EIC automated routine case preparation across the provider's existing systems, allowing the same clinical team to handle peak monthly volume above 2,500 cases while maintaining service-level performance.
40 employees including 12 registered nurses and clinical reviewers, running 24/7 assistance for insured members, hospitals, and insurer partners.
This automation is one of three foundations connected in the provider’s AI Agency Transformation program. Read the connected program →
The provider normally handled 1,200 assistance cases a month and could exceed 2,500 at seasonal peaks. It needed to preserve 24/7 service without repeatedly expanding a limited clinical workforce to match those peaks.
Member conversations, formal case records, policy information, medical reports, diagnostics, provider correspondence, estimates and invoices
Staff established what mattered, what evidence was missing and whether the case was ready before an authorized reviewer could act
Preparation took 40 to 60 minutes of active handling on a routine case, so rising volume consumed capacity around every decision
Salesforce managed the formal case, the omnichannel environment handled communication, insurer sources supplied current member and policy information, and hospitals and providers supplied current medical and billing evidence. None of them needed replacing.
What was missing was a controlled way to assemble those inputs into a decision-ready case and to let routine work progress on its own until professional judgment was required.
The operating process and the knowledge needed for that automation had been redesigned and governed beforehand, so preparation could be automated against explicit operating requirements rather than against an ambiguous process.
The case arrives assembled and checked, and the decisions that need authority still go to a person.
Controlled connectors pull them together with the case state and the approved insurer information, and make defined routine updates where that is permitted.
Each stage carries its own required information and checks, so the automation runs against explicit process state, not a guess at what the case needs.
Member conversations, medical reports, diagnostics, provider correspondence, estimates and invoices become one chronology, with inconsistencies and billing anomalies marked.
Anything sent for semantic processing crosses a controlled privacy boundary, and every prepared fact stays traceable to its source.
Policy fields, documented limits, approved amounts, invoice arithmetic, duplicates, thresholds and mandatory approvals stay deterministic.
AI did not determine medical necessity, ambiguous coverage or Guarantee of Payment approval, and a case that leaves routine conditions escalates with the open issue already named.
Monthly volume passed 2,500 cases against 1,200 in normal periods, with no additional seasonal clinical hires and service-level performance maintained.
Active handling on a predominantly routine case fell from 40 to 60 minutes down to 20 to 30, releasing the time that had gone into reconstructing communications, reviewing source documents and completing routine checks.
About 85% of routine cases reached review with the core evidence package preassembled, so reviewers began closer to the point of judgment and kept more capacity for complex cases and exceptions.
The provider did not remove professional judgment from medical assistance; it removed the routine preparation competing with that judgment, so more of each additional case could be absorbed by the operating system while qualified people stayed responsible for the decisions that needed them.
The service behind this storyAI Process Automation →
Process redesign, governed knowledge and automated preparation became one connected operating model, so growth stopped requiring proportional clinical hiring.
Experience-dependent handoffs, readiness rules and professional authority became one validated operating model, so growth no longer meant recreating the correct path case by case.
Fragmented procedures, insurer requirements and undocumented know-how became governed guidance with an owner, a scope and a review date, so a routine answer no longer meant finding the right colleague.
Before automating a case queue, measure how much of the handling happens before the first human review. That part is evidence work and can be automated; what comes after it reaches a patient or a hospital, and needs a name against it.