AI chart review: Definition and benefits

What is AI chart review?

AI chart review sets a new standard for back-office operational efficiency in healthcare. To optimize outcomes across revenue cycle, payer compliance, and clinical quality, healthcare businesses historically relied on manual chart reviews to identify the problem trends that create revenue leak, care gaps, and compliance risk. AI can now accomplish the same reviews across functions, but without the tedium, delays, and overhead associated with manual chart review. 

How AI chart review creates value

AI chart review uses advanced large language models (LLMs) to read and analyze patient encounter documentation. After a provider closes an encounter note, an AI model assesses the documentation for coding accuracy and completion, payer compliance, provider performance and clinical quality measures.

LLMs trained on healthcare documentation can review notes across every encounter in full, including clinical narrative documentation, structured EHR data, diagnoses, orders, and procedures. The best systems can do so across document file types, including EHR fields, faxes, and handwritten notes.  

Automated chart review across 100% of all patient encounters unlocks transformative benefits for medical practices. Ongoing AI chart review generates comprehensive data related to trends that impact revenue performance, operations, and the clinical quality of care delivery. It also enables leaders at the practice to build systems that guarantee pre-billing revenue integrity and payer compliance, as well as build sustainable systems for improving clinical quality measures and by extension, patient outcomes. 

Chart review across your practice

Chart review means different things for different teams at your clinic. Historically, this could result in duplicative labor and disconnected, partial analyses. Such disconnected reviews generate ongoing challenges at the practice level, such as persistent revenue leak and reworking claims that were denied for preventable reasons. 

Implementing AI-powered chart review across 100% of patient encounters not only eliminates these stubborn challenges; it also creates new benefits and insights across teams. Here’s how: 

AI chart review for revenue cycle management (RCM) 

RCM teams primarily review charts for coding accuracy and completeness, as well as payer coding compliance. Incomplete or conservative coding leaves earned revenue behind, while unsupported coding increases audit exposure.

Typical chart reviews confirm that every billed service is fully supported by the documentation in the encounter. That includes:

• Every documented service has been coded
• Specific diagnoses that support CPT billing codes
• Accurate E/M level selection
• Correct use of add-on codes
• Codes and diagnoses align with payer-specific rules for billing

These types of coding-related errors account for approximately 13% of all revenue leaks. But for nearly every outpatient practice, reviewing every chart for these accuracies is impractical and cost prohibitive. Instead, most rely on retrospective sample reviews to identify patterns for remediation, sometimes in conjunction with reviews of charts that meet certain criteria, such as those with higher-risk codes or those submitted by newly onboarded providers. 

AI enables full review of every chart before it makes it to the billing desk, eliminating revenue leaks and preventable denials associated with coding errors. 

AI chart review for compliance 

Compliance teams are concerned with complete documentation and payer compliance. In addition to coding-level compliance with payer billing rules, compliance teams are also tasked with reducing audit risk. To be defensible under a payer audit, provider documentation must meet payer standards for medical necessity, required documentation elements, and in certain cases, additional clinical quality criteria.  

As with RCM teams, most compliance teams have mostly relied on retrospective chart audits to identify patterns of audit risk, and to work with clinical leadership on provider education meant to reduce that risk. While better than nothing, these retrospective reviews do nothing to prevent audit exposure created by charts that were not reviewed prior to billing. 

AI chart review enables full visibility into compliance risk as it occurs, enabling compliance leaders to effectively balance between business demands and payer expectations. 

AI chart review for clinical quality 

Clinical quality leaders review charts to assess whether documentation reflects the care that was delivered, as well as whether the care delivered met payer, professional, and organizational standards. During a clinical quality chart review, a quality leader examines whether care gaps were identified, whether follow-up plans are documented, and if the record corresponds to the standard of care expected for that patient.

Clinical quality reviews identify compliance risk and variations in care delivery by surfacing documentation and care gaps, so medical leaders can intervene before those patterns become systemic problems that could impact patients or revenue. 

What traditional chart review misses

At most practices, chart review is divided between coding and clinical quality teams or handled by third-party services. Reviews are retrospective and only address a small sample of the practice’s total charts because manual reviews are slow and costly. Rule-based tools for identifying potential problem charts are useful for concentrating review capacity on the riskiest charts, but their logic is too rigid to return actionable insights at scale. Until the advent of AI, sampling was the best way to draw conclusions. 

How AI chart review works

AI chart review solves the problems of retrospective manual reviews by integrating with your current workflow, including your existing EHR. When a provider closes a chart, the system reviews the entire encounter, evaluating documentation across:

• Coding completeness
• Coding accuracy
• Medical necessity criteria, including a relevant ICD-10 diagnosis
• Documented justification for E/M levels based on MDM or time
• Examination findings and other physical exam notes
• Support for add-on codes
• Coding and documentation compliance with payer-specific rules 
• Clinical quality measures 

In addition to these standard assessments, an AI model can be trained to look for and evaluate anything in a chart that a human might be trained to assess. The model then reports structured findings tied directly to the documentation and provides recommendations for improvements, where relevant. Each recommendation references supporting or missing elements within the note, so coding and clinical team members can accept, modify, or dismiss recommendations before the chart moves forward to billing. 

How AI evolves traditional chart review

AI chart review creates fundamental shifts in how practices have historically reviewed charts.

Reviews are systematic and scalable 

Instead of sampling a small percentage of encounters, AI can review 100% of charts without the expense and delay of expanding manual headcount. Because it can read across document types and apply consistent logic to each note, it standardizes coding and documentation decisions more reliably than variable human review, while allowing humans to retain control over exceptions.

Reviews happen at the right time

Rather than functioning as a retrospective audit, validation is embedded into existing workflows and happens before submission, when correction is still operationally simple and has the greatest financial impact.. AI can execute distinct types of review within the same encounter—coding accuracy, documentation compliance, and clinical quality—based on what each type of encounter requires.

Insights are actionable 

This shift moves chart review from selective quality assurance to continuous operational oversight. Leaders gain consistent, encounter-level visibility across coding accuracy, documentation compliance, and clinical quality without expanding manual capacity.

By evaluating every encounter consistently and executing the appropriate type of review for each note, AI enables comprehensive oversight and gives leaders reliable data to improve revenue, compliance, and quality performance across the practice.

How AI changes revenue cycle management

In traditional workflows, documentation problems often surface downstream. Claims are submitted, preventable denials occur, and rework begins. 

AI chart review shifts validation upstream, so documentation insufficiencies and coding omissions and mistakes are identified before submission. Providers can address missed diagnoses or unsupported services while the encounters are still fresh in their minds.

This changes the economics of RCM. Preventing denials are operationally cheaper than resolving them. Capturing supported complexity at first pass is more reliable and certainly more efficient than attempting recovery later.

Revenue performance becomes more directly tied to documentation behavior in real time, so practices no longer bear the costs of rework or the extended revenue cycles that can limit profitability for specific periods.

Learn about Charta AI chart review

Charta integrates directly with your EHR to conduct AI chart review across 100% of your charts in real time, so you can improve revenue capture, payer compliance, and provider performance. To learn more, request a demo from an AI chart review expert.