A deep look at how Lumara's Prior Authorization team works today — and a phased plan that starts with end-to-end intake automation as the proving ground.
Across 26 documented workflows, a clear picture emerged: capable specialists doing high-judgment work, slowed by mechanical document handling and fractured tooling.
Across nearly every workflow, a disproportionate share of time goes to downloading, naming, filing, combining, and uploading documents. The naming convention (Lastname_Firstname DOB DocType Date) is enforced manually via copy-paste in 8+ workflows.
Staff navigate Salesforce, Adobe Acrobat, Gmail, a shared file system, Availity, payer portals, the NPI Registry, and external counsel's secure file portal — sometimes inside a single workflow.
Rules-based, high-volume intake work (great for automation) versus judgment-intensive PA specialist work — clinical review, appeal strategy, payer negotiation — that requires AI augmentation, not replacement.
Intake is the front door of the PA process. It's also where the workflow pattern is cleanest, the risk is lowest, and the impact downstream is the most direct.
Account creation, document download/naming/filing, Salesforce upload — clear rules, measurable output.
Errors are mechanical (wrong filename, wrong folder), not clinical. A safe place to test and learn.
Step count and time-per-task are easy to benchmark before and after. ROI shows up fast.
The Documentation Review Complete handoff is the bottleneck. Faster intake means faster PA start, which means faster patient access.
Sequenced for incremental delivery and testability. Automates roughly 265 documented manual steps across the team's intake workflows. By the end of Phase 1, the morning's inbox arrives as PA packets in a specialist's queue.
Email monitoring, document download, page sorting, document classification.
Naming convention, shared drive filing, Salesforce record creation, email organization, account creation for new patients.
AI-assisted extraction of biomarker values, exacerbation history, and provider recommendation. Confidence scoring and triage recommendations.
Territory-to-specialist routing, PA packet assembly, automated Salesforce task creation in the right queue.
Auto-drafted correction requests to territory managers, follow-up task creation, editable template management.
Each path has real strengths and real risks. We recommend running them side by side — fast — and letting evidence drive the decision.
Both vendors and custom builds can use foundation models. The difference is who controls the workflow, the roadmap, and the data perimeter.
Your application. Your roadmap. Your IP. Your data perimeter. Foundation models are an interchangeable substrate underneath.
As the substrate improves — and it improves rapidly — your application gets smarter without a vendor upgrade cycle.
Vendor's product. Vendor's roadmap. Vendor's pricing model. Vendor's data perimeter. The vendor decides when AI capability improves.
Whether they use a trained model or a foundation model internally is a tactical detail. The strategic limit is the same: you're buying their product, not building yours.
If the custom build approach succeeds for PA intake, Lumara has a workflow engine that extends to anywhere a human is touching Salesforce. Appeal packet assembly. Clinical doc review. Field force enablement. Account & territory planning. That's a fundamentally different ROI calculus than buying a point solution.
The phased roadmap below shows how PA intake unlocks a broader automation capability across the PA lifecycle.
Auto-populate insurance fields from structured intake data. Build appeal packet assembly tools. Create payer-specific playbook templates from historical cases.
40+ step appeals processes that span multiple systems with multi-week follow-up cadences. Automate cadence management, portal monitoring, and document coordination across payer portals and external counsel.
Build a structured denial pattern database tracking payer-specific denial reasons, successful appeal approaches, and outcomes. Transform reactive appeals into strategic ones.
As Lumara expands commercial coverage and patient volume grows, payer-specific complexity multiplies. The automation strategy must scale for this.
Each path needs evidence. Each evaluation has a defined owner and a clear endpoint.