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AI is taking on specific administrative tasks in U.S. healthcare, including billing, scheduling, claims review, prior authorization and document handling. Adoption is growing, but the evidence does not show that every organization uses the same tools—or that adoption automatically reduces workload or improves access. The clearest national figures concern predictive AI integrated with hospital EHRs, not generative AI across all healthcare organizations.
What the adoption numbers actually show
The Office of the National Coordinator for Health Information Technology (ONC) analyzed the 2023–2024 American Hospital Association Information Technology Supplement. In 2024, 71% of non-federal acute care hospitals reported predictive AI integrated with their EHR, up from 66% in 2023. The 2024 survey denominator was 2,080 hospitals; the 2023 denominator was 2,425. ONC defines predictive AI here as statistical analysis or machine learning used to classify or produce an individual risk score. These figures do not measure generative AI adoption or all healthcare organizations. ONC’s 2025 analysis describes the pattern as “early evidence suggests a persistent digital divide in hospitals’ adoption and use of predictive AI.”
Among hospitals using any predictive AI, the share reporting it for simplifying or automating billing procedures rose from 36% in 2023 to 61% in 2024; use to facilitate scheduling rose from 51% to 67%. ONC identifies both as the fastest-growing predictive AI use cases in its study. These are reported use-case shares, not measured productivity gains.
Adoption also differed by hospital type in 2024: predictive AI use was reported by 86% of system-affiliated hospitals versus 37% of independent hospitals, and by 96% of large hospitals versus 59% of small hospitals. A deployment pattern that works in a large health system may not transfer directly to an independent or smaller organization.
#1 Best Overall
Where AI enters back-office work
Billing, revenue cycle and claims
Revenue-cycle tools can help identify coverage, support eligibility checks, flag claims that may be denied before submission, draft appeal letters and assist with follow-up. Predictive systems can compare claim details with historical payment patterns or payer adjudication rules; generative systems may help prepare text for a person to review. Those are different capabilities: a risk score is not an appeal letter, and neither guarantees that a claim is correct or payable.
The American Hospital Association (AHA) describes a Fresno-area community health network that used a tool to flag likely denials based on historical payment data and payer rules. The health system reported a 22% decrease in prior-authorization denials by commercial payers, an 18% decrease in denials for services not covered, and estimated 30–35 hours per week saved on back-end appeals. These are outcomes reported by one organization through the AHA, not independently established benchmarks or expected results for other providers. The AHA account also recommends “having humans validate computer-generated outputs to prevent closed-loop automation.”
Scheduling and patient access
Predictive AI may support scheduling by helping identify patterns or prioritize work. Medical groups also report using AI for reminders, call-center and phone-tree support, message routing and patient communications. Whether that makes appointments easier to obtain depends on the data and operational rules behind the tool—for example, how the organization handles appointment types, clinician availability and exceptions. The rise in reported use does not itself establish shorter waits, fewer missed appointments or less staff work.
Prior authorization and document handling
Prior authorization involves assembling and exchanging information required by a payer before some services are covered. AI may assist staff with finding relevant documents, organizing information, drafting clinical documents or supporting authorization workflows. Google Cloud’s summary of a Google Cloud and The Harris Poll study presents document search, clinical-document creation and prior-authorization support as possible generative AI applications. These are described uses and possibilities, not proof that such systems have delivered results across the sector. Read Google Cloud’s study summary.
Administrative work often crosses an EHR and third-party systems, so a tool’s ability to exchange the necessary data matters as much as its model. ONC’s 2024 API analysis identifies scheduling and intake, prior authorization and quality reporting among administrative data-exchange uses between hospital EHRs and third-party technology. Standards-based exchange is not ubiquitous: proprietary APIs and non-API methods are also used. ONC’s API analysis describes the range of approaches.
Medical-group adoption beyond hospitals
Hospitals are not the only organizations adopting these tools. In a poll dated September 30, 2025, 68% of 351 applicable medical-group respondents said they had added or expanded AI tools in 2025. MGMA described clinical documentation as a major focus, with scheduling, patient communications, coding and revenue-cycle work, denials and prior authorization among other uses. Respondents also cited cost, unclear productivity gains and EHR incompatibility as reasons for holding back. The result reflects the applicable poll respondents, not a population-wide estimate of U.S. medical groups. MGMA’s poll report provides the findings.
Why integration and human oversight matter
Back-office AI is a set of task-level applications, not one autonomous operating system. A scheduling assistant, a claim-risk model and a document-search tool use different inputs and can fail in different ways. Administrative workflows may depend on data from the EHR, payer systems and other software; ONC’s findings show that organizations use a mix of exchange methods. Compatibility should be checked against the specific systems and workflow, rather than assumed from a product’s general AI claims.
Outputs can affect whether a claim is submitted, how coverage is interpreted or whether a patient gets routed to the right next step. A practical deployment therefore keeps accountable staff involved in reviewing consequential outputs, provides a way to correct errors and avoids automatically feeding unverified output into the next decision. Teams should also assess data governance, security, error patterns and the effect of incorrect or incomplete information on different patients.
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How to evaluate an AI workflow before adopting it
Use the specific administrative task—not the label “AI”—as the unit of evaluation. The following questions synthesize the integration issues, reported adoption barriers and AHA’s guidance on validation; they are not a standardized vendor framework.
Quick Recap
- Define the task boundary. Specify whether the system checks eligibility, predicts denial risk, drafts an appeal, routes messages or performs another task. Identify what remains a staff decision.
- Confirm system compatibility. Map the EHR, payer and administrative systems involved, and establish how data will move between them. Ask which exchange methods are supported and what happens when an interface or data feed fails.
- Ask for comparable evidence. Look for measured productivity, accuracy or financial results in a setting comparable to yours. Separate survey reports of adoption from measured outcomes, and a single organization’s reported results from general expectations.
- Test errors and review controls. Determine how staff see, correct and document mistakes; who validates consequential outputs; and whether the system can trigger an action without human review.
- Review data governance and security. Establish what information the tool accesses, how it is handled, who can see it and what controls apply.
- Calculate total cost. Include implementation and integration, ongoing operation and the staff time needed to monitor output—not just the quoted software price. Compare these costs with gains measured in the workflow being considered.
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