Pharma patient access and reimbursement support workflows involve layers of benefits verification, prior authorization, and copay assistance coordination — all time-sensitive and all error-prone when handled manually at scale.
A single patient starting a new specialty therapy might need benefits verified, a prior authorization submitted, and copay assistance enrollment processed — often within days, since delays at any one step push back the date the patient can actually start treatment. At the volume most hub services operate, manually coordinating all three for every patient is where cases start to slip.
This complexity is magnified for specialty and rare-disease therapies, where the number of patients is smaller but the reimbursement pathway is often more intricate — multiple layers of prior authorization, manufacturer-specific copay programs, and payer policies that change more frequently than for standard therapies.
This support is available today through a controlled pilot, pairing AI-assisted verification with our team’s oversight of complex or high-risk cases — reducing the time between a prescription and a patient actually receiving therapy.
Instead of a case manager manually calling or logging into multiple payer systems to confirm coverage, the pilot checks benefits automatically and surfaces the results in a format your team can act on immediately, along with any documentation gaps that need to be resolved before the case can move forward.
The approach also flags cases where a patient’s coverage situation is unusual or high-risk early in the process — for example, a plan that historically denies a specific therapy — so your case managers can proactively prepare an appeal strategy rather than discovering the issue only after a denial arrives.
Our AI integrates into existing hub and patient support service workflows, accelerating data-heavy steps while keeping case managers in control of patient-facing decisions.
We’re deliberate about where AI stops and a case manager’s judgment begins. Verifying benefits, assembling documentation, and tracking a case’s status are exactly the kind of repetitive, data-heavy tasks AI handles well. Deciding how to handle a patient’s specific financial hardship, or how to communicate a coverage denial with empathy, stays firmly with your case managers.
This division of labor also scales more gracefully during volume surges — a new therapy launch or a benefit-year transition, for example — since the AI-handled portions of each case can absorb a spike in volume without requiring your hub to add headcount just to get through a temporary peak.
Pharma access and reimbursement workflow support is currently available through Promantra.ai’s controlled pilot program, so we don’t yet have published results specific to this workflow — we’d rather say so plainly than imply proof we haven’t yet established.
A pilot here is designed to target shorter time-to-therapy, reduced manual verification effort, and more consistent case handling, with success criteria defined together with your hub operations team before the pilot begins, and results published here once verified against your program’s own current baseline.
A member of our team will reach out within one business day to schedule a short discovery call — no obligation.