Clinical Deterioration Index (CDI)
by RWJBarnabas Health & Rutgers Health
· Based in United States →
— Predictive AI for early patient decline detection.
Not available publicly
Regulatory Status Disclosed
Overview
The Clinical Deterioration Index (CDI) is a predictive AI algorithm developed by RWJBarnabas Health and Rutgers Health. It continuously analyzes Electronic Medical Record (EMR) data, including vitals, labs, and clinical notes, to identify early signs of patient decline. This enables clinicians to intervene earlier, often 24 hours before visible symptoms emerge, improving patient safety and reducing adverse events. The CDI has been associated with an 18.6% reduction in mortality. The tool is integrated into clinical workflows, calculating a deterioration score every 15 minutes. When a patient’s score reaches a predefined threshold, a Best Practice Advisory (BPA) is triggered in the EHR and on mobile devices, prompting nurses and clinical teams to assess the patient and initiate appropriate interventions. RWJBarnabas Health has implemented Epic’s Deterioration Index across its 12 hospitals, reporting a 15% reduction in inpatient mortality and an estimated 100 lives saved since its systemwide rollout in March 2023. The CDI is designed to augment, not replace, physicians’ individual assessments and clinical decision-making.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Continuous EMR data analysis
- Early identification of patient decline
- Predictive AI algorithm
- Real-time risk scoring (0-100)
- Integration with Epic EHR
- Best Practice Advisory (BPA) alerts
- Mobile device notifications
- Reduced inpatient mortality (18.6% reported)
- Improved patient safety
- Enhanced clinical workflow
Use Cases
- Early intervention for patient deterioration
- Reducing mortality rates in hospitalized patients
- Optimizing rapid response team activations
- Improving situational awareness for care teams
- Supporting clinical decision-making
- Streamlining patient care processes
What Physicians Need to Know
Evidence Base
The Clinical Deterioration Index (CDI) is a predictive AI algorithm that continuously analyzes Electronic Medical Record (EMR) data, including vitals, labs, and clinical notes, to identify early signs of patient decline. It was developed by Epic Systems Corporation and built into its EHR platform. The CDI is a logistic regression model that runs every 15 minutes. The development of the Deterioration Risk Index (DRI), a similar machine learning tool, involved training three separate predictive models for structural heart defect (cardiac), oncology (malignancy), and general diagnostic groups.
Clinical Validation Studies
RWJBarnabas Health conducted a validation phase for the CDI, making adjustments for oversensitivity and developing a three-tiered alert system. A pilot launched in May 2023 at Robert Wood Johnson University Hospital, followed by testing at three additional hospitals. The CDI has been associated with an 18.6% reduction in mortality. Six months into a systemwide rollout, RWJBarnabas Health reported a 15% relative reduction in inpatient mortality, translating to an estimated 100 lives saved based on 2023 data. During the initial pilot, the mortality rate for patients transferred to the ICU also fell by 27%. Another study showed a 64% reduction in Rapid Response Team (RRT) activations when CDI prompted a change in management. The model was prospectively validated on 6232 hospital encounters, showing an area under the curve (AUC) of 0.70 for predicting ICU transfer or RRT event within 6-18 hours. A study involving over 5 million CDI predictions for 13,737 patients reported AUROCs of 0.759 at the observation level and 0.685 at the encounter level.
Alert Fatigue Management
To minimize administrative burden and prevent delays, alerts are sent to the hospital's rapid response team rather than directly to the primary attending or bedside nurse. Red alerts are sent as text messages to rapid response team members' cellphones. The system was designed to only flag patients whose CDI score was higher than 65, which was identified as the optimal cutoff for high-risk patients. The CDI uses a three-tiered alert system: green (risk score 0-30), yellow (31-59), and red (60 or higher). The DRI, a similar tool, achieved 2.3 times fewer alarms per detected event while being 2.4 times more sensitive than existing programs.
Clinical Workflow Integration
The CDI operates in the background of routine care, continuously analyzing EMR data. The score is incorporated into clinical workflows, with the model calculating and storing a score in Epic every 15 minutes. A Best Practice Advisory (BPA) is triggered in the patient's chart when the CDI score reaches or exceeds a threshold. These alerts also appear in mobile applications like Rover and Vocera Vina. When an alert is received, clinicians are guided to assess the patient, conduct a huddle with the clinical team, and use a checklist to review mitigation strategies. This includes documenting actions and decisions in an EHR-based tool. The implementation involved an interdisciplinary committee and significant input from front-line clinicians. Guidelines aligning with the World Health Organization's AI guidance emphasize that AI tools augment, not replace, physician assessment.
Decision Audit Trail
The workflow for managing clinical deterioration following a CDI alert includes documentation using an EHR-based tool that captures actions taken and decisions made during risk-of-deterioration huddles.
Physician Tip
The CDI is a powerful tool for early identification of patient deterioration, often 24 hours before visible symptoms. Utilize the three-tiered alert system (green, yellow, red) to prioritize patient assessments. Red alerts are sent directly to the rapid response team, allowing for immediate intervention. Remember that the CDI augments your clinical judgment; it does not replace it. Engage with the system by reviewing the contributing factors when an alert is triggered to understand the 'why' behind the score. Leverage the CDI to improve communication with nursing staff and facilitate proactive care discussions.
The Clinical Deterioration Index is developed by Epic Systems Corporation and is built into its EHR platform. It continuously analyzes EMR data such as vitals, labs, and clinical notes. The system integrates with mobile applications like Rover and Vocera Vina for alert delivery. RWJBarnabas Health's implementation involved a systemwide Epic rollout.
Details
| Category | Clinical Decision Support & Reference, Triage & ER/ICU AI |
| Pricing | Not available publicly |
| Deployment | Integrated within Epic EHR platform |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status | No AI-estimated |
| Integrations | |
| EHR | Not specified |
| Specialties | Critical Care, Emergency Medicine, Hospital Medicine |
What the Web Says
The Clinical Deterioration Index (CDI) is an AI-powered predictive analytics tool developed by RWJBarnabas Health to identify patients at risk of clinical deterioration up to 12 hours in advance. It integrates with electronic health records (EHRs) to analyze various patient data points, aiming to enable earlier intervention and improve patient outcomes. Reviews suggest it is a promising tool for proactive patient management.
Overall: PositiveStrengths
- Early identification of patient deterioration (up to 12 hours in advance)
- Integrates with existing EHR systems (e.g., Epic)
- Utilizes AI and machine learning for predictive analytics
- Aids in proactive clinical intervention and resource allocation
- Potential to improve patient safety and outcomes
- Developed and validated within a large healthcare system
Limitations
- Requires robust data input and accurate EHR documentation
- Potential for alert fatigue if not finely tuned
- Implementation requires significant IT and clinical workflow adjustments
- Specific cost information is not readily available in public reviews
- Requires ongoing validation and refinement based on real-world performance
- Limited independent, third-party reviews from platforms like G2 or Capterra specifically for CDI as a standalone product
Based on reviews from: RWJBarnabas Health official website, Healthcare IT News, Becker's Hospital Review, HealthLeaders Media, Various medical and healthcare technology publications
Last updated: 2026-09-18
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Press & Coverage
RWJBarnabas Health & Rutgers Health | New Jersey: AI Center of Excellence | AHA
AI Center of Excellence: Mindful Advancement for Safer, Smarter Care
RWJBarnabas Health and Rutgers Health have established an AI Center of Excellence, deploying the Clinical Deterioration Index (CDI) as a predictive AI algorithm. The CDI analyzes EMR data to identify early signs of patient decline, leading to earlier interventions and an 18.6% reduction in mortality.
2025-09
ResearchGate
Proactive Patient Management: Evaluating the Role of Clinical Deterioration Index (CDI) in Deterioration Detection and Reduction of Rapid Response Team (RRT) Utilization
This study prospectively assesses the efficacy of the Clinical Deterioration Index (CDI), an objective score calculated by the EPIC system, in preventing clinical deteriorations and reducing Rapid Response Team (RRT) utilization. The CDI facilitated early detection and intervention, leading to a 64% reduction in RRT activations in patients where management was changed.
2026-06
PMC (JMIR Research Protocols)
Predicting and Responding to Clinical Deterioration in Hospitalized Patients by Using Artificial Intelligence: Protocol for a Mixed Methods, Stepped Wedge Study
This study protocol outlines a mixed-methods approach to assess whether an AI-enabled work system, utilizing the Clinical Deterioration Index (CDI) predictive model, improves clinical outcomes. The CDI, developed by Epic Systems, runs every 15 minutes, analyzing 31 physiological measures to generate a risk score for clinical deterioration.
2021-07
ResearchGate
Evaluation of an AI-Driven Risk Stratification System for Clinical Deterioration in Geriatric Hip Fracture Patients
This research aimed to determine if an AI-driven Clinical Deterioration Index (CDI) could identify geriatric hip-fracture patients at risk of early postoperative complications. An elevated CDI score of u226565 correlated with higher complication rates, prolonged hospital stays, reduced ambulation, and increased one-year mortality.
2026-08
SHM Abstracts | Society of Hospital Medicine
ANALYSIS OF A CLINICAL DETERIORATION PREDICTION MODEL TO GUIDE AN ALERT -AND- RESPONSE SYSTEM DESIGN
This abstract presents an analysis of the Clinical Deterioration Index (CDI), a machine learning model developed by Epic Systems, designed to identify patients at risk of decompensation within 24 hours. The retrospective analysis of rapid response codes at Stanford Hospital indicated that a CDI flag trended towards increased mortality.
2021-05
ResearchGate
Leveraging AI-driven predictive analytics to enhance cognitive assessment and early intervention in STEM learning and health outcomes
This article discusses the broader application of AI-driven predictive analytics, including methodologies transferable to healthcare, such as those used in the Clinical Deterioration Index. It highlights how AI can identify learning difficulties with high accuracy and improve STEM performance and long-term cognitive health outcomes.
2025-08
ResearchGate
A randomized trial of realu2010time automated clinical deterioration alerts sent to a rapid response team
This research explores the effectiveness of real-time automated clinical deterioration alerts, which are conceptually similar to the Clinical Deterioration Index (CDI), when sent to a rapid response team. A recent study mentioned in the article showed that patients receiving a Targeted Real-time Early Warning System (TREWS) alert had reduced mortality and organ failure.
2026-07
Videos
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Frequently Asked Questions
The Clinical Deterioration Index (CDI) is a tool designed to identify patients at risk of clinical decline. It typically integrates with existing electronic health records (EHRs) by pulling real-time patient data (e.g., vital signs, lab results) and applying an algorithm to generate a risk score or alert. This integration aims to provide timely, actionable insights to clinicians at the point of care.
The effectiveness of CDI in improving patient outcomes and reducing adverse events is supported by various studies, which often demonstrate a reduction in unplanned ICU admissions, code blue events, and mortality when CDI is implemented effectively. However, the specific impact can vary depending on the CDI model, the patient population, and the clinical setting.
Common barriers to physician compliance include alert fatigue, lack of trust in the algorithm, integration challenges with existing workflows, and insufficient training. Mitigation strategies involve optimizing alert specificity to reduce false positives, involving physicians in the design and implementation process, providing comprehensive training, and ensuring seamless integration with EHRs to minimize workflow disruption.
CDI often incorporates a broader range of physiological parameters and can utilize more sophisticated algorithms (e.g., machine learning) compared to traditional, simpler Early Warning Scores (EWS) like NEWS2 or MEWS. This can lead to improved accuracy in predicting deterioration, but its clinical utility also depends on factors like ease of use, interpretability, and the specific clinical context.
The costs associated with implementing and maintaining a CDI system can vary widely, encompassing software licensing, integration with existing EHRs, staff training, and ongoing technical support. The potential return on investment (ROI) can be realized through reduced lengths of stay, fewer adverse events, decreased ICU utilization, and improved patient safety, though quantifying this precisely requires careful analysis within each institution.
Key limitations of CDI include potential for alert fatigue if not properly tuned, varying performance across different patient populations (e.g., specific diseases, age groups), and the need for high-quality, consistent data input for accurate predictions. It may also not fully capture rare or atypical presentations of deterioration, requiring clinicians to maintain their independent judgment.
The degree of customization for CDI algorithms varies by vendor and system. Many CDI platforms offer some level of customization, allowing hospitals to adjust parameters, weighting of different clinical variables, and alert thresholds to align with their specific protocols, patient demographics, and clinical priorities. This can help optimize its relevance and reduce unnecessary alerts.
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| Last Verified: 2026-06-19 | First Added: 2026-06-19