AI for Substance Use and Overdose Prevention
AI shows genuine potential for identifying at-risk individuals and optimizing treatment, with a 2025 Nature Medicine trial demonstrating that EHR-based AI screening reduced 30-day hospital readmissions by 47%. PDMP-integrated models achieve 0.86 AUC for overdose prediction, and wastewater surveillance provides population-level drug trend data without individual privacy concerns. However, implementation faces unique barriers: 42 CFR Part 2 privacy regulations stricter than HIPAA, stigma amplifying harm from false positives, and treatment capacity constraints where identification means nothing without available treatment slots.
This chapter examines AI applications across the substance use continuum. You will learn to:
- Evaluate EHR-based risk models for opioid use disorder identification
- Understand PDMP-integrated AI tools and their validated accuracy metrics
- Assess digital therapeutics for addiction treatment and their evidence base
- Navigate 42 CFR Part 2 privacy requirements for substance use data
- Distinguish demonstrated capabilities from theoretical applications
- Identify implementation barriers specific to addiction medicine contexts
- Apply surveillance AI for real-time overdose spike detection
Prerequisites: Machine Learning Fundamentals, The Data Problem, Ethics, Bias, and Equity.
Introduction
The overdose crisis represents one of public health’s most urgent failures. Despite decades of intervention efforts, drug overdose deaths have more than tripled since 2000, surpassing peak annual HIV/AIDS deaths. The problem has evolved faster than the response: prescription opioids gave way to heroin, then fentanyl, and now synthetic opioids contaminate nearly every illicit drug supply.
AI enters this landscape with both promise and peril. Machine learning models can identify at-risk individuals before they experience fatal overdose, predict treatment discontinuation, and detect community-level drug trends in real time. A 2025 clinical trial demonstrated that AI-prompted addiction consultations reduced hospital readmissions by 47%, providing concrete evidence that these tools can work.
However, substance use AI faces unique implementation barriers. Privacy regulations (42 CFR Part 2) exceed standard HIPAA protections, fragmenting the data needed for training. Stigma amplifies harm from false positives, where a misclassification can trigger treatment denial or criminal justice involvement. Most critically, treatment capacity constraints mean that identifying high-risk patients accomplishes nothing if no treatment slots exist.
This chapter examines AI applications across prevention, identification, treatment, and surveillance. We focus on demonstrated capabilities backed by clinical trials, distinguish them from theoretical applications, and address the regulatory, ethical, and practical challenges specific to this domain.
The AI Opportunity in Substance Use Prevention
The United States is experiencing an unprecedented overdose crisis. In 2023, over 107,000 Americans died from drug overdoses, with synthetic opioids (primarily fentanyl) driving most deaths. The crisis has evolved through three waves: prescription opioids (1990s-2010), heroin (2010-2013), and synthetic opioids (2013-present).
AI offers potential interventions at multiple points:
| Intervention Point | AI Application | Current Evidence |
|---|---|---|
| Prevention | Identify high-risk prescribing patterns | PDMP models validated (AUC 0.86) |
| Identification | Screen hospitalized patients for OUD | Clinical trial showing 47% reduction in readmissions |
| Treatment | Predict medication adherence, relapse risk | Limited validation studies |
| Surveillance | Detect community overdose spikes | Demonstrated in wastewater, EMS data |
| Harm Reduction | Optimize naloxone distribution | Theoretical, emerging evidence |
The Unique Challenges of Substance Use AI
Substance use AI faces barriers distinct from other clinical AI applications:
Stigma amplifies harm from errors: False positives in cancer screening cause anxiety. False positives for opioid misuse can trigger discrimination, loss of pain treatment, and criminal justice involvement.
Privacy regulations exceed HIPAA: 42 CFR Part 2 imposes additional consent requirements for substance use records, fragmenting data needed for AI training.
Treatment capacity constraints: Identifying patients is meaningless if treatment slots do not exist. Many regions have month-long waits for medication-assisted treatment.
Shifting drug supply: Models trained on prescription opioid patterns may not generalize to illicit fentanyl, which now dominates overdose deaths.
Overdose Prediction and Prevention
EHR-Based Risk Models
Electronic health record data contains signals predictive of overdose risk. AI models analyze clinical notes, diagnoses, prescriptions, and utilization patterns.
Landmark Study: University of Wisconsin AI Screening Trial
A 2025 clinical trial published in Nature Medicine demonstrated real-world effectiveness of EHR-based AI screening (Afshar et al., 2025):
Design: - 51,760 hospitalizations across baseline (2021-2022) and intervention (2023) periods - AI analyzed EHR documentation in real-time using convolutional neural networks - High-risk patients triggered alerts recommending addiction medicine consultation
Results: - Consultation rates: 1.35% (baseline) vs. 1.51% (AI-assisted) - 30-day readmission rates: 14% (baseline) vs. 8% (AI-assisted) - Odds ratio for readmission: 0.53 (95% CI: 0.30-0.91) - Estimated savings: $109,000 during study period ($6,801 per readmission avoided)
Key finding: AI-prompted consultations were as effective as provider-initiated consultations, but reached patients who would otherwise have been missed.
# Conceptual example: EHR-based OUD risk screening
# Actual implementation requires clinical validation
class OUDRiskScreener:
"""
EHR-based opioid use disorder risk screening
Based on approach from Afshar et al. 2025
"""
def __init__(self, model_path):
self.model = self.load_validated_model(model_path)
self.required_consent = True # 42 CFR Part 2 compliance
def screen_patient(self, patient_data):
"""
Screen hospitalized patient for OUD risk
Parameters:
- patient_data: Dict containing EHR elements
Returns:
- risk_score: Float 0-1
- recommendation: String
- confidence: Float
"""
# Check consent status (42 CFR Part 2 requirement)
if not self.verify_consent(patient_data['patient_id']):
return {
'error': 'Substance use screening requires patient consent',
'consent_required': True
}
# Extract relevant EHR features
features = self.extract_features(patient_data)
# Features used in validated models include:
# - Clinical notes (NLP-processed)
# - Diagnosis history (ICD codes for pain, mental health)
# - Prescription history (opioid MME, benzodiazepines)
# - ED utilization patterns
# - Social history documentation
risk_score = self.model.predict_proba(features)[0, 1]
# Threshold based on clinical validation
if risk_score >= 0.7:
recommendation = "Consider addiction medicine consultation"
alert_level = "high"
elif risk_score >= 0.4:
recommendation = "Monitor for signs of OUD"
alert_level = "moderate"
else:
recommendation = "Standard care"
alert_level = "low"
return {
'risk_score': risk_score,
'recommendation': recommendation,
'alert_level': alert_level,
'model_version': self.model.version,
'validation_population': 'Hospitalized adults, Midwest US'
}
def extract_features(self, patient_data):
"""Extract and process EHR features for prediction"""
features = {}
# Prescription history
if 'medications' in patient_data:
features['total_mme_90d'] = self.calculate_mme(
patient_data['medications']
)
features['concurrent_benzo'] = self.check_concurrent_benzo(
patient_data['medications']
)
features['num_prescribers_90d'] = len(set(
med['prescriber'] for med in patient_data['medications']
if med['is_controlled']
))
# Clinical notes (NLP)
if 'notes' in patient_data:
features['note_embeddings'] = self.process_clinical_notes(
patient_data['notes']
)
# Diagnosis history
if 'diagnoses' in patient_data:
features['pain_dx_count'] = self.count_pain_diagnoses(
patient_data['diagnoses']
)
features['mental_health_dx'] = self.has_mental_health_dx(
patient_data['diagnoses']
)
return featuresPDMP-Integrated AI
Prescription Drug Monitoring Programs (PDMPs) track controlled substance prescriptions across all dispensers in a state. AI models built on PDMP data can identify high-risk patterns.
Pennsylvania PDMP Overdose Prediction Model
Researchers developed and validated a machine learning model using Pennsylvania PDMP data (Lo-Ciganic et al., 2023):
Data: - 222 potential predictors from PDMP records - Linked to overdose death data - Training: February 2018-September 2021
Model: - Gradient boosting machine - Final model: 20 variables - Validation c-statistic: 0.86
Key predictors: - Morphine milligram equivalents (MME) prescribed - Number of prescribers - Number of pharmacies - Overlapping opioid and benzodiazepine prescriptions - History of medication for opioid use disorder
“PDMP-generated risk scores are created by algorithms in software applied to patient information. Such scores have not been validated against clinical outcomes such as overdose and should not take the place of clinical judgment.”
This applies to commercial tools like NarxCare. Research models with published validation (like the Pennsylvania study) have demonstrated predictive validity, but most deployed commercial tools lack such validation.
Commercial PDMP Tools: A Critical Assessment
NarxCare (Bamboo Health) is embedded in most state PDMPs and generates “Overdose Risk Scores.” However:
- The algorithm’s validation has not been published in peer-reviewed literature
- Clinicians report using scores to justify denying pain treatment
- Civil liberties groups have raised concerns about algorithmic discrimination
Evidence gap: Despite widespread deployment, no published clinical trial demonstrates NarxCare reduces overdoses. The tool may reduce prescribing without reducing harm, or shift patients to illicit markets.
Treatment Optimization
Medication-Assisted Treatment (MAT) Adherence
Medications for opioid use disorder (MOUD), including buprenorphine, methadone, and naltrexone, are highly effective but adherence is challenging. AI models attempt to predict treatment discontinuation.
Current evidence:
- Multiple studies develop predictive models for MOUD discontinuation
- Features include demographics, prior treatment episodes, co-occurring disorders
- Validation typically limited to single health systems
- No published trials demonstrate that predictions improve retention
The intervention gap: Predicting who will discontinue treatment is straightforward. What to do with that prediction is unclear. Evidence-based interventions for improving MAT retention are limited.
Relapse Prediction
AI researchers have attempted to predict substance use relapse using:
- Ecological momentary assessment (EMA): Smartphone-based mood, craving, and context reporting
- Passive sensing: GPS, accelerometer, phone usage patterns
- Social media analysis: Language patterns on public posts
Evidence status: Research-stage. Studies demonstrate correlation between digital biomarkers and relapse, but no clinical trials show prediction-based interventions improve outcomes.
Demonstrated: AI can predict relapse risk from digital data
Not demonstrated: Acting on predictions improves outcomes
The gap is significant. Alerting someone that they are “high risk for relapse” could be helpful, harmful, or neutral, and we do not yet know which.
Digital Therapeutics for Addiction
Several digital therapeutic products have received FDA clearance for substance use disorders:
reSET and reSET-O (Pear Therapeutics, company dissolved 2023): - Prescription digital therapeutics for SUD and OUD - Cognitive behavioral therapy delivered via app - FDA-cleared based on clinical trial data - Company bankruptcy in 2023 left patients without access
Lessons from the Pear Therapeutics failure: 1. Clinical evidence does not guarantee commercial viability 2. Digital therapeutics require ongoing maintenance and support 3. Patients lose access when companies fail 4. Business model sustainability matters for public health tools
Current landscape: The digital therapeutics for addiction space remains fragmented. Academic-developed tools (like A-CHESS) continue in research contexts, but commercial products face reimbursement and adoption challenges.
Surveillance Applications
Wastewater-Based Epidemiology
Wastewater surveillance can detect drug metabolites at the community level without identifying individuals, avoiding privacy concerns that limit individual-level data.
How it works: 1. Collect samples from wastewater treatment plants 2. Analyze for drug metabolites (fentanyl, methamphetamine, cocaine, etc.) 3. Normalize by population biomarkers (caffeine, creatinine) 4. Track trends over time and compare across communities
AI applications: - Time series forecasting of drug trends - Anomaly detection for sudden changes (new drug introduction) - Spatial analysis linking wastewater data to overdose hotspots
Advantages: - Population-level data without individual identification - Captures drug use by people not in healthcare system - Near-real-time (24-48 hour lag vs. weeks for overdose death data) - Not subject to 42 CFR Part 2 restrictions
Limitations: - Infrastructure requirements (access to treatment plant samples) - Cannot distinguish therapeutic use from misuse - Geographic resolution limited to sewershed
Emergency Department Syndromic Surveillance
ED chief complaints and triage notes can signal overdose surges before death data becomes available.
ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics): - CDC-developed syndromic surveillance platform - Analyzes ED visit data in near-real-time - Includes overdose syndrome category
AI enhancements to syndromic surveillance include: - NLP classification of free-text chief complaints - Anomaly detection for geographic/temporal clusters - Forecasting models to anticipate resource needs
EMS Naloxone Administration
Emergency medical services data on naloxone (Narcan) administration provides another real-time signal:
- Each administration indicates suspected opioid overdose
- GPS data enables geographic clustering
- Timestamp data enables temporal pattern detection
Several jurisdictions use EMS naloxone data for: - Directing mobile outreach resources - Alerting harm reduction organizations - Triggering public health advisories
Implementation Challenges
The 42 CFR Part 2 Privacy Framework
Substance use disorder records receive special protections under 42 CFR Part 2, exceeding standard HIPAA requirements.
Key provisions (updated February 2024, enforced February 2026):
| Aspect | HIPAA | 42 CFR Part 2 |
|---|---|---|
| Consent for treatment | Implied | Explicit written consent required |
| Disclosure for payment | Permitted | Requires consent |
| Research use | IRB waiver possible | Additional restrictions |
| Law enforcement | Permitted with warrant | Requires court order (historically) |
| Breach notification | Required | Now aligned with HIPAA |
2024 updates (effective February 2026): - Single consent for treatment, payment, and healthcare operations now permitted - Aligns notice requirements with HIPAA Notice of Privacy Practices - Records disclosed with consent may lose Part 2 protections downstream - SUD counselor notes receive protections analogous to psychotherapy notes
Implications for AI: 1. Training AI on substance use data requires specific consent beyond HIPAA 2. Data linkage across systems may violate Part 2 without proper consent 3. Model outputs must be handled as protected records 4. De-identification standards may differ from HIPAA Safe Harbor
Stigma and Discrimination Risks
AI for substance use carries unique risks of harm:
Treatment denial: Patients flagged as “high risk for opioid misuse” may be denied appropriate pain treatment, even when they do not have an opioid use disorder.
Criminal justice: Unlike other medical conditions, substance use can trigger legal consequences. AI predictions could inform decisions about probation, parole, or prosecution.
Employment/insurance: Despite ADA protections, discrimination against people with substance use disorders remains common. AI-generated risk scores could leak into non-medical contexts.
Trust erosion: Patients who learn their records are analyzed by AI may withhold information, harming both their care and data quality for future models.
Treatment Capacity Mismatch
Identifying at-risk patients is meaningless without treatment access:
- Buprenorphine: DEA X-waiver removed in 2023, but many providers still do not prescribe
- Methadone: Only available through federally licensed opioid treatment programs, with limited geographic availability
- Residential treatment: Wait times often exceed 30 days
- Outpatient counseling: Insurance coverage and workforce shortages limit access
The equity paradox: AI may be most accurate for populations with extensive healthcare data, but those populations often already have treatment access. The populations most in need (uninsured, rural, justice-involved) are underrepresented in training data and underserved by the healthcare system.
Regulatory and Policy Landscape
NIDA HEAL Strategic Plan FY 2025-2029
The National Institute on Drug Abuse’s HEAL Opioid Use Disorder and Overdose Strategic Plan includes specific AI priorities:
“Utilize data, monitoring, technology, and Artificial Intelligence (AI) to identify and inform the deployment of interventions for populations at high-risk of opioid use and overdose.”
Specific objectives include: - Develop real-time or near-real-time substance use monitoring - Mine EHR, clinical trial data, digital health devices, and social media - Optimize clinical data standards for AI research - Address challenges in pain management alongside OUD treatment
FDA Regulation of Addiction AI
FDA regulates AI/ML-based software as a medical device (SaMD) when it is intended for diagnosis or treatment recommendations:
Currently cleared: - Digital therapeutics (reSET, reSET-O) - Clinical decision support tools (various)
Regulatory considerations: - Prospective clinical trial evidence strengthens clearance pathway - Post-market surveillance requirements - Algorithm change protocols (predetermined change control plans)
State-Level PDMP Mandates
All 50 states plus DC now operate PDMPs, with varying AI integration:
- Some states mandate PDMP checks before prescribing
- NarxCare or similar risk scores displayed in most state systems
- Data sharing agreements between states are inconsistent
- Clinical decision support rules vary by state
Evaluation Framework
When assessing substance use AI, consider:
1. Validation Rigor
| Question | Red Flag | Green Flag |
|---|---|---|
| Was external validation performed? | Single-site internal validation | Multi-site prospective validation |
| Is the validation population published? | “Representative sample” (vague) | Specific demographics, timeframe, setting |
| Were outcomes clinically meaningful? | AUC only | Clinical outcomes (overdose, treatment retention) |
2. Equity Assessment
| Question | Red Flag | Green Flag |
|---|---|---|
| Were subgroup analyses performed? | “No significant differences” without data | Published performance by race, SES, insurance |
| Are high-risk populations represented? | Insured patients only | Includes Medicaid, uninsured |
| Were justice-involved populations considered? | Not mentioned | Explicit analysis of carceral settings |
3. Implementation Readiness
| Question | Red Flag | Green Flag |
|---|---|---|
| Is 42 CFR Part 2 compliance addressed? | Not mentioned | Explicit consent and data handling protocols |
| Are workflow integration plans described? | “Alert-based” without details | Usability testing, alert burden analysis |
| Is treatment access coupled to identification? | Identification only | Linked to treatment navigation |
Key Takeaways
The 2025 Nature Medicine trial is the strongest evidence to date. AI screening for OUD reduced 30-day hospital readmissions by 47% in a rigorous prospective trial. This is demonstrated, not theoretical.
PDMP risk models can predict overdose. Validation studies show AUC around 0.86 for fatal overdose prediction. However, commercial tools (NarxCare) lack published outcome validation.
42 CFR Part 2 creates unique data challenges. Updated regulations (effective February 2026) ease some restrictions but maintain stricter protections than HIPAA. AI implementations must address consent requirements.
Treatment capacity must match identification capacity. Identifying at-risk patients without ensuring treatment access is ethically questionable and clinically pointless.
Stigma amplifies harm from false positives. Unlike most clinical AI, substance use predictions can trigger discrimination, criminal justice involvement, and treatment denial. Specificity matters more than sensitivity.
Wastewater surveillance avoids individual privacy concerns. Population-level drug monitoring via wastewater provides trend data without the consent and stigma issues of individual-level surveillance.
Digital therapeutics face sustainability challenges. The Pear Therapeutics bankruptcy left patients without access to FDA-cleared products. Business model viability matters for public health tools.
The NIDA HEAL Strategic Plan FY 2025-2029 prioritizes AI. Federal research priorities include real-time monitoring, EHR mining, and AI deployment for high-risk populations.
Further Resources
Official Guidance
- 42 CFR Part 2 Final Rule Fact Sheet - HHS explanation of 2024 updates
- CDC PDMP Clinical Guidance - CDC guidance on PDMP use
- NIDA HEAL Strategic Plan FY 2025-2029 - Federal AI priorities for OUD
Key Research
- Afshar et al., 2025, Nature Medicine - Clinical trial of AI screening for OUD
- Lo-Ciganic et al., 2023 - PDMP-based overdose prediction validation
Professional Organizations
- SAMHSA - Substance Abuse and Mental Health Services Administration
- ASAM - American Society of Addiction Medicine
- Legal Action Center - 42 CFR Part 2 guidance for practitioners