AI is beginning to help with healthcare paperwork, especially clinical notes, coding, patient messages and prior-authorization administration. The clearest evidence points to assistance inside human-run workflows—not paperwork disappearing or AI making clinical coverage decisions. In an American Medical Association survey, 80% of surveyed physicians said AI for billing codes, medical charts or visit notes would be relevant to their practice; that measures interest, not proven effectiveness.
What healthcare paperwork can AI help with?
Administrative AI tools work on different tasks, and they should not be treated as interchangeable. Some draft text from a clinical encounter; others suggest billing codes, sort messages or help move information between providers and payers.
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Clinical notes and documentation
An ambient documentation tool processes a clinical encounter and prepares a draft note for a clinician to review and finalize. In one example described by the AMA, a Permanente Medical Group deployment used a secure smartphone microphone. The AMA says that tool transcribes rather than records encounters; that description applies to the cited example, not necessarily to other products. A draft can still omit details or include unsupported material, so clinician review matters.
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AI can suggest billing codes based on clinical documentation. The suggestions depend on the source record and the surrounding coding workflow; they are not a substitute for checking whether a code accurately reflects the documented care.
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Messages and records
AI may help scan communications to highlight important information, search patient documents or prepare a draft response to a portal message. A draft is a starting point for staff or clinicians, not a message that should automatically be sent without review.
Prior-authorization paperwork
Prior authorization involves exchanging information between a provider and a payer before certain care is covered. Electronic tools can help prepare or transmit structured information, but moving a request faster does not itself determine medical necessity, guarantee approval or make a denial appropriate.
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What do physicians want, and what evidence shows results?
The figures below describe different things: survey respondents’ views, reported adoption and specific outcome examples. They are not directly comparable measures of AI effectiveness.
| Evidence | What was reported | How to interpret it |
|---|---|---|
| AMA physician survey, published 2025 | 80% said AI use for “Billing codes, medical charts or visit notes” would be relevant to their practice; 72% selected discharge instructions, care plans or progress notes; 57% selected “Generation of draft responses to patient portal messages.” | Views of relevance, not proof of deployment or efficacy. The survey covered sentiment from August 2023 to November 2024 and included nearly 1,200 physicians. AMA survey and examples |
| AMA adoption research, reported by GAO in 2026 | 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 is a survey finding about specified tasks, not a measure of all healthcare organizations. GAO overview |
| AI scribe study reported by GAO in 2026 | One study found documentation time fell by 20%, or two minutes per appointment. | This is a result from one study as summarized by GAO; the cited passage does not establish a universal saving or identify further study details. GAO overview |
| Developer-reported coding deployment recounted by GAO in 2026 | A developer reported more than 95% accuracy in generated codes and more than $1 million in annual coding cost reduction in emergency departments at a five-hospital health system. | These are developer-reported results from one system, not an independent general benchmark or guarantee. GAO overview |
The distinctions matter. Physician interest does not establish that a tool works well; rising reported use does not show that adoption is widespread everywhere; and results from an individual study or deployment do not promise the same outcome elsewhere.
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Why is prior authorization a policy focus?
CMS estimates that providers spend an average of 13 hours per week on prior authorization. At an estimated provider cost of $20–50 per hour, CMS puts the annual burden at roughly 700 hours and $34,000 per provider. These are CMS estimates, not measurements of every practice’s workload or costs. CMS Electronic Prior Authorization overview
CMS’s Interoperability and Prior Authorization Final Rule, CMS-0057-F, requires impacted payers to implement a FHIR-based prior-authorization API. CMS said generally applicable API compliance dates shifted from January 1, 2026, to January 1, 2027. Applicability depends on payer type, and the rule is about electronic information exchange—not a requirement that generative AI approve requests or prevent denials. CMS rule announcement
CMS’s overview also describes a June 2025 stakeholder pledge, the WISeR model, which uses enhanced technologies such as AI and machine learning with clinician review, and 2026 proposed rulemaking. A voluntary pledge, a model, a proposal and a finalized requirement have different status; they should not be read as equivalent mandates or evidence of proven results.
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AI-generated notes, codes and messages can look convincing while being wrong. The AMA identifies hallucinations, bias, privacy, security, transparency and evolving liability as concerns. A sound workflow makes it clear who checks the output, who corrects it and who signs or submits the final record.
- Match the tool to the task: confirm whether it drafts notes, suggests codes, handles messages, searches records or supports authorization. A system built for one task may not handle another.
- Check integration: understand how the tool fits the electronic health record, data-exchange standards and payer processes—and whether staff still need to re-enter information in separate portals.
- Keep review accountable: identify who verifies, edits and approves generated content, and how omissions, unsupported statements, coding disagreements or failed submissions are surfaced.
- Examine privacy and security: ask how protected health information is handled, retained and accessed, and whether it is sent to a public or third-party platform.
- Judge the evidence by its scope: distinguish independent studies from a vendor’s report, and ask what baseline, setting and workflow the result actually covers.
CMS’s responsible-AI guidance warns CMS employees, contractors and people or organizations doing work on CMS’s behalf not to enter PII, PHI or sensitive CMS data into public AI platforms, and calls for third-party outputs to be checked. It is guidance for that covered CMS context, not a blanket statement of rules for every healthcare provider. CMS responsible AI guidance
The AMA’s broader overview discusses potential uses and risks of AI in healthcare. AMA Future of Health overview
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