1 in 10 opioid users become addicted.1
Less than half fully recover.
1 in 10
1 in 10 — that's 10% of patients. For the 9 in 10 patients who can safely take opioids, it is important to know if you just need standard precautions to take opioids. For the 1 in 10 patients who are at high risk, it is important to protect them and prevent exposure.
With half the risk due to genetic factors, Proove's AI analysis gives you and your doctor up to 96% accuracy to make better informed medication decisions.2,3
KidSafe Test
Get your child's Opioid Risk and Drug Metabolism results from home — no office visit required. Complete a short intake online, then take the at-home test and return your sample. Once testing is complete, a licensed telehealth clinician reviews the results and signs off before they're released.
If you have a kid, use code KIDSAFE299 at checkout to get the KidSafe Test — Opioid Risk + Drug Metabolism combined — for just $299.
- Opioid Risk + Drug Metabolism, combined into one test
- Reviewed and signed off by a licensed telehealth clinician
- Kit ships to your door with everything included
- Just $299 with code KIDSAFE299 at checkout
Precision Lifesaving Intelligence to Protect Those at Risk and Empower Those Who Can Use Opioids With Standard Precautions
Rational Decision-Making to Inform Opioid Utilization
Proove Opioid Risk combines genetics + clinical assessments into one clear, actionable, accurate score.
Superior Accuracy Compared to Other Tools
Published accuracy data shows Proove Opioid Risk outperforms standard screening tools such as a clinical interview, SOAPP, ORT, and DIRE.3,4
Extensive Published Evidence Supporting the Accuracy
Proove Opioid Risk is supported by three clinical validity studies demonstrating superior accuracy up to 96.7%, involving 52 different sites enrolling 2,784 patients.4,5,6
Extensive Published Evidence Showing Better Outcomes
Proove Opioid Risk is supported by three clinical utility studies demonstrating positive outcomes including reduced opioid prescribing, no opioid abuse, and safely prescribing opioids to achieve substantial pain relief, involving 194 different sites enrolling 13,468 patients.7,8,9
Precision Medicine Across the Pain Journey
We analyze 12 genes that influence your opioid addiction risk. Hover over each card to learn what each gene does and why it matters.
Reward Genes
How good things feel, including opioids.
▾ Hover to learn
Opioid Genes
How your body responds to opioids.
▾ Hover to learn
Mood & Stress
Emotions, anxiety, and coping.
▾ Hover to learn
Opioid Risk Index (ORI)
A combined score integrating genetic markers and personal history.
Low (0-23)
Standard precautions
Moderate (24-50)
Monitor closely
High (51-100)
Consider alternatives
6 personal history factors integrated into ORI:
✓ Alcoholism history
✓ Prescription drug abuse
✓ Mental health disorders
✓ Illegal drug abuse
✓ Age 16-45
✓ PHQ-2 (Patient Health Questionnaire)
Two questions asking how often, over the past two weeks, you've had little interest or pleasure in doing things, and how often you've felt down, depressed, or hopeless — a validated screener integrated into the assessment to identify depression as a risk factor, rather than relying on a self-reported checkbox.
Frequently Asked Questions
Answers to the most common questions about Proove's opioid risk test.
ORI combines validated genetic markers with personal history to provide a comprehensive risk stratification. Clinical studies show up to 96% predictive accuracy for identifying patients at high risk of developing opioid use disorder.
Ready to Reduce Opioid Risk?
Take the next step toward safer, smarter pain management.
References
- [1] Marcia Meldrum. Opioids' Long Shadow. AMA J Ethics. 2020;22(8):E729-734. doi: 10.1001/amajethics.2020.729.
- [2] American Society of Addiction Medicine. Definition of Addiction. April 2011.
- [3] Farah JR, Lee C, Kantorovich S, Smith GA, Meshkin B, et al. (2017) Evaluation of a Predictive Algorithm that Detects Aberrant Use of Opioids in an Addiction Treatment Centre. J Addict Res Ther 8:312. doi:10.4172/2155-6105.1000312
- [4] Moore TM, Jones T, Browder JH, Daffron S, Passik SD. A comparison of common screening methods for predicting aberrant drug-related behavior among patients receiving opioids for chronic pain management. Pain Med. 2009 Nov;10(8):1426-33. doi: 10.1111/j.1526-4637.2009.00743.x. PMID: 20021601.
- [5] Brenton A, Richeimer S, Sharma M, Lee C, Kantorovich S, Blanchard J, Meshkin B. Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder. Pharmgenomics Pers Med. 2017 May 18;10:187-195. doi: 10.2147/PGPM.S123376. PMID: 28572737; PMCID: PMC5441670.
- [6] Sharma M, Lee C, Kantorovich S, Tedtaotao M, Smith GA, Brenton A. Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting. Health Serv Res Manag Epidemiol. 2017 Aug 24;4:2333392817717411. doi: 10.1177/2333392817717411. PMID: 28890908; PMCID: PMC5574481.
- [7] Brenton A, Lee C, Lewis K, Sharma M, Kantorovich S, Smith GA, Meshkin B. A prospective, longitudinal study to evaluate the clinical utility of a predictive algorithm that detects risk of opioid use disorder. J Pain Res. 2018 Jan 5;11:119-131. doi: 10.2147/JPR.S139189. PMID: 29379313; PMCID: PMC5759857.
- [8] Sharma M, Lee C, Kantorovich S, Tedtaotao M, Smith GA, Brenton A. Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting. Health Serv Res Manag Epidemiol. 2017 Aug 24;4:2333392817717411. doi: 10.1177/2333392817717411. PMID: 28890908; PMCID: PMC5574481.
- [9] Lee C, Sharma M, Kantorovich S, Brenton A. A Predictive Algorithm to Detect Opioid Use Disorder: What Is the Utility in a Primary Care Setting? Health Serv Res Manag Epidemiol. 2018 Jan 21;5:2333392817747467. doi: 10.1177/2333392817747467. PMID: 29383324; PMCID: PMC5784544.