Written by Corliss Collins, BSHIM, RHIT, CRCR, CSM, CCA, CBCS, CPDC
Artificial Intelligence (AI) and Revenue Cycle Management (RCM) are hot topics in the healthcare industry. Optimizing RCM across all payment and reimbursement models can reduce overhead and improve accuracy which results in allowing your RCM workforce to focus on financial counseling and other important area requiring personal interactions with your patients and conducting pre and post documentation and coding audits. This article addresses what Chief Financial Officers (CFOs) and other C-Suite executives needs to know about AI efficiencies and revolutionizing your RCM process.
Revenue Cycle Management Compliance
Revenue cycle management involves complex processes to manage financial transactions related to healthcare services. Artificial Intelligence (AI) Automation and Claims Processing Algorithms for coding, billing, and payments are designed to reduce billing errors and maximize revenue recovery. Payers are already implementing AI as a solution to deny inappropriately coded claims and detect potential fraud and abuse.
In today’s environment, it is important for providers to increase efficiency and reduce the cost of operations, and it is the responsibility of Revenue Cycle Managers to understand and get ahead of root causes which can contribute to potential compliance issues. As we navigate some of the more common underlying causes of billing and reimbursement compliance discussed below, it becomes evident that AI and advanced technology may be a solution to consider, but what if AI algorithms don't perform as expected?
Revenue Cycle Management Coding Edit Errors
Implemented by payers is Medicare’s NCCI Edits (National Correct Coding Initiative) which are coding edits designed to minimize improper payer payments. They include Procedure-to-Procedure (PTP) edits and Medically Unlikely Edits (MUEs). Auto-Coding Edit Errors (automated prepayment edits) significantly impact RCM billing, claims processing, and revenue operations. Because payers are advancing their technology in these areas, it is important for providers to increase billing accuracy and implement cost-effective technology to stay compliant.
Coding related edit errors stem from improperly programmed edits and rules-based algorithms designed or written by human intelligence. These issues can exist within your practice management system or occur during an update. These unintentional flaws can cause claims to be held up in the Medical Claims Scrubber, and not submitted to payers timely. This causes delayed payments, cash flow problems, manual work to detect and correct as well as compliance issues.
Here are a few examples of why coding edit errors may occur and why detecting system failures is critical to the RCM process:
- Incorrect Coding: One of the primary causes of errors is incorrect coding. This could involve using the wrong CPT (Current Procedural Terminology) or HCPCS (Healthcare Common Procedure Coding System) codes.
- Bundling Issues: PTP edits often occur when procedures or services should not be billed separately because they are considered bundled together.
- For example, if a comprehensive service includes specific components that should not be billed separately, attempting to do so will trigger a PTP edit.
- Mutually Exclusive Procedures: PTP edits also arise when two procedures are mutually exclusive, meaning they should not ever be performed during the same encounter.
- Billing for both procedures would trigger an edit and if billed, could trigger an audit.
- Frequency Limits: MUEs limit the number of times a specific service can be billed within a given timeframe. Payer edits look for inappropriate number of units per line-item on a claim as well.
- Errors occur when providers attempt to bill for services that exceed these limits.
- Documentation Deficiencies: Errors can also stem from documentation deficiencies.
- If the medical record does not support the services billed or lacks sufficient detail, it can trigger automated edits.
- Upcoding or Unbundling: Sometimes errors occur due to intentional or unintentional upcoding.
- An example is billing for a more complex or expensive service than what was provided; or unbundling (billing separately for components that should be billed together).
- System Glitches or Errors: Occasionally, edit errors can occur due to glitches or errors in the billing system.
- Software updates, database errors, or incorrect application of rules could cause this.
- Changes in Coding Guidelines or Regulations: Updates to coding guidelines or regulations can sometimes lead to errors.
- When providers are not aware of the changes and the systems and software applications aren't updated appropriately for guidelines/regulations to be applied correctly.
- Inaccurate Charge Capture: Failing to capture all automated billable services rendered to a patient. This includes failing to charge for documented services which are medically necessary and allowed under the patient’s insurance plan.
- Inaccurate charge capture results in lost revenue and potential compliance issues.
- Manual Review determines an automation edit error occurred (Claim should not have ever been placed on a billhold).
While it would be very challenging to provide an exact dollar amount for the cost of auto-coding edit errors across all healthcare organizations, studies and industry reports suggest that they contribute to significant financial losses. Inaccurate auto-coding can lead to poor data quality, which can undermine the reliability of healthcare data used for billing, claim reimbursements, quality improvement, and policy development.
Conclusion
Each year the complexity of medical coding increases due to provider’s need to capture detailed accuracy in how they charge for treatment to maximize revenue. AI can sort through codes, annual updates and guidelines with lightning speed. The root causes of these automated coding edit errors are broad in scope and require a multifaceted approach to identifying the issues involved, reconciling and improving AI programming, and Algorithmic System updates.
To mitigate the auto-coding edit errors' financial impact, healthcare organizations should implement effective coding validation processes, regularly audit coding edit rules, and leverage technology solutions that help to improve coding accuracy and efficiency.
Healthcare Organizations should address coding edit errors proactively, evaluate how the current state and rework cost impact revenue, and enhance overall revenue cycle management performance:
- Implement robust testing policies and procedures;
- Measure, and monitor progress (what is measured is what improves);
- Provide ongoing oversight to critical education system users;
- Address the factors that can help reduce auto-coding edit error occurrences;
- Created sustainable accuracy and efficiency best practices for the entire revenue cycle management process; and
- Realize that AI and algorithms can cause claims processing errors in key ways, including overreliance on incomplete or inaccurate data, lack of human oversight, and the inability to keep up with evolving regulations and guidelines.
Addressing these issues through automating error-proofing measures, data quality control, and maintaining the right balance between automation and human expertise is crucial to mitigate the risk of AI-driven claims processing errors.
Auditing & Implementing a Proactive Approach
As providers implement advanced technology, critical oversight is needed to ensure the new systems are performing within compliance guidelines. Your coding professionals can be assigned to perform oversight by conducting Root Cause Analysis (RCA) after a problem is identified and implementing prospective assessments, as seen with Healthcare Failure Mode and Effect Analysis (HFMEA).
HFMEA is a prospective assessment that identifies and improves steps in a health care process thereby reasonably ensuring a safe and clinically desirable outcome. It is also considered a systematic approach to identify and prevent product and process problems before they occur.
Ahmed. (2023,). What do scrubbers do in revenue cycle management? Continuum.
Armstrong. (2024). How Cigna saves millions by having its doctors reject claims without reading them. ProPublica.
Knight. (2021). The foundations of AI are riddled with errors. WIRED.
Medicare NCCI FAQ Library | CMS. (n.d.).
About the Author
Corliss Collins, BSHIM, RHIT, CRCR, CSM, CCA, CBCS, CPDC
Corliss is the founder and Chief Revenue Integrity Officer, Chief Compliance Officer of P3 Quality LLC. She is serves as a subject matter expert and volunteer on the Education Committee for the American Institute of Healthcare Compliance.
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