Home / Clinical Decision Support & Reference / eCARTv5 Clinical Deterioration Suite (eCART)

eCARTv5 Clinical Deterioration Suite (eCART)

by AgileMD · Based in United States → — AI-powered clinical decision support for better healthcare outcomes and efficiency.

Critical Care Emergency Medicine Hospital Medicine

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Overview

The eCARTv5 Clinical Deterioration Suite (“eCART”) by AgileMD is an AI-powered Software as a Medical Device (SaMD) designed to continuously assess the risk of impending clinical deterioration in hospitalized adult ward patients, specifically predicting death or Intensive Care Unit (ICU) transfer. Integrating directly into the Electronic Health Record (EHR) system, eCART leverages up to 97 real-time variables, including vital signs, laboratory data, and nursing assessments, to generate an eCART score and risk designation. This early warning system aims to maximize early identification of at-risk patients, minimize false alarms, and decrease clinician workload. Beyond prediction, the suite includes embedded clinical pathways to guide medical staff in the evaluation and management of patient care, ensuring timely and appropriate escalation. Developed from over a decade of research, eCART is a cloud-based platform that supports standardized workflows and provides robust reporting and benchmarking capabilities to drive quality improvement.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

AI-powered clinical deterioration prediction
Continuous patient risk assessment (death or ICU transfer)
EHR integration with real-time data (up to 97 variables)
Embedded clinical pathways for care guidance
Early warning system (alerts hours before critical illness)
Risk stratification (eCART score and risk designation)
Minimizes false alarms and clinician workload
Reporting and benchmarking capabilities
Customizable protocols and pathways
Cloud-based SaaS platform

Use Cases

Early identification of at-risk hospitalized patients
Preventing failure to rescue
Improving timely ICU transfers
Reducing clinical variation and standardizing care
Optimizing resource utilization
Guiding medical staff in evaluating and managing patient care

What Physicians Need to Know

Evidence Base

eCARTv5 is a gradient-boosted machine learning model developed from a multi-center dataset of over 900,000 patient admissions, utilizing 97 variables including demographics, vital signs, documentation, and laboratory values. Key variables include respiratory rate, delivered FiO2, systolic blood pressure, and heart rate. It is built upon more than a decade of scientific research and has received FDA clearance for identifying clinical deterioration in hospitalized ward patients.

Clinical Validation Studies

Validated through multicenter retrospective (1.7 million admissions) and prospective (over 200,000 admissions) observational studies across 21 hospitals. eCARTv5 demonstrated superior performance with an AUROC of 0.834-0.835, outperforming eCARTv2 (0.775), NEWS (0.766), and MEWS (0.704). It alerted medical staff a median of 5 hours in advance of clinical deterioration at the high-risk threshold, significantly earlier than NEWS (3 hours) and MEWS (2 hours). Performance remained high across diverse patient demographics and clinical conditions, including COVID-19, sepsis, heart failure, COPD, surgical, and obstetric patients. A clinical trial (NCT05893420) is ongoing to assess its impact on ventilator utilization, length of stay, and mortality.

Alert Fatigue Management

The advanced machine learning model of eCARTv5 is designed to increase detection rates while simultaneously limiting false positive alerts, thereby aiming to decrease alarm fatigue compared to traditional early warning systems like MEWS and NEWS.

Override Rate Data

Not explicitly detailed in public literature specific to eCARTv5.

Drug Interaction Checking

Not a primary feature of this clinical deterioration suite, which focuses on predicting overall patient deterioration.

Differential Diagnosis Support

Not a primary feature; eCARTv5 focuses on risk stratification for imminent death or ICU transfer and guiding next steps in care based on standardized clinical pathways.

Guideline Update Frequency

As a machine learning model, eCARTv5 implies continuous learning or periodic retraining. The clinical pathways it directs users to are typically updated by the implementing institutions.

Clinical Workflow Integration

eCARTv5 is a cloud-based software device designed for seamless integration into the Electronic Health Record (EHR) workflow. It analyzes real-time EHR data and presents a risk score (0-100) to flag at-risk patients. It includes embedded workflow features that enable users to acknowledge risk predictions and record subsequent medical management steps, directing clinical teams towards standardized guidance. Integration feasibility with Health Level Seven (HL-7 V2) messaging standards has been demonstrated.

Decision Audit Trail

Not explicitly detailed in public literature specific to eCARTv5.

Physician Tip

Leverage eCARTv5 as a proactive tool for early identification of clinical deterioration, recognizing its superior predictive accuracy over traditional scores. Integrate its real-time alerts and risk stratification into your daily workflow to prioritize patient assessments and interventions. Remember that while eCARTv5 provides valuable risk assessment and guidance, it serves as a clinical decision support tool and does not replace your independent medical judgment or the standard of care. Focus on the provided standardized guidance for next steps in care for elevated-risk patients.

eCARTv5 is designed for deep integration with Electronic Health Record (EHR) systems, leveraging real-time patient data (vitals, labs, demographics, documentation) via standard interfaces like HL-7 V2. This integration facilitates automated risk stratification and embeds alerts and guidance directly within existing clinical workflows, minimizing disruption and maximizing utility for medical-surgical ward patients.

At a glance

Pricing Contact vendor for tailored quote.

Deployment Cloud Based SaaS, Integrated Directly Into Electronic Health Record (EHR) Systems.

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Details

Category	Clinical Decision Support & Reference, Triage & ER/ICU AI
Pricing	Contact vendor for tailored quote. Pricing details are not publicly listed; prospective customers are encouraged to reach out for a tailored quote based on specific requirements
Deployment	Cloud-based SaaS, integrated directly into Electronic Health Record (EHR) systems.
Compliance
BAA Available	Unknown AI-estimated
HIPAA Compliant	Unknown AI-estimated
FDA Status	1 AI-estimated AgileMD's eCART Clinical Deterioration Suite received 510(k) marketing clearance from the US FDA on June 21, 2024 (K233253) as a Software as a Medical Device (SaMD) for predicting in-hospital clinical deterioration (death or ICU transfer) in adult ward patients.
Integrations
EHR	Not specified
Specialties	Critical Care, Emergency Medicine, Hospital Medicine

What the Web Says

eCARTv5 Clinical Deterioration Suite is an AI-powered software designed to continuously assess hospitalized patients' risk of clinical deterioration, including impending death or transfer to the ICU. It integrates with electronic health records (EHRs) to analyze up to 97 real-time variables, such as vital signs, lab results, and nursing assessments, generating an eCART score and risk designation. The software aims to assist medical staff in early identification and intervention, potentially improving patient outcomes and reducing clinician workload. It has received FDA 510(k) marketing clearance and has been validated through extensive clinical performance data from nearly two million hospitalizations across 21 hospitals.

Overall: Positive

Strengths

AI-driven prediction of clinical deterioration.
Continuously assesses patient risk using real-time data.
Integrates directly into electronic health records (EHRs).
Validated with extensive clinical data across diverse patient demographics and medical conditions.
Minimizes false alarms and reduces clinician workload.
Identifies at-risk patients significantly earlier than traditional methods.

Limitations

No specific cons were found in the provided search results regarding eCARTv5 Clinical Deterioration Suite. Some general concerns about AI in healthcare, such as generalizability across diverse patient populations and settings, were mentioned in the context of the FDA clearance process, but these were addressed by AgileMD's robust testing.

Based on reviews from: HealthAidb, Clinical Trials, Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score - PMC, Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score - PubMed, accessdata.fda.gov, Multicenter Development and Prospective Validation of eCARTv5: A Gradient Boosted Machine Learning Early Warning Score - PubMed, AgileMD, Agiled Review: Run your entire business in one APP! Seriously (Nearly ) EVERYTHING!, Treatment Recommendations for Clinical Deterioration on the Wards: Development and Validation of Machine Learning Models - JMIR AI, Multicenter Development and Validation of eCARTv5: A Machine Learning Early Warning Score | medRxiv, Agiled Reviews 2026: Verified Ratings, Pros & Cons - AppSumo, Agiled Reviews 2026. Verified Reviews, Pros & Cons - Capterra, Agiled: Reviews, Pricing & Free Demo - Software Finder, Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score - ResearchGate, AgileMD receives FDA clearance for eCART to predict in-hospital clinical deterioration across medical conditions - BioSpace, FDA clears Agilemd's AI-driven clinical deterioration tool | BioWorld, Multicenter Development and Prospective Validation of eCARTv5: A Gradient Boosted Machine Learning Early Warning Score | medRxiv

Last updated: 2026-07-18

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Frequently Asked Questions

How does eCARTv5 integrate into my daily clinical workflow, and what specific actions are required from me when an alert is generated?

eCARTv5 is a cloud-based software device that integrates directly into your existing Electronic Health Record (EHR) system, continuously analyzing real-time patient data like vital signs, laboratory results, and demographics. When an alert for potential clinical deterioration is generated, the system directs clinical teams to standardized guidance within the eCART interface to help determine the appropriate next steps in patient care.

What is the evidence supporting eCARTv5's effectiveness in improving patient outcomes and reducing adverse events?

eCARTv5 has demonstrated higher accuracy than traditional early warning scores like MEWS and NEWS2 in predicting outcomes such as acute hospital transfer, ICU admission, and mortality across various patient populations. Studies indicate that eCARTv5 can identify at-risk patients with fewer errors and alert medical staff hours before serious health issues occur, potentially leading to earlier interventions and a reduction in adverse events.

How does eCARTv5 compare to other early warning scores or clinical deterioration tools, and what are its unique advantages or disadvantages?

eCARTv5 consistently outperforms conventional early warning scores such as MEWS and NEWS in predicting clinical deterioration, largely due to its machine learning algorithm that incorporates a broader range of real-time EHR data, including laboratory values. A key advantage is its FDA clearance for identifying deterioration in hospitalized ward patients, although one study noted MEWS had higher accuracy in predicting adverse outcomes within 24 hours of Rapid Response Team (RRT) activation in a specific institutional context.

What are the potential limitations of eCARTv5, such as alert fatigue or false positives, and how does the system mitigate these issues?

While clinical decision support systems generally face challenges with alert fatigue and false positives, eCARTv5 is designed to identify more at-risk patients with fewer errors compared to older tools, aiming to reduce unnecessary alerts. The system provides a comprehensive risk score and trend analysis, offering nuanced information to aid clinical interpretation and potentially mitigate the impact of alert fatigue.

What is the typical implementation timeline and ongoing support structure for eCARTv5, and what impact will it have on physician training and adaptation?

Implementing eCARTv5 involves establishing an EHR connection and data interfaces to transmit necessary patient data, with deployment periods observed to be around 12 months in some studies. Clinical teams, including physicians, receive training on how to interpret the eCART score and utilize the standardized guidance within the interface to adapt to this new decision support tool.

What is the expected impact of eCARTv5 on my clinical decision-making process and overall workload?

eCARTv5 is intended to augment, not replace, clinical judgment by providing additional, real-time information to aid in identifying patients at high risk of deterioration. By combining a machine learning algorithm with clinical pathways, it aims to standardize care for high-risk patients and facilitate timely interventions, potentially reducing the urgent workload associated with managing advanced deterioration.

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