Auditing, Managing Denials Is Important to Good A/R Hygiene
Auditing

Conducting Internal Risk Adjustment Coding Audits

Written by Joanne Byron, BS, LPN, CCA, CHA, CHCO, CHBS, CHCM, CIFHA, CMDP, OHCC, ICDCT-CM/PCS

Does your compliance program include auditing and monitoring documentation and coding related to risk adjustments and your value-based care reimbursement?  Regulatory agencies and payers use hierarchical condition categories (HCCs) to calculate patient risk; the higher the risk score for a population, the higher the benchmark will be for expenditures, which can affect shared savings in value-based payment arrangements. This short article provides a basic overview of this complex topic.

Medicare & the OIG are performing Risk Adjustment audits, are you?

Detecting and internal correction of coding errors should be a major part of your overall compliance program.  As always, correct coding must be supported by clinical documentation in the patient’s medical record. 

Value-based care (VBC) is a healthcare delivery model where healthcare providers are paid based on patient health outcomes instead of based on the volume of patients seen and services delivered. Value-based care ties the amount health care providers earn for their services to the results they deliver for their patients, such as the quality, equity, and cost of care.

Risk adjustment and Hierarchical Condition Category (HCC) coding is a risk adjustment model that groups chronic medical conditions into categories based on cost patterns and clinical complexity. This was originally mandated by the Centers for Medicare & Medicaid Services (CMS) back in 1997 and implemented in 2004.

HCC coding relies on ICD-10-CM coding to assign risk scores to patients.  The Office of Inspector General (OIG) and CMS both conduct targeted audits and reviews.

CMS uses HCCs to calculate payments to healthcare organizations for patients insured by Medicare Advantage, Accountable Care Organizations, and some Affordable Care Act plans.  HCCs are based on ICD-10-CM coding, which assigns risk scores to patients. Insurance companies use these scores, along with demographic factors, to calculate a patient's risk adjustment factor (RAF) score.

Patients with high HCCs are expected to require more intensive medical treatment verses those enrollees who have low HCCs. Based on the HCC system, providers need to document all health conditions to the appropriate specificity level for CMS to ascertain the patients’ true health status which impacts the risk adjustment payment.

How can your organization improve accuracy of HCC documentation?

This is a complex topic. Implementing software can assist your organization to detect gaps and areas requirement improvement before an external audit is performed.  This is a perfect area to consider using generative Artificial Intelligence (AI).

  • Performing routine documentation and diagnosis coding audits to ensure accuracy is not only important for risk adjustment audits, but overall accuracy of your claims.
  • Avoid auto-population of diagnosis codes.  An internal audit can reveal system problems contributing to reporting conditions that have resolved or were not treated for a particular encounter.
  • Analyze patient data to identify gaps in coding and provide information on under-documented conditions which can be relevant to HCC data.  Review the record to ensure active conditions are not reported as “history of”, for example.
  • HCC conditions may be rejected without the proper supportive documentation. Use the acronym M.E.A.T. to make sure you are documenting appropriately.
    • Monitor is documentation of the patient’s signs, symptoms, disease progression or regression as recorded in the ongoing surveillance of the chronic condition
    • Evaluation must be recorded in the record as physical exam findings, test results and response to treatment and effectiveness of medication
    • Assessment is documenting the discussion of chronic conditions, reviewing records, counseling ordering or further tests and how the chronic condition will be evaluated     
    • Treatment includes documenting a plan of care, changes to the plan of care as it relates to medications, therapies, referrals and other modalities

About the Medicare Advantage Risk Adjustment Data Validation (RADV) Program

CMS' primary way to address improper overpayments to Medicare Advantage Organizations (MAOs) is through the RADV. During a RADV audit, CMS confirms that any diagnoses submitted by an MAO for risk adjustment are supported in the enrollee's medical record.  Click Here for the CMS.gov Risk Adjustment information page and risk adjustment model software.

About the OIG Targeted Review of Documentation Supporting Specific Diagnosis Codes

The OIG Work Plan states that prior OIG reviews have shown that some diagnoses are more at risk than others to be unsupported by medical record documentation. We will perform a targeted review of these diagnoses and will review the medical record documentation to ensure that it supports the diagnoses that MA organizations submitted to CMS for use in CMS's risk score calculations and determine whether the diagnoses submitted complied with Federal requirements. 

MA organizations are required to submit risk-adjustment data to CMS in accordance with CMS instructions (42 CFR § 422.310(b)), and inaccurate diagnoses may cause CMS to pay MA organizations improper amounts (SSA §§ 1853(a)(1)(C) and (a)(3)). In general, MA organizations receive higher payments for sicker patients.

  • CMS estimates that 9.5 percent of payments to MA organizations are improper, mainly due to unsupported diagnoses submitted by MA organizations.
  • Prior OIG reviews have shown that some diagnoses are more at risk than others to be unsupported by medical record documentation.
  • The OIG will perform a targeted review of these diagnoses and will review the medical record documentation to ensure that it supports the diagnoses that MA organizations submitted to CMS for use in CMS's risk score calculations and determine whether the diagnoses submitted complied with Federal requirements.

Recent OIG reports of risk adjustment audits published in September 2024 indicate noncompliance with Federal requirements.

EmblemHealth – The OIG released a report in September 2024 regarding audit results of EmblemHealth and sampled enrollee Hierarchy of Chronic Conditions (HCC).  The OIG found that some diagnosis codes submitted to CMS S for use in the risk adjustment program in accordance with Federal requirements. First, although most of the diagnosis codes that EmblemHealth submitted were supported in the medical records and therefore validated 860 of the 1,222 sampled enrollees’ HCCs, the remaining 362 HCCs were not validated and resulted in overpayments. These 362 unvalidated HCCs included 54 HCCs for which we identified 54 other HCCs for more and less severe manifestations of the diseases. Second, there were an additional 65 HCCs for which the medical records supported diagnosis codes that EmblemHealth should have submitted to CMS but did not. Learn more about this audit.

Humana Health Plan - In the September 2024 CMS report, the diagnosis codes that Humana submitted to CMS were not supported by the medical records.  The OIG made the following recommendations to Humana:

  1. Refund to the Federal Government the $6.8 million of estimated overpayments;
  2. Identify, for the high-risk diagnoses included in this report, similar instances of noncompliance that occurred before or after our audit period and refund any resulting overpayments to the Federal Government; and
  3. Continue to examine its existing compliance procedures to identify areas where improvements can be made to ensure that diagnosis codes that are at high risk for being miscoded comply with Federal requirements and take the necessary steps to enhance those procedures. 

Humana disagreed with some of the OIG findings which resulted in the OIG reducing the number of enrollee-years identified as errors and revised the amount in the first recommendation listed above. Read the OIG’s report to review for lessons learned.

HealthAssurance - The OIG report published September 2024, indicates that most of the selected diagnosis codes submitted to CMS failed to comply with Federal risk adjustment program requirements. HealthAssurance, through CVS Health, disagreed with the OIG’s audit methodology and overpayment estimation methodology.  After reviewing HealthAssurance’s comments and the explanations it provided, the OIG reduced the number of enrollee-years in error and revised the amount in the first recommendation, but maintain that the second and third recommendations remain valid:

  1. Refund to the Federal Government the $4.2 million in overpayments;
  2. Identify, for the high-risk diagnoses included in the report, similar instances of noncompliance that occurred before or after our audit period and refund any resulting overpayments to the Federal Government; and
  3. Continue its examination of its existing compliance procedures to identify areas where improvements can be made to ensure that diagnosis codes that are at high risk for being miscoded comply with Federal requirements (when submitted to CMS for us in CMS’s risk adjustment program) and take the necessary steps to enhance those procedures.

Read the OIG’s report for more information.

Conclusion

Risk adjustment ensures that providers are paid fairly for the care they provide, based on the health needs of their patients. Incomplete documentation and inaccurate coding cannot only cause underpayments, but more importantly over payments. 

Internal auditing and monitoring to ensure documentation is appropriate is essential to ensure processes are carried out in accordance with applicable laws, regulations, and ethical standards.

Remember, non-compliance with federal regulations can result in financial penalties, negative press, and even a plan being barred from participating in government healthcare programs.  If your organization has a large patient population on that plan, providers suffer the financial consequences as well.

About the Author

Joanne Byron, BS, LPN, CCA, CHA, CHCO, CHBS, CHCM, CIFHA, CMDP, OHCC, ICDCT-CM/PCS serves as the Board Chair of the American Institute of Healthcare Compliance (AIHC) and oversees the Volunteer Education Committee.

Copyright © 2024 American Institute of Healthcare Compliance All Rights Reserved 

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Artificial Intelligence in Healthcare
Artificial Intelligence

AI and Algorithms: An Effective Approach to Medical Claims Processing

Authored by Corliss Collins, BSHIM, RHIT, CRCR, CSM, CCA, CBCS, CPDC   

Founder. Principal and Managing Consultant     



This is Part 7 in a series of articles providing a general overview of Artificial Intelligence (AI) impacting the healthcare industry.  This information is an overview of very basic suggestions on Standard Operating Procedures (SOP), Policies and Best Practices for smaller hospitals and provider organizations. This information is not designed to be all-inclusive. and is not intended as consulting or legal advice.

Introduction

The Health Leaders Media Report, Final Denial Rate Increase, states that denials increased by 51% in 2021 and 2023 despite significant investments in Artificial Intelligence (AI) Automation and Revenue Cycle Management (RCM) Operations utilizing AI-Powered Medical Coding and Medical Claims Processing Software. This pressing issue demands our attention!

About the technology - Optical Character Recognition (OCR) and Natural Language Processing (NLP) are powered by AI Machine Learning (ML) and deep learning large language models (LLM). These technologies have become a core function in RCM Operations and have also been adopted by the Health Insurance, Health System, and Provider communities across the United States. Nonetheless, claim denials continue to show an alarming increase year over year. 

So, why aren't the numbers of healthcare claim denials and rejections declining with all the automation and technology advancements?

Are Automated Coding Models Powered by AI Not Accurate Enough for Medical Coding?

This article delves into this complex and sometimes very confusing topic, briefly recapping Part 6, AI and Algorithms: The Other Side of Medical Claims Processing, and then providing a general synopsis in Part 7AI and Algorithms: An Effective Approach to Medical Claims Processing. 

Advancing RCM Operations

Every year, more than five billion medical claims are processed by Payers in the United States for reimbursement, according to the Centers for Medicare & Medicaid Service, CMS.gov.  Many of these claims are processed using the Healthcare Common Procedure Coding System (HCPCS) and the Current Procedural Terminology (CPT®) coding systems. These two coding systems are the backbone of our billing process. It is essential to comprehend the underlying causes of AI, algorithms, and Rule-Based Automation in order to reduce risks:

There are Three Primary Claim Edit Types to Pay Attention to:

  1. Technical Edits are triggered by missing or incomplete claim information
  2. Clinical Edits are directly associated with care or services rendered
  3. Underpayments Claim amounts aren't being paid as the Contract Agrees to pay

Artificial Intelligence (AI) in the Medical Claims Process

AI leverages cutting-edge technology, including robotic process automation and AI machine learning, to improve the different medical claims adjudication activities that enable accurate billing for provided healthcare services.

These AI systems also use LLMs, which are machine learning models with the capacity to communicate in normal language with claim managers to evaluate substantial amounts of data. Complex patient data management, medical record administration, processing medical claims, collecting payments, and financial reporting are among the duties covered by CMS AI Resources.

There are hundreds of HIPAA-Compliant Medical Coding Software Applications on the market (see CMS for more on HIPAA-Compliant Code Sets.) Using automation more and manual intervention less is supposed to lead to the reduction of errors and inefficiencies. If this is the case, why do claim rejection and denial rates seem to not be declining annually?

On another note, unautomated accounts are processed differently. Claims are routed to manual review work queues (WQs) to be analyzed, corrected and billed. 

AI-Powered Challenges

Historically, rule-based algorithms for coding and claim adjustments (also known as coding-related edits) were created to streamline the claims adjudication and payment procedure for providers sending medical bills to payers. However, according to a 2022 Experian Report, rejections are still increasing year over year.  Statistics on Healthcare Claim Denials Healthcare Claim Denial Statistics are provided below:

Claim Denials Statistic Examples:

  • In 2009, there was an estimated $210 billion rise in claim denials
  • In 2019, a decade later, that number increased to $265 billion

These technologies seek to transform coding quality, accuracy, and financial performance, even as artificial intelligence in medical claims processing is a game-changer. In order to determine why claim denial rates are still rising, it is imperative that automation accountability, compliance, and transparency (ACT) standards be established; criteria suggestions are provided below:


Key AI-Powered Automation Evaluations could start with the Challenge Areas below:

  1. Patient Registration and Scheduling:
    • Chatbots and Virtual Assistants: AI-driven chatbots assist patients with scheduling appointments
    • Automated Data Entry: AI tools can automatically populate patient information from various sources, ensuring data consistency.
  2. Claims Processing and Billing:
    • Claims Scrubbing: AI systems can review claims for errors or omissions before submission.
    • Predictive Analytics: AI can predict the likelihood of claim approval based on historical data.
  3. Denial Management:
    • Root Cause Analysis: AI can analyze denial patterns to identify common causes and suggest process improvements.
    • Automated Appeals: AI-driven tools can generate and submit appeals for denied claims.
  4. Payment Posting and Reconciliation:
    • Automated Payment Posting: AI can automate the posting of payments received from payers.
    • Discrepancy Detection: AI systems can identify and flag discrepancies between expected and received payments.  
  5. Patient Engagement and Collections:
    • Predictive Payment Models: AI can analyze patient payment behaviors to predict the likelihood of payments.
    • Automated Reminders: AI can send personalized payment reminders to patients.
  6. Financial Reporting and Analysis:
    • Revenue Forecasting: AI models can predict future revenue based on current and historical data. 
    • Insights and Analytics: AI provides real-time analytics and dashboards, offering insights into key performance indicators (KPIs).
  7. Compliance and Risk Management: 
    • Fraud Detection: AI systems can identify unusual patterns that may indicate fraudulent activities.
    • Regulatory Compliance: AI helps ensure billing and coding practices comply with current healthcare regulations.

AI-Powered Algorithmic Opportunities Can be Leveraged

Best Practices for resolving discrepancies, recoding, and resubmitting rejected claims, and appealing denials are outlined in the Six Key Mitigation and Remediation guidelines below: 


Rejected or Denied Claims Review Strategy

  1. Claim Rejections
    1.1 Opportunity:      One or more edits caused the entire claim to be rejected
    1.2 Solution:             Identify the edit error, correct it, and resubmit the claim for payment
  2. Line-Item Rejections
    2.1 Opportunity:     One or more edits caused individual line items to be rejected
    2.2 Solution:            Identify claim edit line-item error, correct it, and resubmit it for payment
  3. Claim Denials    
  4.      3.1 Opportunity:    One or more edits have caused the entire claim to be denied

         3.2 Solution:          The provider must submit a letter with documented medical necessity                                                                                                                    

  5. Line-Item Denials
    4.1 Opportunity:     One or more edits caused individual line items on a claim to be denied
    4.2 Solution:             The claim line item that was denied must be appealed.
  6. Return-to-Provider
    5.1 Opportunity:     One or more edits caused the entire claim to be returned to the provider
    5.2 Solution:             The Provider should correct the claim edit error and resubmit it for payment
  7. Suspension 
    6.1 Opportunity:     One or more edits caused the entire claim to be suspended
    6.2 Solution:             The MAC must review the claim and determine if it will be paid


The Time to ACT is Now

It is estimated that the Medical Coding AI business will invest around $8.5 billion between 2024 and 2033, a period of nine years. The first step in reimagining a successful medical claims processing strategy is to set up and implement Accountability, Compliance, and Transparency (ACT) Best Practices.

The Department of Health and Human Services (HHS) has produced and distributed a trustworthy AI  (TAI) playbook, which assists Healthcare Leaders and Staff in developing TAI-specific Standard Operating Procedures (SOP), Policies, AI Playbooks, and Process Optimization Solutions.


HHS TAI Playbook Objectives

  1. Promote understanding of TAI Principles in the Playbook
  2. Provide guidance and frameworks for applying TAI Principles
  3. Centralize relevant federal and non-federal resources on TAI
  4. Serve as a framework for future HHS Policies on TAI acquisition, development, and use

The HHS TAI Playbook provides a great Blueprint for improving AI, Algorithms, Medical Claims Processing, and Revenue Cycle Management (RCM) Operational outcomes. 

REFERENCES:

About the Author

Corliss Collins, BSHIM, RHIT, CRCR, CSM, CCA, CBCS, CPDC

Corliss is the Founder. Principal and Managing Consultant of P3 Quality LLC. She serves as a subject matter expert and volunteer on the Education Committee for the American Institute of Healthcare Compliance.



Copyright © 2024 American Institute of Healthcare Compliance All Rights Reserved

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Artificial Intelligence in Healthcare
Artificial Intelligence

AI and Algorithms: The Other Side of Medical Claims Processing

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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).
  7. 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.
  8. 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.
  9. 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 bill hold).

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.

References

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.



Copyright © 2024 American Institute of Healthcare Compliance All Rights Reserved 

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