Key takeaways
- 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.