AI Automation in Healthcare

What Is AI Automation in Healthcare?

AI automation in healthcare takes over the paperwork between providers and health plans that standard electronic transactions never covered, such as faxed referrals and the clinical notes payers ask to see before approving care. The 2025 CAQH Index estimates that fully automating the remaining manual and partly manual transactions would save another $21 billion a year across medical and dental care.

Much of the activity sits in the revenue cycle and patient access. Typical tasks include assembling prior authorization requests, suggesting diagnosis and procedure codes from clinical notes, checking claims before submission, and following up on denials. The technology is also automating patient intake and phone calls between provider offices and payers.

The same technology runs on both sides of the transaction. Providers use it to submit requests and appeal denials, while payers use it to review those requests. Regulators have focused on the payer side, where an automated denial can hold up a patient's treatment.

Why Are Healthcare Organizations Automating Administrative Work?

• Administrative cost is large and addressable. Every manual phone call or portal entry carries a staff cost. Automating high-volume, repetitive transactions lowers costs for both sides.

• Paperwork delays reach patients. A prior authorization that sits in a queue for days delays the treatment it covers. Faster processing shortens the time between a clinician's order and the patient's care.

How Does AI Automation in Healthcare Work?

• Clinical documents are read and structured. Referral faxes and scanned records pass through optical character recognition and language models that extract diagnosis codes and requested services a payer will require.

• Requirements are matched to the request. The system checks the payer's coverage criteria against the patient's record. HL7's Da Vinci implementation guides, starting with Coverage Requirements Discovery, standardize this exchange over FHIR, and CMS's prior authorization rule requires many payers to offer a Prior Authorization API from January 1, 2027.

• Submissions are drafted and sent. The system assembles the request with supporting documentation and submits it through an API when available. When the payer offers only a web portal, robotic process automation fills in the forms.

• Codes are suggested from clinical notes. Models read the encounter documentation and propose diagnosis and procedure codes. Certified coders review the suggestions, and some vendors code simpler encounters without review when confidence is high.

• Exceptions go to people. Cases the system is unsure about, or that need clinical judgment, are routed to staff with the relevant context attached. A coder sees the note excerpt behind each uncertain code, and a prior authorization specialist sees which payer criterion is still unmet.

What Tools Do Healthcare Organizations Use for AI Automation?

• Prior authorization automation. Cohere Health automates prior authorization review for health plans, and Infinitus uses voice AI to make benefit-verification and prior-authorization status calls to payers on behalf of providers and life sciences companies.

• Coding and revenue cycle. Fathom and CodaMetrix automate medical coding from clinical notes, and AKASA applies generative AI across revenue cycle tasks such as claim preparation and denial follow-up.

• General automation platforms. UiPath sells prebuilt healthcare automations for prior authorization and claim denials, and some health systems build their own payer-portal workflows on Microsoft Power Automate. The general approach is covered under intelligent automation.

What Are the Key Characteristics of AI Automation in Healthcare?

• Clinicians keep medical-necessity decisions. A February 2024 CMS FAQ tells Medicare Advantage plans that an algorithm may assist a coverage decision, but the decision has to rest on the individual patient's record and circumstances. California's SB 1120, in effect since January 1, 2025, requires a licensed physician or other qualified health professional to make any medical-necessity decision that denies or modifies care in state-regulated health plans.

• Legacy channels stay in scope. Much of healthcare still runs on fax and payer web portals. Automation tools work across those channels until APIs replace them, so voice AI and robotic process automation remain part of the toolkit.

• Actions are logged. The system records each submission and code suggestion with its source documents, so the organization can respond to an audit or appeal later.

• Success is measured in touchless rates. Programs track how many transactions complete without staff involvement, alongside error and denial rates.

What Are the Benefits of AI Automation in Healthcare?

• Lower cost per transaction. Each automated eligibility check or claim status inquiry replaces staff time, which adds up across millions of transactions a year.

• Faster prior authorization turnaround. Automated assembly and API submission shorten the time between an order and a decision. Since January 1, 2026, CMS-0057-F has required Medicare Advantage plans and Medicaid and CHIP programs to decide expedited requests within 72 hours and standard ones within seven calendar days.

• Fewer avoidable denials. Pre-submission checks catch missing documentation and coding errors before a claim goes out.

• Less time on hold with payers. Voice AI waits in payer phone queues and handles routine status calls, so patient access teams spend more of the day with patients.

What Are the Challenges of AI Automation in Healthcare?

• Payer-side AI draws scrutiny. Traditional Medicare's WISeR model has used AI vendors, Cohere Health among them, to review prior authorization requests in six states since January 2026. A Senate resolution to repeal it failed 46–50 in July 2026, and a House committee has approved FY2027 spending language that would defund it. Payers that automate reviews face more oversight, and clinician sign-off on every denial limits how far the automation can go.

• Errors repeat at scale. A coding model with a systematic mistake applies it to thousands of claims before anyone notices. Sampling audits catch these patterns, at the cost of keeping experienced coders in the loop.

• Portal automation is fragile. Robotic process automation breaks when a payer redesigns its website. Moving to APIs fixes this as payers roll them out ahead of the 2027 deadline.

• Vendor concentration creates risk. The 2024 Change Healthcare cyberattack showed how much of the industry depends on a few clearinghouses and vendors. Spreading work across vendors limits exposure, at the price of more integrations to maintain.

What Is the Difference Between Provider-Side and Payer-Side AI Automation?