EIC turned experience-dependent handoffs, readiness rules, exception handling, and professional authority into one validated operating model that could support higher volume and later automation.
40 employees including 12 registered nurses and clinical reviewers, running 24/7 assistance for insured members, hospitals, and insurer partners.
This process became the foundation for the provider’s connected AI Agency Transformation program. Read the connected program →
The provider needed to support higher and more volatile case volume without making each increase in demand require a corresponding increase in experienced operational and clinical coordination.
Routine assistance touched member communications, policy information, medical evidence, provider responses, financial information and several decisions
Experienced employees knew how to connect the parts, what was missing, which insurer rules applied and when a routine case had become an exception
Qualified personnel could become involved while a case was still being reconstructed or clarified, exposing scarce professional capacity to work that did not itself require medical, coverage or financial judgment
Salesforce remained the formal case environment, the omnichannel platform the communication workspace, insurer sources the current member and policy information, and hospitals and providers the current medical and billing evidence. None of those environments needed replacing.
What was absent was one operating definition of when a case was ready to move forward, what evidence the next role required, which variations could stay on the routine path, and where professional authority had to interrupt execution.
Repeated information requests, informal handoffs, premature specialist involvement and duplicate review were real parts of the work, and experienced employees decided case by case what should happen next.
The real flow was written down: what each step needs, what counts as an exception, and who decides.
The case was traced from the first call through registration, evidence, medical and coverage review, provider coordination, authorization and closure.
Implicit transitions were replaced with explicit readiness conditions, so a step moves only when the named role can produce the next valid state.
Incomplete evidence, conflicting information, coverage uncertainty, unusual provider requests, high-cost treatment and financial discrepancies each got their own path.
Preparing a decision was separated from making it, so clinical assessment, coverage judgment and Guarantee of Payment approval stayed with authorized people.
A documented process was not treated as a finished one.
Real use exposed missing information requirements, routing issues and exception patterns, and those went back in until the routine path and the escalation logic held.
The provider gained one operating definition for routine work, major variations, readiness, and escalation, instead of relying on experienced employees to recreate the correct path as case volume increased.
Clinical, coverage, and financial authority became explicit within the operating flow, with clearer expectations about what information should already be available before a case reached them.
The provider entered automation with tested case states, readiness conditions, information requirements, exception paths, and human authority already defined.
Leadership could scale the operation around a repeatable model rather than around the availability of the people who understood how all the parts of the service fitted together, and the risk of using AI to compensate for an ambiguous process was removed before automation began.
The service behind this storyAI Process Reengineering →
Process redesign, governed knowledge and automated preparation became one connected operating model, so growth stopped requiring proportional clinical hiring.
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.
Routine case preparation ran across the existing systems, so the same clinical team absorbed a peak above 2,500 cases a month without seasonal hiring.
EIC reconstructs how the work actually moves, then defines the readiness conditions, the exception paths and the points where professional authority has to interrupt execution. That comes before anything is automated.