A regional medical assistance provider needed more case capacity without tying every volume increase to more manual coordination and clinical hiring. EIC connected the redesigned process, governed knowledge, AI-enabled preparation and deterministic controls into one operating model, with professional authority kept at every consequential decision.
40 employees including 12 registered nurses and clinical reviewers, running 24/7 assistance for insured members, hospitals, and insurer partners.
The measured business impact below comes from the automation stage of this program. Read the automation case →
Case capacity needed to grow without tying every volume increase to more manual coordination and scarce professional staffing. Isolated automation of single activities was not enough: the business needed one model connecting process state, evidence, knowledge, controls and authority across the whole case.
Every additional case increased the communication, evidence, policy context and financial information that had to be reconstructed before consequential work could begin
New medical evidence, provider responses, policy questions or financial discrepancies could surface after the first review, so a case that started routine did not always stay that way
Document interpretation improved one activity and workflow automation another, but nothing connected process state, evidence, knowledge, controls and human authority across the full lifecycle
The operating model needed to know what stage a case had reached, what information was available, which requirements remained unmet, which controls had passed and what action was permitted next.
AI was suited to interpretation and preparation, deterministic logic to reproducible controls and workflow, existing systems to authoritative records, and qualified people to consequential judgment and approval.
Additional routine work could move toward exception-led execution only once the process, knowledge, inputs, controls, AI performance, exception detection and business risk were sufficiently reliable.
Each foundation keeps doing its own job; what changed is that they share one case lifecycle, one set of process states and one definition of what a case needs next.
The redesigned process, the AI Knowledge Base, the existing systems and the automation capabilities now run against one case lifecycle and one set of process states.
Each part reads a case the same way, so expansion never means creating a separate interpretation of the business.
The model defines what AI-enabled execution can observe, interpret, prepare, recommend, route and execute, and where professional review, approval or conflict resolution is required instead.
It can name which role and authority a stopped case needs, not just that it stopped.
Routine work only progresses when process state, validated information, permissions and required approvals allow it.
Monitoring preparation quality, routing, exceptions, overrides and control failures shows where automation can expand next.
Before: separate tools each covered one activity. Now: one flow carries the case from context to the point where a person has to decide.
Salesforce case state, communication, policy information, medical evidence, provider information, estimates, invoices and prior approvals for the current stage.
The AI Knowledge Base supplies approved procedures and insurer-specific guidance, while AI interprets the unstructured information around the case.
Process state, completeness requirements, thresholds, permissions, approvals and routing conditions decide whether the case can progress on its own.
Routine activity continues where every condition is met. Clinical judgment, coverage authority, Guarantee of Payment decisions and other consequential exceptions go to the right person.
Monthly volume passes 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, once the process, knowledge and automation foundations were connected.
About 85% of routine cases reach review with the core evidence package preassembled, so reviewers spend more of their time on judgment and less on reconstruction.
The provider does not run three separate AI initiatives; it runs one connected program across process, knowledge, automation and human authority. As operating evidence accumulates, more of the routine work can move toward exception-led execution, while qualified people stay accountable for the decisions that need them.
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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.
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.
Before funding a fourth automation project, check whether the first three can already talk to each other. If they can't share a case lifecycle, the fourth one won't either.