Automation in hospital auditing: what AI does

Denial rates at Brazilian hospitals have never been so high. According to the Anahp Observatory 2025, from Anahp (National Association of Private Hospitals), the average managerial initial denial rate reached 15.89% in 2024, compared with 11.89% in 2023 and a historical range of 3% to 5%. For an industry that already operates on thin margins, this growth is a sign that audit processes need to evolve.

Artificial intelligence has been gaining ground as one of the tools to improve this rate. Hospitals that have adopted automation solutions in the revenue cycle already report significant reductions in billing time and denial rates. But the technology is still far from universal: most Brazilian institutions run predominantly manual processes, and the transition requires planning.

This article shows, in practical terms, which stages of hospital auditing can be automated with AI, which still depend on human judgment and how to make this transition in a way that improves results.

What automated hospital auditing does in practice

The term automation may suggest replacing professionals with machines, but in the context of hospital auditing the concept is more precise: it means identifying which tasks in the process take the most time, repeat most often and depend least on clinical judgment, so that AI systems can perform them faster and with a smaller margin of error.

These tasks account for a significant share of the daily workload of billing and audit teams. When they are concentrated in manual work, they limit the team’s capacity to focus on more complex and strategic analyses.

Here are the main ones.

Checking charge entries and missing items

AI checks whether all the procedures, materials and medications delivered during care were entered correctly on the hospital claim. This includes identifying items that were administered but not charged, amounts outside the contracted price tables and inconsistencies between what is recorded in the system and what will be sent to the payer.

In hospitals with a high volume of admissions, this kind of check covers hundreds of lines per claim. Automating this stage frees the auditor to work where a clinical eye makes a difference.

Reading medical records and cross-checking them against the claim

AI systems read medical records and clinical documents automatically and cross-check this information against the hospital claim. The goal is to identify discrepancies between what was recorded clinically and what was charged, documentation gaps that can lead to denials and inconsistencies that need to be corrected before submission.

This cross-check is one of the pillars of concurrent auditing: the earlier discrepancies are identified during the hospital stay, the lower the risk of denial and the less time the claim stays at the hospital.

Automatic XML submission to payers

Sending the XML file to payers involves rules specific to each health plan: price tables, schedules, formats and contractual requirements that vary from payer to payer. AI validates the file before submission and corrects inconsistencies while respecting the particulars of each contract.

This process is regulated by ANS Normative Resolution No. 305/2012 (updated by Normative Resolution No. 501/2022), from the ANS (Brazil’s National Supplementary Health Agency), which establishes the TISS standard (Supplementary Health Information Exchange) for the electronic submission of medical claims. Automation ensures compliance with this standard and helps reduce the rate at which payers reject files.

Identifying error patterns with machine learning

With continued use, machine learning–based systems learn which types of error appear most often in each hospital’s operation: which procedures are most often left out of the charge entries, which specialties generate the most discrepancies, which payers have the most specific requirements.

This learning turns AI into a process improvement tool. The audit team starts receiving targeted alerts, focusing attention on the highest-risk points instead of going through the whole claim from scratch in every cycle.

Learn more about how AI agents create hospital efficiency.

What still needs (and always will need) the human auditor

Automation expands the audit team’s operational capacity, but it does not eliminate the need for the human professional. On the contrary, some stages of the process depend on clinical judgment, industry experience and technical responsibility. These remain the auditor’s non‑transferable duties.

Understanding this limit clearly is as important as knowing what to automate. Delegating to AI what it does well frees the auditor to work in greater depth where they are irreplaceable.

Clinically complex cases

Claims involving complex diagnoses, multiple comorbidities or clinical decisions outside the usual standard require a contextualized reading of the medical record. AI identifies discrepancies and gaps, but interpreting whether a course of treatment was clinically justified depends on the physician auditor’s technical repertoire.

In these cases, the professional needs to assess the care as a whole, the patient’s clinical context and the applicable care guidelines, something that goes beyond verifying data.

Denials that require technical arguments and context

When a payer denies a procedure, the denial appeal must be built on well-founded technical arguments: clinical evidence, contract references and justifications that demonstrate the need for what was charged.

AI can organize the data, identify the available grounds and suggest arguments based on previous appeals. But deciding how to handle the appeal, which evidence to prioritize and how to structure the argument for each payer requires the judgment of someone who knows the contract and the clinical context.

Decisions with ethical or legal impact

Situations involving suspected fraud, serious inconsistencies between the medical record and the claim, or charges that could have legal implications for the institution must be handled with technical responsibility and often with the involvement of other areas, such as compliance and legal.

AI can flag anomalies and generate alerts. Handling the case, with all its implications, remains a human responsibility.

Ongoing supervision and calibration of AI

AI systems learn from the data they receive. If that data contains systematic errors or if contract rules change, the system needs to be corrected and recalibrated. This supervision depends on professionals who understand both the system’s logic and the specifics of hospital billing.

The quality of what AI delivers is directly proportional to the quality of the human supervision it receives. Automating well requires a team capable of evaluating the results of automation and adjusting it when necessary.

Manual auditing vs. AI auditing

The table below sets out the main differences between the two models, with criteria relevant to a manager’s decision:

The two models are not mutually exclusive. In practice, the hospitals that get the best results combine AI’s processing capacity with the team’s technical knowledge, and each works where it has the most to contribute.

How to automate without losing quality — 5 practical tips

The decision to automate hospital auditing involves more than choosing a tool. It involves preparing the operation, the team and the processes so that the technology helps achieve the expected result. The tips below draw on common mistakes seen in implementations and help avoid the main risks of the transition.

1. Start with processes that are already documented

AI learns and operates best when the process rules are clear. Before automating a stage, it is important to map how it works today: which criteria are used, which exceptions exist and where the decision points are. Murky processes, once automated, tend to produce equally murky results.

2. Define indicators before switching the system on

Set baseline metrics before implementation: current denial rate, average time to bill, percentage of backlogged claims, average appeal time. Without these starting figures, it is hard to assess whether automation is producing results or just shifting the problem elsewhere.

3. Keep the auditor in the loop, especially at the start

In the first months of operation, the auditor needs to follow closely what the AI is doing: validating the checks, identifying classification errors and feeding the system the necessary corrections. This phase of active supervision is what ensures the system learns from the hospital’s reality, ensuring that the system learns from the hospital’s specific reality.

4. Treat master data and contract data as a priority

AI works with the data it receives. Outdated tables, poorly configured contracts and inconsistent master data directly compromise the quality of automated checks. Before automating, it is worth reviewing the data that will feed the system.

5. Integrate automation into the existing workflow, without creating silos

One of the most common mistakes is deploying an AI tool that runs in isolation, without connecting to the hospital management system, the electronic medical record and the institution’s other systems. Automation only delivers full results when data flows in an integrated way across platforms.

The impact of automation on the revenue cycle

Auditing is one stage within a larger process: the hospital revenue cycle, which runs from scheduling care to receiving payment from the payer.

Hospitals that implemented automation solutions in the revenue cycle recorded a 24% reduction in billing time and 32% in time to payment, as well as completing denial appeals in under ten days, according to data published by the Futuro da Saúde news portal.

With faster processes and claims submitted on time, the hospital also improves its cash turnover and gains predictability to plan investments. In a reality where the average time to payment exceeds 80 days, any gain in cycle speed has a direct effect on the institution’s financial health.

Automation also changes the nature of the billing team’s work. With less time spent on repetitive tasks, professionals can focus more on analysis, relationships with payers and continuous process improvement, functions that create value and would hardly be prioritized in an overloaded operation.

How Rivio automates the entire revenue cycle

Many institutions are already adopting artificial intelligence tools to optimize the revenue cycle and reduce denials. AI helps read and interpret free-text clinical notes, detect documentation gaps, cross-check clinical data against billing rules, generate denial appeals automatically and automate unified XML submission to payers.

Rivio uses artificial intelligence to manage the entire hospital revenue cycle, increasing revenue and operational efficiency. From audit to payment, the technology analyzes clinical records, cross-checks information against hospital claims, identifies and corrects denials, submits the XML and manages denial appeals, all automatically.

With Rivio, hospitals and clinics leave the bureaucracy to AI and can focus on what really matters: caring for the health of the Brazilian population.

FAQ - frequently asked questions about AI automation

Can AI completely replace the physician auditor?

AI efficiently performs tasks such as checking, cross-checking data and submitting claims, but complex clinical cases, denial appeals and decisions with ethical or legal implications still depend on human judgment. The auditor’s role shifts from performing repetitive tasks to analyzing situations that require clinical interpretation and technical responsibility.

Which stages of hospital auditing are easiest to automate?

The stages with the greatest potential for automation are checking charge entries, identifying missing items, cross-checking medical records against the hospital claim, validating and submitting the XML to payers and monitoring error patterns. These are high-volume processes with well-defined rules and little dependence on clinical judgment.

How does automation help reduce denials?

Automation identifies discrepancies and inconsistencies before the claim is sent to the payer, reducing entry errors, documentation gaps and files that do not follow the TISS standard. By correcting these problems at the source, it lowers the volume of denials caused by operational failures, which make up a significant share of the total denied.

What is the risk of automating auditing without human supervision?

AI systems operate based on the data and rules they receive. Without active supervision, configuration errors, algorithm failures, outdated tables or contract changes can go unnoticed and compromise the quality of the checks. Human supervision, especially in the first months of operation, is what ensures the system learns and adjusts to the hospital’s reality.

What is concurrent auditing and how does AI make it more efficient?

Concurrent auditing is the review of claims during the patient’s hospital stay, before discharge, making it possible to correct discrepancies in real time. With AI, this process gains scale: the system continuously cross-checks medical records and charge entries, flagging inconsistencies for the auditor to step in before the claim is closed and sent to the payer.