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AI is beginning to help with healthcare’s administrative workload by drafting clinical notes, suggesting billing codes, sorting messages and supporting electronic prior authorization. The evidence points to assistance inside clinician-run workflows—not paperwork disappearing or AI making clinical decisions on its own.
Which healthcare paperwork tasks can AI assist with?
Administrative AI is a collection of task-specific tools, not one system that handles every form or record. The main uses include turning an encounter into a draft note, suggesting codes from documentation, organizing incoming information and helping staff assemble authorization requests.
Drafting clinical notes
An ambient documentation tool processes a clinical encounter and generates a note for a clinician to review, edit and finalize. In one example described by the American Medical Association (AMA), Permanente Medical Group used a secure smartphone microphone with a tool the AMA said transcribed rather than recorded encounters. That description applies to this example, not necessarily to other products.
Suggesting billing codes
Coding tools can propose codes based on clinical documentation. The suggestions depend on what the record says and still need to be checked in the relevant coding and billing workflow. A code suggestion is not the same as a verified claim.
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Sorting messages and records
AI can help scan communications or documents to surface information for staff. Drafting a response to a patient-portal message is another proposed use: a clinician or authorized staff member must check that a draft is accurate, appropriate and consistent with the patient’s record before sending it.
Helping with prior authorization
Authorization workflows involve gathering clinical information, completing requests and exchanging them with payers. AI or other automation may help organize documentation, but electronic exchange standards and generative AI are different things. An API can move structured information between systems; it does not by itself decide whether a service is medically necessary or guarantee approval.
What do physician interest and adoption figures show?
AMA survey results show which uses physicians considered relevant, not whether those uses were deployed or effective. In a survey of nearly 1,200 physicians covering sentiment from August 2023 through November 2024, 80% said AI use for “Billing codes, medical charts or visit notes” would be relevant to their practice. Seventy-two percent selected discharge instructions, care plans or progress notes, and 57% selected “Generation of draft responses to patient portal messages.” These are survey labels and relevance ratings, not measured outcomes or quotations from individual physicians. AMA’s survey and examples
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Use has grown, but the available figure is specific to surveyed clinicians and particular tasks. The U.S. Government Accountability Office (GAO) reports that AMA research found the share of surveyed clinicians using AI tools to assist with clinical documentation or medical coding rose from 21% in 2024 to 28% in 2026. This does not mean that 28% of healthcare organizations use AI, or that the same share use it for every administrative task. GAO’s 2026 overview of AI for medical notes and coding
What evidence is there that these tools save time or money?
The reported results are promising but come from different kinds of evidence. They should not be combined into a universal savings estimate.
Documentation time
GAO reports that one study found clinicians using AI scribes reduced documentation time by 20%, or two minutes per appointment. The cited passage does not establish that every practice, clinician or type of visit would see the same result.
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Coding accuracy and cost
GAO also recounts a developer’s report from a five-hospital health system: its software generated codes with more than 95% accuracy and reduced annual coding costs in emergency departments by more than $1 million. Those are developer-reported results from one deployment, not an independent general benchmark or a guarantee for another organization. They do not establish that the tool alone caused the savings.
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How is CMS changing the prior-authorization workflow?
Prior authorization is a substantial administrative task, and CMS puts numbers on the burden. The Centers for Medicare & Medicaid Services estimates that an average provider spends 13 hours per week on prior authorization, at an estimated provider cost of $20–50 per hour—roughly 700 hours and $34,000 annually per provider. These are CMS estimates, not a measurement of every provider’s workload. CMS’s Electronic Prior Authorization overview
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CMS finalized the Interoperability and Prior Authorization Final Rule, CMS-0057-F, in January 2024. It requires impacted payers to implement a FHIR-based prior-authorization API. CMS said generally applicable API compliance dates moved from January 1, 2026, to January 1, 2027. Applicability depends on payer type and the rule’s scope; an API requirement is an interoperability policy, not a mandate to use generative AI or a promise that requests will be approved. CMS’s final-rule announcement
Structured electronic submissions can reduce some manual form and portal work when the systems on both sides can exchange the information. CMS’s overview also discusses a voluntary stakeholder pledge, the WISeR model, and proposed rulemaking. A pledge or proposal should not be mistaken for a final requirement; check CMS’s current materials for the applicable payer, program and implementation date.
What should a healthcare organization check before adopting an administrative AI tool?
Assess the tool against the actual workflow rather than treating “healthcare AI” as a single capability.
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- Task fit: Confirm whether the system drafts notes, suggests codes, sorts messages, searches records or supports authorization. Evidence for one task does not demonstrate capability in another.
- Integration: Check how it connects to the electronic health record and other systems, whether it supports needed data-exchange standards, and whether staff will still have to re-enter information or switch between payer portals.
- Review and accountability: Identify who checks, edits and signs each output, how errors are corrected, and who remains responsible for notes, codes and submissions.
- Evidence quality: Distinguish an independent study from survey interest, a single deployment or a developer-reported result. Ask what was measured and against which baseline.
- Privacy and security: Understand how protected health information is handled, including access, retention and whether information is sent to a public or third-party system.
- Exceptions and accuracy: Find out how the tool flags missing details, unsupported text, coding disagreements, denials and system failures—and what staff do when it cannot complete a task.
Why do human review and privacy still matter?
A generated note can omit a detail, add unsupported information or misrepresent what happened during an encounter. A coding suggestion can affect a claim. A message draft can be misleading or inappropriate for a particular patient. These risks make review part of the workflow, not an optional final polish.
The AMA identifies hallucinations, bias, transparency, privacy, security and evolving liability as concerns for health-related AI. CMS separately advises its employees, contractors and people or organizations working on its behalf not to enter personally identifiable information, protected health information or sensitive CMS data into public AI platforms, and calls for third-party outputs to be checked. That is guidance for CMS’s covered context; it should not be presented as a universal rule for every provider or as a complete statement of HIPAA requirements. AMA’s overview of AI considerations in health care · CMS guidance for responsible AI use at CMS
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