- A predictive model scores opioid use disorder and overdose risk from prescribing history, diagnoses, encounters and clinical notes you already hold.
- Reason codes show which factors raised each score, so your teams can check the model against what they know.
- The model builds a prioritized outreach list. Clinicians and program staff decide every next step, and H2O makes no clinical efficacy claim.
- Protected health information never has to leave your boundary, and substance-use records can carry extra consent rules your privacy team controls.
Risk builds across records no one reads together
The signs of rising opioid risk rarely sit in one place. They are spread across prescriptions from several prescribers, a pain diagnosis, an emergency visit and a line in a clinical note.
Your care-management and public-health teams can review only a fraction of those records by hand. By the time a pattern is clear, the person may already have had an overdose. A model that reads the records together gives your teams a shorter, better-ordered list of people to reach with care.
How opioid risk prediction works
This is a classification model trained on your own history, estimating the chance that a person develops opioid use disorder or has an overdose, built inside your own boundary.
Step 1: connect the history
Pull prescribing history, diagnoses, encounters and clinical notes from the systems you already run. The model sits on top of the record system; it does not replace it.
Step 2: read the notes
Natural language processing turns free-text notes into features, next to structured fields.
Step 3: build and compare models
H2O Driverless AI runs automatic feature engineering and ranks candidate models on held-out records.
Step 4: explain each score
Interpretability tools show the factors behind each score, so your reviewers can question it.
Step 5: check for bias
Review how the model scores across age, sex, race and payer groups before anyone acts on it.
Step 6: run it in shadow mode
Score people beside your current process first, and compare the list with what your teams would have chosen.
Governance for sensitive records
Substance-use records can carry extra consent rules, so your privacy team sets which data the model may use. The model supports public-health and care-management work, not a clinical or legal decision: H2O makes no clinical efficacy claim, and the score changes where your teams start, not who decides.
Proof
H2O can be used to score opioid use disorder and overdose risk from records you already hold, explain each score, and hand your teams a prioritized outreach list. No customer result is published for this use case yet.