Prior-auth automation creates value only where supported decisions can actually move.
This is a commercial application hypothesis for DCP, not a claim that every payer has the same review burden. The control problem is case-specific: when can an AI-shaped payer action proceed, when is more evidence needed, and when does the decision require qualified human authority?
Use the payer's own review and exception data.
Do not assume a universal cost per review or a universal reduction in human handling. Measure case volume, current review rate, reviewer time, loaded cost, exception patterns, downstream rework, and which decisions are legally or clinically required to remain human.
Review-capacity model
Enter buyer-specific inputs. This calculator estimates potential reviewer capacity freed if shadow-mode analysis identifies a subset of current full reviews that could safely avoid full handling. It does not estimate DCP performance or clinical outcomes.
Add decision-level control where AI influences the payer action.
For a proposed prior-authorization application, DCP would sit alongside the payer's existing intake, policy, utilization-management, and review systems and return a handling response before the consequential action proceeds.
Proceed
Allow the case to continue when the applicable control conditions are met.
Verify
Request additional verification when the case needs more support.
Review
Require qualified human review when additional judgment is warranted.
Hold or escalate
Prevent the proposed payer action from proceeding until the required issue is resolved.
DCP does not predict payer approval, replace medical-necessity policy, or independently approve or deny care.
Is decision authority the real prior-auth bottleneck?
Bring one workflow to a fit call. We will decide whether the volume, review burden, decision-risk profile, and authority constraints justify deeper assessment.
