AI in Healthcare Administration: A Complete Overview

Key Use Cases: How AI Automates Healthcare Administration

A 2025 survey from AHA and the Assistant Secretary for Technology Policy found billing and scheduling were the two fastest-growing use cases for AI in healthcare. Below is a closer look at how health systems bring AI-driven automation to these key administrative functions, along with a few others. Research from Gartner co-authored by Potts helps provide definitions and examples. 

Billing, Claims Processing and RCM Automation

Automated coding tools can analyze data in the EHR and other clinical systems and assign the appropriate diagnostic or procedural codes. This cuts down on the time it takes to properly code a patient visit. Claims can be submitted faster, which means organizations get reimbursed faster.

Keeping humans in the loop is an important part of this process, according to Gartner. If software determines a code for a medical service that doesn’t meet a predetermined level of confidence, the code will be flagged for a manual review. 

Prior Authorization Automation

Here, AI tools similarly analyze patient records, as well as clinical guidelines and preloaded payer requirements, to decide if a procedure or medication will require prior authorization. From there, Gartner notes, software can create documentation it deems a payer will need to conclude a service is medically necessary.

This use case takes on added importance amid the push for electronic prior authorization from the Centers for Medicare & Medicaid Services. It’s also an area where AHA advocates for having a clinician in the loop — especially on the payer side, Holloman says. If hospitals and health systems are forced to review and appeal denials, they face an undue administrative burden.

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Scheduling and Patient Communication

AI is well positioned to help organizations optimize schedules by analyzing patient demand, projecting the resources needed to meet that demand and recommending necessary staffing levels, Holloman says. This is particularly helpful for managing capacity in the operating room, where careful orchestration of surgeons, physical space and equipment is necessary to schedule procedures.

Meanwhile, different AI tools can help hospitals communicate with patients who have procedures scheduled. Gartner notes that use cases can include providing patients with tailored educational materials, generating treatment plans, offering pre-visit instructions and reminders, summarizing the clinical encounter, and creating post-visit instructions. While Gartner describes the financial and operational gains of these use cases as largely marginal, the consultancy does note a positive impact on patient satisfaction.

EHR Management and Documentation

Ambient listening and documentation is one of the most promising uses of AI in healthcare administration, Holloman says. Supporting transcription and creating structured clinical notes reduces the burden on staff to accomplish those tasks — especially during much-maligned after-hours “pajama time.”

Potts agrees: “This solves several issues at once.” Gartner’s research calls ambient scribes a “likely win” for a health system, noting that it can improve coding and reimbursement, save time, mitigate legal and regulatory risk, improve job satisfaction, and even aid clinician recruitment efforts.

Supply Chain Management

As with scheduling, using AI to manage hospital supply chains begins with assessing demand for medical equipment, medications, and any general supplies used in procedures or to support everyday patient care. Analyzing patterns in supply use can help reduce overstocking, Potts notes. It also lets organizations standardize orders so they can purchase items in bulk, reducing costs as well as variability of items available.

Benefits of AI in Healthcare Administration

Organizations tend to have two main motivations for automating administrative workflows, according to Potts. One is improving efficiency and reducing costs amid operations margins that were just 1.5% at the end of 2025, according to Strata Decision Technology.

The other is addressing the reality of an aging workforce. “Billers, coders and patient access staff are retiring, or almost ready to leave, and there’s no backfill for them that organizations can count on,” Potts says. As with other healthcare jobs, these roles can be difficult to recruit for, in large part because salaries aren’t always competitive relative to the experience required.

There are also secondary benefits to process automation, Holloman notes. Ambient documentation does more than simply improve patient and staff satisfaction. Because clinicians spend less time drafting notes, practices have greater capacity for appointments throughout the day — up to four hours in some cases, Holloman says. “That gives patients better access to care.”

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Implementation Challenges, Considerations and Best Practices

While healthcare is no stranger to AI, there’s still a lot for organizations to consider as AI tools and use cases evolve rapidly.

To determine where to get started, Potts recommends prioritizing value over complexity. A task that’s difficult but not done frequently may not justify the cost of automation, for example. Ideal tasks for automation couple high volume with little complexity.

As Potts put it, think of the tedious things “people are willing to have off their plate.” Ambient scribes in the exam room are an easy win, he says. So is automated coding, as the steps of revenue cycle management are largely defined by predetermined rules that predate the use of AI.

One challenge is that proofs of concept are often executed at a small scale. “Organizations tend to think of individual tasks instead of entire departments. They’re not thinking big enough,” Potts says. If automation speeds up one step of a process only to reinsert the completed task into a manual workflow, there’s little benefit to the organization.

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Other important considerations include the following:

• Governance. Holloman recommends that organizations convene multidisciplinary groups to look at how AI use cases will impact everything from EHR integration to workflow design to end-user education: “You need to do a comprehensive review of all the issues that may come up.”
• Cost. The cost of AI doesn’t come from the tools alone. There are upfront investments in technology infrastructure, to be sure, as well as the cost of training users and tweaking workflows. That may impact the timeline for achieving ROI, which Potts says seems to be getting shorter for AI implementations.
• Risk. “You need to be aware of the line between administrative and clinical use cases,” Holloman says. Potts recommends assessing the risk of automating a given workflow if the output is incorrect. He also highlights the importance of vetting third-party vendors for willingness to share risk with their healthcare customers.