What AI Can Automate in Prior Authorization Without Making Clinical Decisions

Where the automation boundary belongs

The safe boundary runs through the middle of the prior authorization workflow. Software can retrieve payer requirements, collect information, prepare submissions, monitor responses, and manage follow-up. Patient-specific medical reasoning remains with the treating clinician or another appropriately licensed professional.

This distinction matters for practice administrators, patient access leaders, and revenue cycle teams deciding whether to automate an authorization queue. The strongest first targets are repetitive tasks that consume staff time without requiring a judgment about diagnosis, treatment, or medical necessity.

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

Automation boundaries based on the electronic prior authorization functions defined by CMS-0057-F and the HL7 Da Vinci prior authorization workflow.
Workflow step	Recommended classification	What AI can safely do	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.	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.	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, and completed forms.	Confirm that the packet is accurate, clinically relevant, and sufficient for the request.
Complete forms and submit	Administrative automation	Populate payer fields, attach approved records, transmit the request, and log the submission.	Complete clinical attestations and review requests involving interpretation.
Check status	Administrative automation	Poll portals or APIs, retrieve updates, record reference numbers, and calculate follow-up dates.	Work exceptions that cannot be reconciled against the original request.
Conduct payer phone follow-up	Automatable until the conversation becomes clinical	Confirm receipt, status, missing documents, reference numbers, and expected response timing.	Answer questions about treatment selection, diagnosis, or medical necessity.
Respond to requests for more information	Split workflow	Classify the request, locate existing records, and route the case to the correct 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.	Decide how to answer a medical-necessity denial or whether the treatment plan should change.
Conduct a peer-to-peer review	Clinical	Schedule the review and prepare a concise case packet.	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

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, tracking, and routine follow-up

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, and identify missing attachments.

Status checking is often the cleanest first automation target. It is frequent, rules-based work, and the output is usually a fact rather than a judgment: pending, approved, denied, expired, or awaiting information.

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, 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 intended 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 repetitive retrieval, reference-number capture, and queue updates.
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 the packet and attestation controls are established.
Denial preparation: automate classification, deadlines, and document assembly while preserving clinical ownership of the response.

This sequence reduces authorization workload without asking the system to make progressively riskier judgments. It also gives the practice measurable evidence about exception rates before broader automation is attempted.

When automation is the right intervention

Prior authorization automation is a strong fit when specialists spend substantial time checking portals, calling for status, gathering records, and re-entering the same demographic or coverage 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 before adding headcount when the bottleneck is repetitive retrieval and documentation. Keep authorization specialists focused on incomplete cases, payer discrepancies, clinician coordination, and denials. The better comparison is not software versus one employee; it is automated queue work plus specialist exception management versus using specialists for every routine touch.

Where prior authorization automation breaks

Stale payer rules: a fast workflow using an outdated requirement can create avoidable denials or unnecessary authorization work.
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.
Authorization separated from scheduling: an approval that expires before the appointment is not operationally complete.
No exception owner: automation stalls when an unmatched patient, changed plan, unclear code, 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, and appointments protected from authorization failure.

An athenaOne example: catch the authorization need before booking

For an athenaOne practice, the useful intervention can happen before the authorization packet exists. A changed insurance plan can trigger an eligibility check and flag that the scheduled service requires prior authorization before the wrong appointment is committed. Pretty Good AI’s insurance workflow is one example of that pattern.

Pretty Good AI is built only for athenaOne and uses more than 730 athenaOne APIs in production. Its direct integration works without middleware, allowing insurance, authorization, and scheduling information to remain in the same operating workflow. Clinical decisions remain with the practice. Pretty Good AI platform details and integration and clinical guardrails

Frequently asked questions

Can AI do prior authorizations?

Yes, AI can perform a large share of the provider-side workflow. It can check requirements, gather coverage and demographic data, retrieve chart documents, populate forms, submit requests, monitor status, and prepare follow-up. It should escalate patient-specific medical reasoning, clinical attestations, medical-necessity denials, treatment alternatives, and peer-to-peer reviews to appropriately licensed staff.

Can AI determine whether prior authorization is required?

Yes, AI can query payer requirements using the member, plan, service, procedure codes, provider, and site of care. The system should record which payer response or rule produced the answer and route ambiguous results for review. CMS treats this requirement-discovery step as distinct from submitting the authorization itself. CMS Prior Authorization API FAQ

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

Automate status checks first when specialists are spending their day opening portals, calling payer lines, recording reference numbers, and updating queues. Those tasks are repetitive and produce administrative facts. Retain specialists for missing information, payer inconsistencies, clinician coordination, denials, and appeals. Track the exception volume after automation before deciding whether additional authorization headcount is still necessary.

Can AI write and submit a medical-necessity rationale?

AI can draft a rationale from documented chart facts, but the treating clinician should validate and own the clinical representation. The system should not add diagnoses, symptoms, contraindications, prior treatment failures, or conclusions that are absent from the record. The American Medical Association recommends physician review before an automated recommendation results in a limitation or denial of care. AMA policy on AI and health care

Does the CMS interoperability rule require fully automated prior authorization?

No. CMS-0057-F standardizes electronic functions such as discovering requirements, exchanging documentation, submitting requests, and receiving approval, denial, or additional-information responses. It does not turn medical-necessity judgment into an administrative software function or require practices to remove clinicians from clinical review.

Can AI handle denials and peer-to-peer reviews?

AI can prepare the work around them. It can retrieve the denial reason, separate administrative issues from clinical disputes, assemble records, identify deadlines, draft nonclinical sections, and schedule the peer-to-peer discussion. A clinician should determine the medical response and conduct any discussion about diagnosis, treatment, or medical necessity.