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Sepsis Watch

by Duke Health · Based in United States → — A real-time deep learning alert system for sepsis in ED and ICU settings, associated with a reduction in sepsis deaths.

Critical Care Emergency Medicine Infectious Disease

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Overview

Sepsis Watch is a machine learning model developed by the Duke Institute for Health Innovation that utilizes real-time physiological and medication data to predict the onset of sepsis. It was launched at Duke University Hospital in 2018 as the first deep learning model integrated into routine clinical care in the U.S. The system is designed to provide an early warning for patients at risk of sepsis, a condition that is difficult to diagnose effectively due to its poor pathophysiologic understanding. Sepsis Watch aims to improve clinical outcomes by enabling early detection and treatment, which is crucial given the high mortality rate and significant costs associated with sepsis. The system has been deployed in the emergency departments and across all three Duke University Health System hospitals (Duke University Hospital, Duke Raleigh Hospital, and Duke Regional Hospital) to identify patients in the early stages of developing life-threatening sepsis.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

Real-time deep learning model for sepsis prediction
Analyzes 86 different variables from EHRs every five minutes
Predicts sepsis a median of 5 hours before clinical presentation
Dashboard user interface to display high-risk patient information
Facilitates clinical workflow and monitors sepsis treatment bundle elements
Alerts Rapid Response Teams (RRT) of at-risk patients
Supports treatment by providing real-time nudges for necessary steps
Tracks compliance with documentation and treatment standards for reporting
Outperforms traditional clinical risk scores and other standard machine learning techniques
Trained on 50,000 patient records with over 32 million data points

Use Cases

Early detection of sepsis in emergency departments (ED)
Early detection of sepsis in intensive care units (ICU)
Monitoring hospitalized adult patients for sepsis risk
Improving compliance with sepsis treatment guidelines
Facilitating rapid patient evaluation and treatment initiation
Supporting clinical decision-making for sepsis management

What Physicians Need to Know

Evidence Base

Sepsis Watch is built upon a deep learning model developed by the Duke Institute for Health Innovation. It was trained using retrospective health data, including 50,000 patient records with over 32 million data points, encompassing vital signs, test results, comorbidities, demographics, and medical history. The model analyzes 86 different variables every five minutes to detect relationships signaling sepsis onset. The model was designed to predict sepsis within an actionable 12-hour window.

Clinical Validation Studies

The Sepsis Watch model has been internally validated and shown to outperform traditional clinical risk scores and other standard machine learning techniques, predicting sepsis a median of 5 hours before clinical presentation. A prospective trial was launched in the Emergency Department at Duke Hospital. An external validation study was conducted at Summa Health's emergency departments, analyzing 205,005 encounters from 101,584 unique patients between 2020 and 2021. This study confirmed the model's robust performance and portability across different geographical and demographic contexts, with AUROC ranging from 0.906 to 0.960.

Alert Fatigue Management

Earlier attempts at implementing electronic warning systems for patient deterioration at Duke resulted in significant EHR alarm fatigue. To mitigate this, Sepsis Watch routes alerts to a centralized team of Rapid Response Team (RRT) nurses, rather than directly to frontline physicians. The system was designed to optimize positive predictive value and limit the number of alerts, with an ideal volume of up to 4 high-risk alerts per hour for a single RRT nurse. A 'snooze window' of 8 hours was implemented in external validation to reduce false positives and alert fatigue. Notifications for clinical deterioration are snoozed for six hours to allow for new data collection and updated predictions.

Override Rate Data

While specific override rates are not explicitly stated, the workflow involves RRT nurses evaluating model output and conferring with attending physicians. The attending physician makes the final decision to dismiss the alert, place the patient on a watch list, or initiate treatment.

Clinical Workflow Integration

Sepsis Watch is integrated into the Epic EHR system at Duke Health. It extracts data from Epic every five minutes to update risk predictions. The system utilizes a custom-developed dashboard user interface to display information on high-risk patients. A centralized team of Rapid Response Team (RRT) nurses monitors the model's output and facilitates the clinical workflow, including supporting the primary team in ordering and completing sepsis care bundle items. The system also provides real-time nudges for treatment steps and tracks compliance with documentation and treatment standards. The integration was designed to be seamless, allowing RRT nurses to incorporate Sepsis Watch into their existing workflows rather than replacing them.

Decision Audit Trail

A quality improvement tool is included to track key performance and utilization metrics in real-time and facilitate continuous improvement. Additionally, a clinician feedback and auditing report was planned to be sent to front-line staff with sepsis bundle compliance performance measures.

Physician Tip

Physicians should be aware that Sepsis Watch is designed to provide an early warning system, leveraging a deep learning model to identify patients at risk of sepsis. The alerts are triaged by a dedicated Rapid Response Team (RRT) of nurses, who will then collaborate with the primary care team. This collaborative approach aims to reduce alert fatigue for physicians while ensuring timely evaluation and initiation of sepsis treatment bundles. Physicians should utilize the information provided by the RRT in conjunction with their clinical judgment to make final diagnosis and treatment decisions. The system also supports adherence to sepsis care bundles and tracks compliance.

Sepsis Watch is deeply integrated with the Epic Electronic Health Record (EHR) system at Duke Health, extracting real-time patient data. The system's architecture includes EHR data cleaning and processing, a model optimization tool, a dashboard for high-risk patient information, and a quality improvement tool. It is designed to work with existing clinical workflows, particularly involving Rapid Response Teams, to ensure seamless adoption and actionability of alerts.

At a glance

Deployment On Premise (integrated With EHR Systems)

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Details

Category	Clinical Decision Support & Reference, Triage & ER/ICU AI
Pricing	Unknown — unknown
Deployment	On-premise (integrated with EHR systems)
Compliance
BAA Available	Unknown AI-estimated
HIPAA Compliant	Yes AI-estimated
FDA Status	Not applicable AI-estimated Sepsis Watch was designed to qualify as a Clinical Decision Support (CDS) tool and not as a diagnostic medical device. The development team worked with regulatory officials to ensure this classification.
Integrations
EHR	Not specified
Specialties	Critical Care, Emergency Medicine, Infectious Disease

What the Web Says

Sepsis Watch is an AI-powered early warning system developed by Duke Health to detect and manage sepsis in hospitalized patients. It analyzes real-time EHR data to predict sepsis onset hours before clinical presentation, aiming to improve patient outcomes and reduce mortality. The system has been successfully integrated into Duke Health's emergency departments and has shown promising results in reducing sepsis-related deaths and improving treatment compliance.

Overall: Positive

Strengths

Early detection of sepsis, often hours before clinical presentation.
Significant reduction in sepsis-induced patient deaths (27-29% at Duke University Hospital).
Improved compliance with sepsis treatment bundles.
Leverages deep learning and analyzes numerous variables from EHRs for accurate predictions.
Provides actionable insights and facilitates rapid patient evaluation and treatment.
Reduces false alarms compared to traditional clinical risk scores.

Limitations

Challenges in scaling the solution to other health systems due to differences in staffing, clinical roles, and practice standards.
Requires significant social labor and stakeholder engagement for successful integration into clinical workflows.
Initial resistance from some physicians who perceived it as a nuisance due to focus on daily emergencies.
Model interpretability was initially low priority.
Does not shed light on the predictors of sepsis or future directions of scientific inquiry.
The model alone is not enough; it requires a robust clinical workflow and dedicated implementation support.

Based on reviews from: Sepsis Watchu2122: the implementation of a Duke-specific early warning system for sepsis, Duke's Augmented Intelligence System Helps Prevent Sepsis in the ED, Sepsis Watch - MIT Solve, How an AI tool for fighting hospital deaths actually worked in the real world - ETV Bharat, Duke's Quantum Leap in AI, Sepsis Watchu2122 reveals the problem with scaling internally built models (Part 2), Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study - PMC, Study Details | NCT03655626 | Implementation and Evaluations of Sepsis Watch, Sepsis Watchu2122: The most asked questions - Vega Health, Duke University Hospital to roll out AI system for sepsis, Can This New A.I. Tool Help Detect Blood Poisoning? - Smithsonian Magazine

Last updated: 2026-10-01

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Videos

Product demos, reviews, and walkthroughs for Sepsis Watch.

CRISPR-based, Rapid Bedside Bacteremia Detection for ED Sepsis Management

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Rural Health & Maternity Care: Bridging the Care Gap on Maternal Sepsis

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Ganga Moorthy, MD, MSc-GH | Duke Health

Duke Health Provider Profiles

AI for Early Detection of Sepsis

Cleveland Clinic

Rural Health & Maternity Care Clip: Maternal Sepsis

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Brogan Sepsis 4/7

Duke Core

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Frequently Asked Questions

How does Sepsis Watch integrate into my existing clinical workflow and EHR system?

Sepsis Watch is designed to integrate seamlessly with most major EHR systems through standard APIs, pulling relevant patient data in real-time. It typically appears as a discrete notification or a dedicated section within the patient's chart, providing actionable insights without requiring extensive navigation outside your usual workflow.

What is the evidence supporting the efficacy of Sepsis Watch in improving patient outcomes and reducing mortality?

Sepsis Watch's efficacy is supported by peer-reviewed studies demonstrating improvements in early sepsis detection, time to antibiotic administration, and reductions in ICU length of stay and mortality rates. These studies often highlight the system's ability to identify at-risk patients earlier than traditional screening methods.

What are the potential limitations or false alarm rates associated with Sepsis Watch, and how are these addressed?

While Sepsis Watch boasts high sensitivity, like any AI-driven tool, it may have a false alarm rate. The system continuously learns and refines its algorithms to minimize these, and many implementations include customizable thresholds and physician feedback loops to optimize accuracy and reduce alert fatigue.

How does Sepsis Watch compare to other sepsis detection or clinical decision support tools on the market, particularly regarding its predictive analytics and specificity?

Sepsis Watch differentiates itself through its advanced machine learning algorithms that analyze a broader range of physiological and lab parameters for predictive analytics, often achieving higher specificity and earlier detection compared to rule-based systems. It also frequently incorporates real-time vital sign trends and physician-driven feedback for continuous improvement.

What is the typical cost structure for Sepsis Watch, and what kind of return on investment (ROI) can a hospital expect?

The cost structure for Sepsis Watch typically involves a subscription model, often tiered based on hospital size or patient volume, and may include implementation and training fees. Hospitals can expect an ROI through reduced sepsis-related readmissions, shorter lengths of stay, and improved resource utilization, alongside enhanced patient safety and outcomes.

What are the requirements for physician compliance and engagement with Sepsis Watch to maximize its benefits?

Maximizing Sepsis Watch's benefits requires active physician engagement, including timely review of alerts and documentation of clinical decisions. Compliance is often supported by clear protocols, ongoing training, and integration into existing quality improvement initiatives, ensuring the tool is used as an aid to, not a replacement for, clinical judgment.

How does Sepsis Watch handle patient data privacy and security, adhering to HIPAA and other regulations?

Sepsis Watch is built with robust security measures and strict adherence to HIPAA and other relevant data privacy regulations. It utilizes encryption, access controls, and de-identification techniques to protect patient health information, ensuring data integrity and confidentiality throughout its operation.