Use Cases AI Agents Healthcare

What are use cases for AI agents in healthcare?

Use cases for AI agents in healthcare are tool-using jobs, such as assembling a prior authorization packet or sorting a referral queue, where the agent gathers data, drafts a change and hands it to a named reviewer. Only after acceptance does the agent write the change to the EHR, payer system or work queue.

Agent or plain model?

A plain model call is enough when the job ends in text a person reads, such as a summary. An agent is justified when the job needs several lookups, a rule check and a structured write, and when a person would otherwise copy data between screens to do it.

Eight agent jobs and the systems they touch

Each job below names what the agent reads, what it prepares and who accepts it. Keeping that triple explicit is what separates an agent design from a demo.

• Prior authorization packet: Reads orders, notes and coverage rules, assembles the packet and drafts the necessity narrative. A utilization or billing staff member accepts before submission.
• Referral intake: Reads incoming faxes or electronic referrals, extracts patient, reason and urgency, and drafts a referral record. An intake coordinator accepts and schedules.
• Inbox triage: Reads portal messages, labels each by type and urgency, and drafts a routing decision and reply. A nurse accepts the routing and edits the reply.
• Care-gap outreach: Reads quality measure gaps and recent visits, builds an outreach list and drafts messages. A care manager approves the list and the wording.
• Coding suggestions: Reads the signed note and charge rules, proposes codes and modifiers, and flags missing documentation. A coder accepts each line.
• Denial follow-up: Reads remittance codes and the claim, sorts denials by cause and drafts appeal letters or corrected claims. A biller accepts before resubmission.
• Trial pre-screening: Reads structured chart data against trial criteria and drafts a candidate list with reasons. A research coordinator confirms eligibility with the investigator.
• Scheduling assistance: Reads provider templates and patient preferences, and proposes slots. A scheduler or the patient confirms before the booking is written.

Which rules attach to an agent's writes?

Rules attach to what the agent changes and on whose behalf. A draft carries little regulatory weight. A coverage decision, a filed note or a submitted claim carries the same rules as when a person does it.

• Medicare Advantage coverage: CMS's February 2024 FAQ on CMS-4201-F says Medicare Advantage organizations may use algorithms to assist coverage decisions but cannot deny coverage based on an algorithm alone, without the individual patient's circumstances.
• Prior authorization APIs: CMS-0057-F requires impacted payers to stand up a Prior Authorization API from 1 January 2027, and CMS recommends, but does not require, the HL7 Da Vinci CRD, DTR and PAS guides for it.
• EHR write paths: The certified API under 45 CFR 170.315(g)(10) is a read API for patient and population services, so an agent that files data must use the EHR vendor's write APIs or an HL7 v2 interface.
• Audit controls: The HIPAA Security Rule at 45 CFR 164.312(b) requires mechanisms that record and examine activity in systems containing ePHI, which covers every tool call an agent makes.
• Untrusted inputs: NIST AI 600-1, the generative AI profile, describes indirect prompt injection, where instructions hidden in retrieved content steer a model, as an information security risk.

How should agent tools and permissions be designed?

Give the agent the narrowest tools that finish the job, and separate tools that read from tools that write. A tool that can do more than the job requires is a standing risk, even when the model behaves.

• Read tools: Expose FHIR searches for the specific resources the job needs, such as Coverage, ServiceRequest or DocumentReference, rather than a general query tool.
• Write tools: Expose one write tool per target, each taking a typed payload, and route every write through the review queue instead of calling the system directly.
• Scopes: Request SMART on FHIR scopes that match the job, for example patient-level read scopes for a single-patient task, rather than system-wide access.
• Identity: Run each write under the identity of the person who accepted it, so the source system records a real user.
• Limits: Cap the number of tool calls per task and the number of tasks per hour, and stop the run if a cap is hit.
• Dry run: Support a mode where write tools return the payload they would have sent without sending it, for testing and audits.
• Kill switch: Let an operations owner disable one tool or the whole agent without a code release.

What does a safe writeback look like?

A safe writeback is a fixed sequence in which nothing reaches the system of record until a person has accepted the exact payload. The sequence below fits EHR, payer and work-queue targets.

• 1. Draft: The agent builds a structured payload and stores it with its inputs, model version and tool trace.
• 2. Validate: Code, not the model, checks the payload against the target's schema, required fields and value sets.
• 3. Present: The reviewer sees the payload in plain terms, the sources behind each field and any validation warnings.
• 4. Accept or edit: The reviewer accepts, edits or rejects, and a reject reason is stored for evaluation.
• 5. Write once: The write carries an idempotency key, so a retry after a timeout does not create a duplicate order, note or claim.
• 6. Confirm: The agent reads the record back and compares it with the accepted payload, and flags any difference.
• 7. Log: Draft, acceptance, write and confirmation are linked in one audit record that compliance staff can search.

Agent failure points to test before launch

Agents fail in ways a single model call does not, because they take several steps and act on what they find. Each failure point below needs a test case in the evaluation set.

• Wrong patient: A search returns two patients with similar names, and the agent picks one. Require an exact identifier match before any write.
• Injected instructions: A faxed referral or outside note contains text telling the model to take an action. Treat retrieved content as data, never as instructions.
• Loops: The agent retries a failing tool without end. Cap retries and send the task to a person.
• Partial writes: Two of three writes succeed and the third fails. Design writes so a failure leaves a clear state that a person can finish.
• Stale data: The agent drafts from coverage or medication data that changed during review. Re-check key fields at write time.
• Scope creep: New tools are added for convenience until the agent can reach far more than the job. Review the tool list on every release.
• Silent model changes: A model update changes which tools the agent calls. Re-run the evaluation set on every model or prompt change.

How do you measure an agent job?

Measure an agent job by what happens at the review queue and the system of record, not by model scores alone. The metrics below come from the audit trail the writeback sequence already produces.

• Accept without edit: The share of drafts accepted as written, tracked per job and per field.
• Edit pattern: Which fields reviewers change most, which points to the tool or prompt that needs work.
• Reject reasons: Coded reasons for each rejection, reviewed weekly by the job owner.
• Cycle time: Time from intake to confirmed write, compared with the same job before the agent.
• Write corrections: Records that had to be changed or reversed after the write, the measure closest to patient or financial harm.
• Escalations: Tasks the agent handed to a person because of caps, errors or missing data.
• Tool calls per task: A rising count can signal looping or a change in model behavior.

Set a target for each metric before launch with the job owner, and agree what result pauses the agent. Without that, nobody can say when the pilot has failed.

How Agnotic can help

An agent is a small distributed system with a model in the loop, and Agnotic Technologies builds those systems for providers, payers and digital health companies that want one job automated end to end. Agnotic engineers define the read and write tools, set up SMART on FHIR scopes and vendor write paths, build the review queue and idempotent writeback, and write the evaluation cases for the failure points above.

Each agent ships with its audit trail, tool caps and kill switch in place, so the operations owner can see and stop what it does from the first day.

Sources

• CMS: HPMS FAQ on coverage criteria and utilization management, CMS-4201-F (February 6, 2024)
• CMS: Interoperability and Prior Authorization final rule (CMS-0057-F) fact sheet
• ASTP/ONC: 170.315(g)(10) Standardized API for patient and population services
• HL7: SMART App Launch, scopes and launch context
• 45 CFR 164.312: HIPAA Security Rule technical safeguards
• NIST AI 600-1: Generative Artificial Intelligence Profile (PDF)