AI Agents Claims Work: How Agents and Human Decision-Makers Split the Load

How do AI agents and human in the loop claims processing differ?

The split depends entirely on scale versus accountability. Volume work scales with the number of claims. It requires the same repetitive tasks regardless of whether a claim has an error. This includes checking the balance on every remit, requesting status updates for unpaid claims, or pulling benefits lookups for tomorrow's schedule.

Decision work carries the accountability for a claim and requires human judgment. This includes determining if a denial is worth appealing, assessing whether an authorization meets clinical criteria, or deciding if a check run should be released.

Organizations need a person to answer for financial and clinical outcomes when auditors, payers, or plan sponsors ask who made a decision. Because of this, Daybook Health routes volume work to software while reserving decisions for a small senior team rather than a large offshore one: agents do the volume work, a small senior team in the United States makes every decision, and nothing is offshored (Daybook Health).

Comparison: Agent Volume Work vs. Human Decision Work

What counts as volume work in agentic RCM?

Volume work includes any repetitive step that follows a rigid rule and produces a verified fact. In true agentic RCM, where agents do the volume work and billers decide, the software prepares the groundwork so a person can take action.

For a Third-Party Administrator (TPA), agents clear pends by chasing itemized bills and coordination-of-benefits data. They prepare authorization requests with criteria and records already attached so the clinician has exactly what they need.

For billing companies and dental teams, agents ask every payer for claim status and balance every remit. They read benefits breakdowns before a visit and draft appeals. Each of these steps ends with a clear fact placed in front of a person, never a final judgment.

Who handles AI claims automation decisions?

A decision is any step that moves money, dictates patient care, or carries organizational accountability. The list of decisions that must stay with a person is short and highly consistent across the industry.

Humans must determine if a claim is paid or denied, whether a service was medically necessary, and what an appeal should argue. Humans must also decide what posts to the ledger, whether a claim is held or sent, and what a patient is quoted.

At Daybook Health, examiners and clinicians handle these choices exclusively. In TPA settings, this explains how examiners and clinicians make every decision on a TPA's claims. Software only proposes actions; the partner's team ultimately files, sends, or escalates every claim.

Why should software never decide a denial or medical necessity?

Software should never make these decisions because errors are expensive, hard to detect, and difficult to defend. When an organization is asked who decides a denial, the answer must be a qualified person. A software-generated denial forces a health plan to defend a decision without a human who can explain the clinical reasoning.

A medical-necessity determination made by an AI agent is a clinical decision made by a non-clinician. This creates massive compliance risks for the patient, the plan, and the organization. Furthermore, write-offs decided by software mean a practice is forfeiting money without active human consent.

Decision work requires deep context that software often lacks. This includes the payer's historical behavior with a provider, the exact intent of a contract, and the nuanced clinical picture of a patient.

What happens during AI medical billing human review when an agent is unsure?

When an agent encounters uncertainty, it immediately holds the item, states the reason, and routes it to the appropriate human. It never guesses. This behavior forms the foundation of a trustworthy AI medical billing human review process.

For example, a remit line carrying a downgrade or a payment under the fee schedule is held for the posting lead with a clear reason attached. If a status answer provides no useful information, it is flagged for a biller rather than recorded as a definitive status.

An agent that resolves uncertainty by guessing crosses a dangerous line. By escalating edge cases, the system keeps the line of accountability visible and directs human attention only to the cases that truly need it.

How do agents and billers split tasks across different RCM models?

The operational split remains identical regardless of the partner type, though the terminology shifts. For practices, Daybook acts as a propose-only tool. It identifies short payments and provides a drafted appeal for every short payment, filed by the practice's team.

For dental billing teams, agents write back directly to practice management systems like Open Dental, Dentrix, and Curve. The billers make every call on claims, and the dentist signs off on anything clinical.

Because Daybook does not run a billing company, it never competes with its RCM partners. Pricing is usage-based, per payer enrollment, remit matched, or claim adjudicated, meaning partners pay for volume work as a utility, not as a software seat.