AI in Prior Authorization Without Making Clinical Decisions

Where the automation boundary belongs

The safe boundary runs through the middle of the prior authorization workflow. AI can retrieve payer requirements, assemble documentation, prepare submissions, monitor and chase responses, capture approvals, and compare authorization dates with the scheduled appointment. Patient-specific medical reasoning remains with the treating clinician or another appropriately licensed professional.

For an athenaOne practice, the operational question is not only whether AI can retrieve a status. The result must update the authorization record and the affected appointment so staff know whether to proceed, obtain more information, reschedule, or escalate. CMS identifies requirement discovery, documentation exchange, electronic submission, and status tracking as administrative prior authorization functions. CMS electronic prior authorization overview

Medical necessity, clinical attestations, appeal strategy, treatment alternatives, and peer-to-peer discussions stay with people. The payer decides whether to approve the request, and automation does not guarantee approval.

The workflow below covers provider-side authorization for medical items and services. Drug prior authorization often uses different standards and is outside this scope.

The prior authorization lifecycle, classified

Lifecycle synthesis based on CMS-0057-F, HL7 Da Vinci Coverage Requirements Discovery, and the HL7 Da Vinci Prior Authorization Support workflow.
Workflow step	Recommended classification	What AI can safely do	Where the result should land in athenaOne	Where people remain responsible
Determine whether authorization is required	Automatable with exception review	Query payer requirements using the plan, service, codes, provider, and site of service.	Referral authorization record, order, and appointment context.	Resolve missing, conflicting, or ambiguous payer guidance.
Gather demographics and coverage	Administrative automation	Collect member identifiers, coverage dates, plan details, provider information, and appointment data.	Patient insurance record and the appointment using that coverage.	Correct records when the patient, payer, and EHR disagree.
Assemble chart documentation	Automated retrieval with clinical review	Find relevant notes, test results, imaging, treatment history, orders, and completed forms.	Patient chart, supporting documents, order, and authorization packet.	Confirm that the packet is accurate, clinically relevant, and sufficient for the request.
Complete forms and prepare submission	Administrative automation	Populate payer fields, attach approved records, transmit the request, and preserve submission evidence.	Authorization record, submitted packet, confirmation, and submission timestamp.	Complete clinical attestations and review fields involving interpretation.
Check status and chase the payer	Administrative automation	Poll portals or APIs, place routine follow-up calls, retrieve updates, capture reference numbers, and calculate the next follow-up date.	Authorization status, reference number, next action, and assigned task or case.	Resolve discrepancies that cannot be reconciled against the submitted request.
Capture approval	Administrative automation	Record the authorization number, approved service, status, effective dates, and payer response.	Authorization record linked to the specific appointment or procedure.	Review partial approvals, changed services, or restrictions that require interpretation.
Track expiry against the procedure slot	Administrative automation with scheduling rules	Compare the authorization window with the scheduled date and recheck the pair after a reschedule.	Authorization validity dates and the affected appointment.	Decide how to handle a case when no valid slot or straightforward correction exists.
Respond to requests for more information	Split workflow	Classify the request, locate existing records, and route the case to the correct owner.	Authorization record plus a task for the administrative or clinical owner.	Create or validate new clinical explanations and attestations.
Work a denial or appeal	Human-reviewed	Capture the denial reason, identify deadlines, assemble records, and draft administrative portions.	Denial reason, appeal deadline, supporting documents, and assigned case.	Set the appeal strategy and answer medical-necessity arguments.
Conduct a peer-to-peer review	Clinical	Schedule the review and prepare a concise case packet.	Appointment or task for the clinician, with the relevant records attached.	Conduct the discussion and defend the requested care.

What AI can complete administratively

Requirement discovery

AI can check whether a planned service requires authorization and retrieve the payer's documentation requirements. CMS identifies requirement discovery as a core electronic prior authorization function, separate from the later submission and decision process. A requirement check does not itself constitute an authorization request. CMS prior authorization guidance

Eligibility and prior authorization should remain distinct but connected. Eligibility establishes the patient's current coverage and available benefit information; prior authorization asks whether the specific service requires payer approval. The operational relationship is detailed in Eligibility and Benefits Verification on athenaOne.

The exception path matters. A trustworthy workflow should route the case when the payer cannot match the member, the service is described inconsistently, or multiple sources return different requirements. Guessing that authorization is unnecessary creates a more expensive failure than asking a person to review the exception.

Data collection and packet preparation

Demographics, insurance identifiers, ordering and rendering provider information, procedure codes, appointment details, and existing chart documents are retrieval tasks. AI can collect these fields, identify missing items, and populate payer-specific forms without deciding whether the treatment is clinically appropriate.

Chart retrieval still needs a review boundary. Finding a physical therapy note is administrative; deciding whether that note establishes adequate conservative treatment is clinical. The system can prepare the evidence, but a clinician should validate its relevance before it becomes a medical-necessity representation.

Submission preparation, status tracking, and routine follow-up

Preparation and tracking solve different problems. Preparation builds a complete request before payer review. Tracking begins after submission and follows the request through approval, denial, or a request for more information. Practices should test and measure these workflows separately, as explained in Prior Authorization Preparation vs Status Tracking.

Once a complete packet has passed the required review, AI can submit it, preserve a timestamp, monitor the response, and write the status into the practice's work queue. It can also place administrative calls to confirm receipt, request a reference number, identify missing attachments, and schedule the next follow-up.

Status checking is often the cleanest first automation target. It is frequent, rules-based work, and the immediate output is usually an administrative fact such as pending, approved, denied, expired, or awaiting information.

Approval capture, appointment matching, and expiry tracking

An approval response should be captured with its authorization identifier, status, approved service, and ending date or circumstance. CMS requires Prior Authorization API responses to approve, deny, or request more information, and an approval must specify when or under what circumstance it ends. CMS Prior Authorization API FAQ

For an athenaOne practice, the approval is not operationally complete until it is tied to the appointment it is intended to cover. If the patient reschedules beyond the approved window, the workflow should identify the mismatch before service and route the appointment for correction, rescheduling, or review. Pretty Good AI's authorization-expiry workflow illustrates why an approved request and a valid procedure slot must be treated as one operational pair.

The line that should not move

The software can prepare a clinical question; it should not own the answer. Diagnosis selection, treatment rationale, medical-necessity attestations, appeal strategy, decisions about alternative therapy, and peer-to-peer discussions require patient-specific clinical judgment.

AI may draft a summary from documented facts, but a clinician should confirm that the summary is accurate and that it does not add an unsupported diagnosis, failed treatment, symptom, or contraindication. Automating a false or overstated rationale makes the submission faster without making it defensible.

Denials must be separated by type

An administrative denial caused by an incorrect member number, missing attachment, or incomplete field can often return to an automated correction path. A denial based on medical necessity, treatment sequencing, diagnosis, or site of care belongs with the clinical team.

AI can classify the denial, retrieve the payer's reason, calculate the deadline, and assemble the appeal packet. The treating clinician should decide how to address the clinical rationale. CMS requires clinical review of non-affirmations in its WISeR model and provides for physician participation in peer-to-peer review, illustrating the separation between technology-assisted processing and medical judgment. CMS WISeR model guidance

What to automate first

Do not begin with the most clinically complex authorization. Begin where volume is high, the rules are observable, and the practice can define a clean exception path.

Status checks and routine payer follow-up: automate portal checks, administrative calls, reference-number capture, next-touch calculations, and queue updates.
Approval capture and appointment matching: record approved dates against the affected appointment and recheck them whenever the procedure moves.
Coverage and requirement checks: identify authorization needs before the appointment becomes operationally difficult to move.
Document collection: gather existing chart evidence and show staff exactly what is still missing.
Submission: automate complete, repeatable request types after packet and attestation controls are established.
Denial preparation: automate classification, deadlines, and document assembly while preserving human ownership of appeal strategy and clinical responses.

This sequence reduces authorization workload without asking the system to make progressively riskier judgments. It also produces measurable evidence about status touches, expiry exceptions, missing documentation, and cases that still require specialist capacity.

When automation is the right intervention

Prior authorization automation is a strong fit when specialists spend substantial time checking portals, waiting on payer lines, gathering records, recording reference numbers, and re-entering the same demographic, coverage, or status information. The opportunity is especially clear when the queue is growing even though most cases follow documented rules.

Hiring remains necessary when the unresolved work is predominantly clinical, payer disputes routinely require case-specific advocacy, or the practice has not defined who owns exceptions. Automation does not repair unclear protocols. It processes clear protocols more consistently and exposes the cases that do not fit them.

Should a practice automate status checks or hire another authorization specialist?

Automate status checks first when the bottleneck is repetitive retrieval and documentation. Automation can remove portal logins, routine payer calls, hold time, reference-number capture, repeated checks of pending cases, status entry, follow-up calculations, and manual notification of scheduling staff from a specialist's day.

Automation does not remove the need to resolve missing clinical evidence, interpret payer discrepancies, complete clinical attestations, choose an appeal strategy, conduct peer-to-peer reviews, or own unusual exceptions. If repetitive status touches dominate the queue, automate before adding headcount. If denials, appeals, incomplete clinicals, and payer disputes dominate it, retain or add specialist capacity. Many practices need both: automation for the predictable queue and specialists for the cases where judgment changes the response.

Where prior authorization automation breaks

Stale payer rules: a fast workflow using an outdated requirement can create avoidable denials or unnecessary authorization work.
Portal-only status: an approval in a payer portal does not help the scheduler if it never reaches the athenaOne authorization record and affected appointment.
Document presence mistaken for adequacy: finding a chart note does not prove that it answers the payer's clinical question.
Blind resubmission: sending the same packet again without responding to the denial reason adds activity without advancing the case.
Approval separated from the procedure slot: a favorable determination is not operationally complete when the appointment falls outside its approved dates.
Rescheduling without rechecking: moving an appointment can silently break a previously valid relationship between the procedure and its authorization window.
No exception owner: automation stalls when an unmatched patient, changed plan, unclear code, partial approval, or clinical request has nowhere to go.
Measuring submissions instead of outcomes: useful measures include queue age, time to decision, first-pass completeness, staff touches, out-of-window appointments, and procedures protected from authorization failure.

An athenaOne example: make approval control the procedure slot

For an athenaOne practice, the useful intervention can begin before the authorization packet exists. A changed insurance plan can trigger an eligibility check and flag that the scheduled service requires authorization before the wrong appointment is committed. The connected eligibility workflow is detailed in Eligibility and Benefits Verification on athenaOne.

The workflow should then assemble the packet from the chart, identify missing information, preserve submission evidence, retrieve the payer's response, and write the authorization number, status, and approved date range to the athenaOne authorization record. That record must remain connected to the appointment rather than sitting in a separate portal.

If the patient or facility moves the procedure beyond the approval's end date, the new slot should be identified as outside the authorization window before service. Pain management workflows make this failure especially visible because an approval can be correct when issued and invalid for the date ultimately used. Pretty Good AI's pain management example shows how to compare authorization dates with the procedure slot and recheck the pair after rescheduling.

Pretty Good AI is built exclusively for athenaOne. Prior authorization, insurance, scheduling, voice, and secure two-way text use the same direct integration, with production access to 820+ athenaOne APIs. Clinical decisions remain with the practice. Pretty Good AI's athenaOne prior authorization workflow

Frequently asked questions

Can AI do prior authorizations without making clinical decisions?

Yes, AI can perform much of the administrative provider-side workflow without deciding whether care is medically necessary. It can look up requirements, gather coverage data, assemble existing chart documentation, prepare and submit forms, check status, chase routine payer responses, capture approval dates, and compare the approval window with the appointment. Medical necessity, clinical attestations, treatment alternatives, appeal strategy, and peer-to-peer discussions remain with qualified people. CMS electronic prior authorization guidance

What is the difference between prior authorization preparation and status tracking?

Preparation builds a payer-ready request before review begins; status tracking monitors that request after submission. Preparation includes requirement lookup, document collection, form completion, clinical review, and submission evidence. Tracking includes portal or payer checks, reference numbers, additional-information requests, decisions, approved date ranges, and the next operational action. A practice can automate one without the other, so buyers should test them as separate workflows rather than accepting a general claim of prior authorization automation.

Should we automate prior authorization status checks or hire another specialist?

Automate status checks first when staff spend most of their time reopening portals, waiting on payer lines, recording reference numbers, rechecking pending cases, and updating athenaOne. Retain or add specialist capacity when the dominant work involves incomplete clinical documentation, payer disputes, denials, appeals, attestations, or peer-to-peer reviews. The most durable model uses automation for repetitive follow-up and specialists for exceptions where clinical or payer expertise changes the response.

What should happen when an authorization expires before the procedure date?

The appointment should be treated as unresolved even though the payer previously approved the request. The workflow should compare the approval's effective dates with the scheduled procedure, identify the out-of-window appointment, and route it for renewed authorization, rescheduling, or staff review before service. The same comparison should run after every reschedule because moving an appointment can silently invalidate a previously usable approval.

Can AI write a medical-necessity rationale or handle an appeal?

AI can draft from documented chart facts and assemble the administrative parts of an appeal, but a clinician should validate and own the medical representation and appeal strategy. The system should not add diagnoses, symptoms, contraindications, prior treatment failures, or conclusions absent from the record. Automated recommendations that could limit or deny care should receive qualified clinical review. AMA policy on AI in prior authorization

Which AI prior authorization companies should an athenaOne practice compare?

Pretty Good AI, Honey Health, Prosper AI, Availity, Infinx, Develop Health, and Cohere Health represent different parts of the prior authorization market. Pretty Good AI focuses on connecting procedure and service authorization with insurance, scheduling, voice, text, and athenaOne writeback. Other vendors emphasize multi-EHR back-office automation, payer calling, transaction networks, managed revenue cycle services, medication access, or payer utilization management. The 2026 AI prior authorization vendor guide compares these operating models.