Healthcare executives often evaluate artificial intelligence with a familiar checklist: model accuracy, integration effort, implementation time, and license cost. That list omits the expense that determines whether an administrative AI project creates capacity or creates another system to supervise. The missing line item is exception handling.
The demand is real. In its 2026 Physician Survey on Augmented Intelligence, the American Medical Association reported that 81 percent of respondents were aware of or used AI in practice. Seventy-three percent expected administrative automation to improve physician well-being and burnout. The same survey found that physicians placed high importance on validated safety and efficacy, privacy assurances, and involvement in adoption decisions. Buyers are therefore trying to solve two problems at once: reduce operational burden and preserve accountable human control.
Feature comparisons hide operational work
A product demonstration usually shows a clean case. The input is complete, the identity matches, the portal is available, and the action follows a known rule. Real queues contain missing demographics, duplicate records, expired credentials, unclear orders, payer discrepancies, unreachable contacts, and policy questions. Those cases do not disappear when the routine path becomes faster. They become a separate queue that somebody must detect, investigate, route, and close.
This changes the economics. A tool can automate a large share of individual steps while increasing management work if staff must watch dashboards, interpret failures, and repair incomplete cases. License utilization may rise while backlog age, rework, and staff interruptions remain unchanged. A business case based only on tasks automated or minutes saved will miss that transfer of work.
Define the unit of value
Procurement teams should define value as a completed, in-scope outcome with evidence. For an eligibility queue, completion might require a verified response stored in the approved system before the visit. For a referral queue, it might require the document to be matched, indexed, advanced to the next task, and escalated when identity is uncertain. The unit should be observable and auditable.
That definition lets leaders compare software, outsourced services, staffing, and managed automation on the same basis. It also prevents a common measurement error: treating an attempted action as completed work. Calls placed, messages generated, and documents classified are activity measures. They matter, but they do not establish that the queue reached its agreed final state.
Price the exception path
Every proposal should state what happens when the normal path fails. Which administrative exceptions will the vendor resolve? How long will it continue working a case? What evidence will it retain? When must clinic staff intervene? Which decisions always remain with an authorized person? If these questions are unanswered, the buyer cannot estimate the actual operating cost.
A practical scorecard should separate routine completion, administrative exception resolution, and practice intervention. It should track eligible cases received, cases completed, turnaround time, backlog age, reopened work, unresolved exceptions, and staff touches. The goal is not a perfect automation rate. The goal is reliable capacity with a visible boundary around human authority.
Treat governance as part of the workflow
Governance cannot live only in a policy document. The NIST AI Risk Management Framework emphasizes managing risk across the AI lifecycle. In operations, that principle should appear in access controls, approval rules, escalation paths, logs, testing, and post-deployment monitoring. A Business Associate Agreement may be required, but it does not define who may change a code, make a clinical determination, accept a financial term, or act for a patient.
The same operating discipline supports the HHS guidance on HIPAA risk analysis. Buyers should map each workflow’s systems, data, permitted actions, retained evidence, and fallback procedures before expanding scope. This makes governance measurable instead of aspirational.
Start with one live queue
The most credible ROI study begins with one bounded queue and includes failure modes from the start. Capture the baseline backlog, completion time, staff effort, rework, and exception mix. Run normal cases alongside missing information, access failures, identity mismatches, and policy exceptions. Measure whether the queue closes with less staff attention while decisions outside the approved boundary still reach the right person.
Healthcare organizations do not need another favorable demonstration. They need a financial model that includes the work left behind when the happy path ends. Once exception handling appears as a priced, measured part of the operating model, leaders can tell whether an AI investment creates capacity or merely moves the burden to a different screen.
About Feynman Xu
Feynman Xu, Ph.D., is the founder and CEO of MedArise, which provides managed AI employees for defined administrative workflows in medical practices. His work focuses on workflow ownership, exception handling, and clear human authority in healthcare operations.
