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AI is taking on defined administrative tasks in U.S. hospitals and medical groups, including billing support, scheduling, claims review, prior authorization, and document handling. The clearest adoption data is about predictive AI integrated with hospital electronic health records (EHRs)—not generative AI, and not every healthcare organization. Whether a tool saves time or reduces denials depends on its workflow fit, data connections, and human review.
What the adoption figures 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 surveyed non-federal acute care hospitals reported predictive AI integrated with their EHR, compared with 66% in 2023. The denominators were 2,080 hospitals in 2024 and 2,425 in 2023. ONC defines predictive AI here as statistical analysis or machine learning used to classify information or produce an individual risk score. These figures do not measure all forms of AI or all healthcare organizations. ONC’s 2025 analysis also found variation by organization: in 2024, reported use was 86% among system-affiliated hospitals versus 37% among independent hospitals, and 96% among large hospitals versus 59% among small hospitals.
Within hospitals using any predictive AI, the share reporting use to simplify or automate billing procedures rose from 36% in 2023 to 61% in 2024. Use to facilitate scheduling rose from 51% to 67%. ONC describes these as its fastest-growing predictive AI use cases. The percentages describe reported use, not measured productivity gains or proof that the tools improved access, accuracy, or revenue.
Medical-group polling offers a different view. In a September 30, 2025 poll of 351 applicable responses, the Medical Group Management Association (MGMA) found that 68% of respondents said their group had added or expanded AI tools in 2025. Respondents cited clinical documentation as a major focus, alongside scheduling, patient communications, coding and revenue-cycle work, denials, and prior authorization. Cost, unclear productivity gains, and incompatibility with EHRs were among reasons groups held back. This is a poll finding, not a population-wide estimate for U.S. practices. Read MGMA’s poll summary.
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Where AI can enter the administrative workflow
Billing, claims, and revenue cycle
Revenue-cycle tools may help identify coverage, support eligibility checks, flag claims with a higher risk of denial before submission, draft appeal letters, or assist with follow-up. These are different tasks: a flag is not a final adjudication, and generated appeal text still needs review against the claim and the payer’s rules.
The American Hospital Association (AHA) described a Fresno-area community health network that used a tool drawing on historical payment data and payer adjudication rules to flag likely denials. The AHA reported that the organization experienced a 22% decrease in prior-authorization denials by commercial payers and an 18% decrease in denials for services not covered; it also estimated 30–35 hours per week saved on back-end appeals. These are the health system’s reported results as relayed by the AHA, not independently established or typical outcomes. AHA’s account also stresses safeguards, including “having humans validate computer-generated outputs to prevent closed-loop automation.”
Scheduling and patient access
Scheduling support can include matching appointment requests to available slots, reminders, call-center or phone-tree assistance, routing messages, and patient communications. Predictive models may help prioritize or classify requests, but they depend on accurate schedules, usable patient information, and the organization’s rules for appointment types and urgency. A higher adoption rate alone does not establish that patients get appointments sooner or staff workloads fall.
Prior authorization and document handling
Generative AI can be used to search or summarize documents, help draft clinical documents, and assist with prior-authorization paperwork. These are possibilities, not evidence that the work is consistently completed accurately or that administrative burden has fallen across the sector. A Google Cloud summary of a Google Cloud and The Harris Poll study discusses such uses; its vendor-published findings should be treated as attributed claims rather than sector-wide proof. See the study summary.
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Administrative work often crosses an EHR, a payer portal or system, and other third-party software. ONC’s 2024 analysis of hospital API use identifies scheduling and intake, prior authorization, and quality reporting as administrative data-exchange uses between EHRs and third-party technology. Standards-based exchange is not universal: hospitals also use proprietary APIs and non-API methods. A promising model can still create extra work if staff must re-enter information, reconcile mismatched records, or move its output manually. ONC’s API analysis describes the range of exchange approaches.
For an organization evaluating a system, compare options on the actual work they cover rather than on broad claims of “AI automation.” Useful questions include:
- Task boundary: Does it check eligibility, flag denial risk, draft an appeal, or submit something? Which actions remain with staff?
- Compatibility: Can it exchange data with the EHR and relevant payer or administrative systems, and how are exceptions handled?
- Comparable evidence: Are accuracy, productivity, or financial results measured in a setting similar to yours, with a clear baseline?
- Validation and recovery: Who reviews consequential outputs, corrects errors, and prevents a bad result from propagating?
- Data governance: What information is accessed, where is it processed, and what security and access controls apply?
- Total cost: What are implementation, integration, training, and ongoing operating costs—not just the license price?
What responsible use requires
Keep human judgment in consequential workflows, particularly where an output could affect a claim, coverage determination, or a patient’s access to care. Validate generated content before it is sent or acted on, make it possible to catch and correct errors, and monitor performance across relevant groups and cases. A model’s historical data or a payer rule can be incomplete or outdated; an automated recommendation should not silently become a final decision.
Start with a bounded task and measure it against a baseline. Track operational outcomes that matter for that task—such as staff time, error and rework rates, denial patterns, or turnaround time—while also accounting for exceptions and review effort. Adoption figures and one organization’s reported savings cannot substitute for local measurement.
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The practical picture for healthcare organizations
AI is not one autonomous back-office system. It is a set of tools applied to distinct administrative jobs, each with its own data dependencies and failure modes. U.S. hospital evidence shows growing predictive AI use in billing and scheduling, while medical-group polling points to broader experimentation and continuing barriers. The useful question for a hospital or practice is not simply whether it uses AI, but whether a specific tool fits a specific workflow, connects reliably to the systems around it, and produces validated results worth its full cost.
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