepsis algorithm at one customer. All of it is vendor marketing. The only PubMed paper linked to the company, a 2022 article in Med on lessons from deploying sepsis machine-learning models (PMID 36087573), comes from the TREWS lineage and is not a trial of the cleared device.
How much does Bayesian Health cost?
Bayesian lists no price. A CMS New Technology Add-on Payment under the FY2027 IPPS final rule began on 1 October 2026, covering up to $61.84 per eligible Medicare fee-for-service inpatient case for up to three years (company release, 2 September 2026). Medicare pays that to the hospital. The hospital's own contract fee is a separate number.
How does it compare?
Prenosis Sepsis ImmunoScore is the cleared peer. Dascena, CLEW, Philips and Cerner offer sepsis tools too, and Epic has its own sepsis model.
The verdict
A hospital that already tracks sepsis bundle compliance has the baseline to run a before-and-after evaluation. Before committing, ask how many hospitals are live and what the alert-to-patient ratio is at a site like yours.
Editorial review last generated October 15, 2026. Synthesized from clinician sentiment, peer-reviewed coverage, and our editorial silo picks. Refined by hand where vendor facts change.
What it costs
All tiers are custom-quoted. The vendor does not publish per-provider list pricing.
| Tier | Monthly | Annual | Notes |
|---|---|---|---|
| Enterprise | — | — | No public price. |
Source: vendor pricing page. Last checked October 15, 2026.
What deploys cleanly
Carries FDA 510(k) per vendor documentation. Independent attestation review is the buyer's responsibility before clinical deployment.
What the literature says
1 peer-reviewed study indexed on PubMed evaluate Bayesian Health in clinical contexts. The most relevant are shown below, ranked by editorial relevance score combining title match, study design, recency, and journal tier.
- Lessons in machine learning model deployment learned from sepsis.
- Lyons PG, Singh K· Med· 2022
- In three recent and related publications, researchers from Johns Hopkins University and Bayesian Health report results from implementing and prospectively evaluating the Targeted Real-time Early Warning System (TREWS) for sepsis at five hospitals..


