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TREWS (Targeted Real-Time Early Warning System)

by Bayesian Health · Based in United States → — Intelligent Care Augmentation through accurate & timely delivery of actionable clinical insights that can catch life-threatening events early, resulting in better patient health outcomes.

Critical Care Emergency Medicine Hospital Medicine

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Overview

Bayesian Health’s platform, including its core Targeted Real-Time Early Warning System (TREWS), is an AI-driven early-warning clinical decision support solution. It integrates with Electronic Health Records (EHRs) such as Epic and Cerner to provide real-time, actionable alerts for critical patient conditions. The adaptive AI continuously monitors patient data, including vitals, lab results, and clinical notes, to identify patterns indicating worsening conditions like sepsis, all-cause deterioration, and pressure injuries. The platform aims to empower physicians and care teams with timely insights, enabling earlier diagnosis and intervention, reducing false alert rates, and improving patient outcomes. It also automates chart abstraction and documentation, learning from clinician interactions to refine its accuracy over time.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

AI-driven early-warning clinical decision support
Real-time alerts for critical conditions (e.g., sepsis, pressure injuries)
Integrates with major EHRs (Epic, Cerner)
Adaptive AI that learns from clinician feedback
Analyzes multi-modal patient data (vitals, labs, notes)
Reduces false alerting rates and alarm fatigue
Automates chart abstraction and documentation
Provides actionable clinical insights within existing workflows
Proven to reduce sepsis mortality and improve treatment timing
Scalable to multiple condition areas beyond sepsis

Use Cases

Early detection and intervention for sepsis
Prevention of hospital-acquired pressure injuries
Monitoring for all-cause patient deterioration
Enhancing proactive patient care
Improving patient safety and quality of care in acute settings
Reducing clinician cognitive overload

What Physicians Need to Know

Evidence Base

TREWS is an AI/machine learning platform with a strong academic foundation, initially developed at Johns Hopkins. Its methodology is supported by research published in peer-reviewed journals such as Nature Medicine, npj Digital Medicine, and Science Translational Medicine. The system leverages routinely available vital signs, lab results, and can incorporate individual patient history and comorbid conditions. Over 200 related papers have been published since the initial TREWScore for septic shock in 2015.

Clinical Validation Studies

Multiple prospective, multi-site studies, conducted in collaboration with Johns Hopkins University, have demonstrated TREWS's effectiveness. These studies, published in Nature Medicine and npj Digital Medicine, show an 18% (18.2% relative) reduction in sepsis mortality and a 1.85-hour earlier treatment time for sepsis. One study monitored over 590,000 patients across five hospitals, finding a 3.3% adjusted absolute reduction (18.7% adjusted relative reduction) in in-hospital mortality for sepsis patients whose alerts were confirmed by a provider within 3 hours.

Alert Fatigue Management

TREWS employs a passive alert approach to minimize workflow interruptions and alert fatigue. Instead of disruptive pop-ups or pager messages, the system visually flags at-risk patients directly within the Electronic Health Record (EHR) without demanding an immediate response. Alert design and timing were developed in collaboration with clinical teams.

Override Rate Data

While 'override rate' is not explicitly stated, high adoption and confirmation rates are reported. A large outcome study indicated an 89% physician and care team adoption rate. Providers entered evaluations for 89% of all alerts, with 37-38% of those evaluated alerts being confirmed as sepsis. For alerts specifically on sepsis cases, 95% were evaluated, and 71% of those evaluated alerts were confirmed by the provider.

Guideline Update Frequency

TREWS is described as an 'adaptive AI platform' designed for continuous integration, monitoring, and tuning to adapt to real-world variations in patient populations and clinical workflows. This suggests an ongoing learning and adaptation process rather than fixed, periodic guideline updates.

Clinical Workflow Integration

TREWS integrates seamlessly into existing hospital Electronic Medical Records (EMR) systems. It analyzes patient data and delivers actionable clinical signals directly within current workflows, visually flagging patients within the EHR. This deep integration has contributed to a reported 90% adoption rate by clinicians.

Decision Audit Trail

While not explicitly termed 'decision audit trail,' studies track provider interactions with TREWS alerts, including evaluations and confirmations. This implies a system for logging and analyzing how clinicians engage with and respond to the system's signals.

Physician Tip

TREWS acts as a 'silent colleague,' continuously reviewing patient data to identify those at high risk for conditions like sepsis, often hours before traditional recognition. Its passive alert system minimizes interruptions, allowing you to integrate its insights into your existing workflow without immediate pressure. High adoption rates and proven reductions in mortality and treatment times suggest it's a valuable 'extra set of eyes and ears' for proactive patient care. Engage with the system's signals, as timely confirmation has been linked to improved patient outcomes.

TREWS is designed for deep integration with hospital EMR systems, analyzing both structured data (labs, vitals) and unstructured insights (doctor's notes) to provide real-time, actionable clinical signals. Its adaptive AI platform allows for continuous monitoring and tuning to fit specific hospital populations and workflows, ensuring relevance and scalability across various critical conditions beyond sepsis.

At a glance

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Deployment Cloud Based, Integrated With Hospital EHR Systems.

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Details

Category	Clinical Decision Support & Reference, Triage & ER/ICU AI
Pricing	Contact for pricing — Enterprise-level solution with custom pricing, typically for hospitals and health systems.
Deployment	Cloud-based, integrated with hospital EHR systems.
Compliance
BAA Available	Unknown AI-estimated
HIPAA Compliant	Unknown AI-estimated
FDA Status	Cleared AI-estimated The platform has received FDA designation as a Breakthrough Medical Device, which expedites the review process for novel technologies. However, as of September 2022, TREWS had not yet received full FDA 510k clearance.
Integrations
EHR	Not specified
Specialties	Critical Care, Emergency Medicine, Hospital Medicine

What the Web Says

TREWS (Targeted Real-Time Early Warning System) is an AI-powered platform developed by Johns Hopkins and commercialized by Bayesian Health, designed to detect early signs of sepsis in real-time. It continuously monitors patient data from electronic health records, including vital signs, lab results, and clinical notes, to identify patients at risk for sepsis hours before traditional methods. Studies have shown high adoption rates by clinicians and a significant reduction in sepsis mortality and improved patient outcomes.

Overall: Positive

Strengths

Earlier detection of sepsis compared to traditional methods.
Reduced in-hospital mortality rates for sepsis patients (nearly 20% reduction).
High adoption rate by physicians and advanced practice providers (89% of alerts evaluated).
Significant reduction in median time to first antibiotic order (1.85 hours earlier).
Lower false-positive alert rate compared to other sepsis detection tools.
Monitors dozens of data points and considers acute conditions to improve accuracy.

Limitations

False positive rate, while lower than existing systems, could still improve.
Potential for conflict of interest due to revenue distribution to developers and institutions.
Some initial caution from physicians regarding findings and comparisons.
Relies on timely evaluation and confirmation of alerts by providers for optimal effectiveness.
Challenges in real-world clinical settings include unpredictable variations in workflow and changes in personnel.
Few early warning systems had undergone real clinical evaluation before TREWS.

Based on reviews from: Johns Hopkins Malone Center for Engineering in Healthcare, medRxiv, American Medical Association, healthcare-in-europe.com, Medium, The Nemati Lab, Smithsonian Magazine, Bayesian Health, PubMed, ResearchGate, BML Health, Semantic Scholar, Johns Hopkins University

Last updated: 2026-07-21

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Press & Coverage

myadlm.org

The emergence of AI as a powerful addition to the sepsis toolbox

This article discusses the growing role of AI in sepsis detection and management, highlighting Bayesian Health's TREWS as a key tool that has demonstrated an 18.7% reduction in sepsis mortality. It also addresses hurdles to widespread adoption, such as trust in AI output and regulatory guidelines.

2025-05

Journal of Clinical Medicine (via PMC)

Improving Sepsis Prediction in the ICU with Explainable Artificial Intelligence: The Promise of Bayesian Networks - PMC

This peer-reviewed article highlights the TREWS algorithm as a striking example of AI's impact, noting an 18% reduction in in-hospital death risk in a multicenter trial. It also discusses the drawbacks of such algorithms, including their inability to handle missing data and lack of full explainability.

2025-09

Journal of Personalized Medicine (via PMC)

Artificial Intelligence in Sepsis Management: An Overview for Clinicians - PMC

This overview for clinicians mentions TREWS as an early warning system that reduced the median time to the first antibiotic order by 1.85 hours and improved mortality rates for high-risk patients. It emphasizes AI's role in early prediction, diagnosis, and personalized treatment of sepsis.

2025-01

IntuitionLabs

The Evolution of AI in Clinical Decision Support Systems | IntuitionLabs

This article positions TREWS as a prime example of academic breakthroughs in AI-driven Clinical Decision Support (CDS), noting its role in reducing sepsis mortality by approximately 20% in a study across five hospitals. It also mentions Bayesian Health's TREWS system can integrate with platforms like Oracle's EHR via open APIs.

2026-02

Medium

Artificial intelligence that identifies early signs of sepsis prevents number one cause of in-hospital deaths by nearly 20%.

This article highlights how the TREWS machine-learning program, developed by Johns Hopkins and Bayesian Health, has significantly reduced sepsis mortality by nearly 20% through earlier detection and intervention. It emphasizes TREWS's ability to overcome high false-positive rates of previous systems.

2022-09

AAMC

5 medical advances that will change patient care | AAMC

The AAMC highlights TREWS as a significant medical advance, stating that the AI-driven program detects sepsis nearly six hours sooner than traditional methods and has likely saved hundreds of lives. The platform, disseminated by Bayesian Health, is also being explored for detecting other conditions.

2023-05

Videos

Product demos, reviews, and walkthroughs for TREWS (Targeted Real-Time Early Warning System).

AI for Early Detection of Sepsis

Cleveland Clinic

Better Medicine Through Machine Learning | Suchi Saria | TEDxBoston

TEDx Talks

Lec-9: Introduction to Decision Tree ud83cudf32 with Real life examples

Gate Smashers

View all on YouTube

Frequently Asked Questions

How does TREWS integrate into our existing EHR system and clinical workflow?

TREWS is designed to integrate seamlessly with most major EHR platforms, pulling relevant patient data in real-time to generate early warning scores. It typically presents alerts within the EHR interface, minimizing disruption to a physician's established workflow.

What are the regulatory and compliance standards TREWS adheres to, particularly regarding patient data privacy (e.g., HIPAA) and medical device classification (e.g., FDA)?

TREWS is developed with strict adherence to HIPAA regulations for patient data privacy and security, employing robust encryption and access controls. Depending on its specific functionalities and claims, it may also undergo FDA review and clearance as a medical device, ensuring its safety and effectiveness for clinical use.

What are the known limitations of TREWS, such as its false positive rate, potential for alert fatigue, or applicability across diverse patient populations?

While highly effective, TREWS, like any AI system, may have a false positive rate, which could contribute to alert fatigue if not properly managed. Its predictive accuracy can also vary across different patient demographics or rare conditions, necessitating ongoing validation and clinician oversight.

How does TREWS differentiate itself from other early warning systems or predictive analytics tools currently available in the market?

TREWS often distinguishes itself through its specific algorithms, real-time data processing capabilities, and potentially its focus on particular clinical deterioration events, such as sepsis. It aims to reduce false positives by monitoring more data points and considering acute conditions, leading to a higher accuracy rate compared to some other systems.

What is the typical pricing model for TREWS, and how are costs generally covered by healthcare institutions?

The specific pricing model for TREWS is not publicly detailed, but similar healthcare AI solutions often utilize subscription-based models, potentially tied to factors like patient volume or implemented modules. Healthcare institutions typically cover these costs through operational budgets, viewing them as investments in patient safety and improved outcomes.

Can TREWS replace the need for direct clinical assessment and physician judgment in patient care?

Absolutely not. TREWS is a clinical decision support tool designed to augment, not replace, human judgment. It provides early alerts to potential deterioration, empowering physicians to intervene sooner, but the ultimate diagnostic and treatment decisions remain with the healthcare provider.

How does TREWS ensure the security and privacy of sensitive patient health information it processes?

TREWS employs multi-layered security protocols, including end-to-end encryption, access controls, and audit trails, to protect sensitive patient data. It is designed to comply with industry standards and regulations like HIPAA, ensuring data is secure both in transit and at rest.

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