cliexaAI - Opioid Use Disorder (OUD) Solution
Predictive Intelligence for Early Opioid Use Disorder Risk Identification
OUD Risk Prediction AI supports earlier identification of patients at elevated risk by analyzing longitudinal prescribing patterns, clinical history, behavioral factors, and care utilization data within a clinical reasoning framework.
Importance of Early Opioid Use Disorder Risk Identification
Opioid related harm remains a major public health challenge. Many patients who later develop opioid use disorder show identifiable risk signals earlier in their care journey.
These signals may include changes in prescribing patterns, co occurring mental health conditions, prior substance use history, and shifts in care utilization. When evaluated independently, these signals can appear limited. When evaluated longitudinally, they reveal meaningful risk trajectories. Early risk identification supports timely intervention, improved care coordination, and prevention focused clinical strategies.
10.1 million
10.1 million Americans misused prescription opioids in the US last year.
$504 billion
Annual societal costs for prescription opioid misuse are estimated at over $504 billion.
1.6 million
1.6 million people in the US had an opioid use disorder.
80,000+ lives
80,000+ lives lost in 2023 alone due to opioid use disorder
Clinical Validation and Performance Results
cliexa’s OUD Risk Prediction AI has been evaluated across clinically meaningful risk tiers to assess performance and reliability.
Greater than 91 percent accuracy in identifying low risk patients
Approximately 82 percent accuracy across four clinically meaningful risk categories
Approximately 78 percent accuracy in identifying high risk patients
OUD Risk Prediction Model Methodology
Longitudinal Analysis of Opioid Prescribing and Care Patterns
The model evaluates patient data across time rather than relying on single encounter assessments. This includes opioid prescribing history, encounter frequency, diagnosis patterns, behavioral health indicators, and care continuity signals.
Longitudinal analysis supports a more accurate understanding of evolving risk.
Clinical and Behavioral Factors Used in OUD Risk Prediction
Risk prediction incorporates a wide range of clinical and behavioral features that are evaluated together. This includes medical history, mental health conditions, prescribing trends, and utilization patterns.
Evaluating these factors collectively supports risk assessment that reflects real patient complexity.
Interpreting OUD Risk Within a Clinical Reasoning Framework
Risk scores are generated within cliexa’s clinical reasoning framework. This approach supports interpretation by contextualizing risk signals and identifying contributing factors.
Clinicians can understand why risk may be increasing and determine appropriate next steps based on clinical context.
How It Works
Designed to identify adults aged 18–80 who are at potential risk for developing opioid use disorder.
Each prediction result includes a four-tier classification (Minimal, Low, Medium, High) with contributing factors clearly displayed. This helps clinicians understand why a patient falls into a given category and act with confidence.
The system integrates directly into EMRs so teams can review risk insights without changing their workflow or adding new dashboards.
Who It’s For
Prescribing clinicians, including physicians, nurse practitioners, and physician assistants, across primary care, pain management, emergency, and behavioral health settings.
Commitment to Responsible AI
Artificial intelligence should never feel like a black box. cliexaAI was built to make every step of its reasoning visible, testable, and clinically defensible. cliexaAI aligns with the principles of fairness, safety, and transparency.
Clinical and Operational Outcomes
Earlier identification of opioid use disorder risk supports more proactive care delivery. Healthcare organizations using predictive risk intelligence have observed:
-
Improved prioritization of patients for monitoring and follow up
-
Reduced time spent on manual chart review
-
Support for prevention focused care programs
-
Enhanced population level risk stratification
Clinical Use Cases
OUD Risk Prediction AI supports a range of clinical and population health use cases, including:
-
Identifying patients who may benefit from closer monitoring
-
Supporting opioid prescribing decisions with longitudinal risk context
-
Informing care coordination and follow up planning
-
Enhancing quality improvement and safety initiatives
-
Supporting preventive outreach and monitoring programs
FAQs
OUD Risk Prediction AI is a healthcare artificial intelligence capability that estimates a patient’s likelihood of developing opioid use disorder by analyzing longitudinal clinical, prescribing, and behavioral data.
The system evaluates patterns across time, including opioid prescribing history, diagnosis trends, mental health indicators, and care utilization signals. These factors are assessed together to identify risk trajectories associated with opioid use disorder.
OUD Risk Prediction AI uses structured and unstructured clinical data such as medication history, encounter frequency, diagnosis codes, behavioral health indicators, and continuity of care signals to support risk assessment.
Risk scores are designed to support clinical awareness and decision making. They help clinicians identify patients who may benefit from closer monitoring, follow up, or preventive interventions while preserving clinical judgment.
No. OUD Risk Prediction AI does not provide a diagnosis. It estimates risk and supports early identification so clinicians can determine appropriate next steps based on clinical evaluation.
By identifying elevated risk earlier in the care journey, the system supports prevention focused strategies such as proactive monitoring, care coordination, and timely intervention before escalation occurs.
Yes. cliexa’s OUD Risk Prediction AI has been evaluated across clinically meaningful risk tiers and has demonstrated strong performance in identifying low, moderate, and high risk patients.
Yes. The model is designed to adapt to different patient populations and care settings through longitudinal analysis and continuous learning.
The system evaluates how multiple clinical and behavioral factors interact over time. This supports risk assessment that reflects real world patient complexity rather than isolated conditions.
Risk outputs are intended for use by qualified care teams and are accompanied by contextual information to support interpretation. The system emphasizes transparency, clinical oversight, and responsible use aligned with patient centered care practices.
Transform your approach to opioid risk. Start with the facts.
See how cliexaAI’s qualified framework supports early detection, compliance, and operational efficiency.
Disclaimer
Mayo Clinic does not endorse or warrant the third-party products or services made available through Mayo Clinic Platform, including their functionality, quality, or performance. Mayo Clinic expressly disclaims any express or implied warranties on such third-party products or services, including any implied warranties of merchantability, quality, accuracy, fitness for a particular purpose, or noninfringement.