Adherence probability, before you prescribe
A proprietary AI/ML score estimates how likely a patient is to adhere to a specific medication - calculated before the prescription is sent, not after a fill is missed.
Instead of reacting to a missed refill after the fact, the model reads the pattern building up to it.
A proprietary AI/ML score estimates how likely a patient is to adhere to a specific medication - calculated before the prescription is sent, not after a fill is missed.
When probability is low, H2H surfaces the likely driver - a pharmacy desert, a complex prescription regimen, cost, or another contributing factor.
Choose a different pharmacy, simplify the regimen, or address the barrier before the script ever leaves your hands.
Our proprietary AI/ML algorithm weighs pharmacy access, regimen complexity, medication class, and other real-world factors to explain a low adherence probability - not just flag one.
That means you can address the actual barrier - switch pharmacies, simplify dosing, or add support - before the patient ever leaves with a script they won’t fill.
The probability and its likely reason appear right in the prescribing workflow, before you send.
Pharmacy desert, complex regimen, cost, or another factor - named, not just scored.
Choose a different pharmacy, simplify the regimen, or route to support - all before the script is sent.
In early pilot practices, prescribers using adherence predictions reported catching likely non-adherence before writing the prescription rather than after a fill was already missed.
*Illustrative pilot feedback formal outcomes data to follow.
Included in EPCS + PDMP DRx, or available as an add-on for enterprise plans.