How Artificial Intelligence Can Improve Coding Audits

Introduction

Medical coding audits are time-consuming, complex, and very costly processes that always need a huge investment of manual resources. However, more recently, there have been many advances in the Artificial Intelligence (AI) sector, which make these audits more accurate, efficient, and cost-effective. Because of the sheer volume and the complexity of the clinical documentation that providers need to manage each day to support coding, the MGMA (Medical Group Management Association) suggests that medical practices complete coding audit regularly.

According to MGMA, coding audits are essential to confirm that a certain practice’s processes are working appropriately and are crucial for effective risk mitigation and revenue cycle optimization. Coding audit guarantee accuracy and allow for practice to understand the level of complexity in the coding and billing within the practice.

Benefits of Coding Audits

Following are some of the benefits of coding audit:

  • Improves your ROI.
  • Enhances medical record accuracy.
  • Helps with training and education.
  • Plays an essential role in sustaining a culture of compliance.
  • Keeps you updated on coding regulations and rules.
  • Helps organizations maintain the OIG compliance regulations.

Challenges in Coding Audits

Coding audits are high-intensity and time-consuming efforts that are essential to support vendor and in-house coders, with minimal visibility of benchmarking or performance. The process for managing coding audits involves a number of spreadsheets along with procedures and payer policies.

Multi-national healthcare organizations can have various vendors and internal teams that are dedicated to coding. This means that such organizations can also have 5 or 6 different, manual methods for auditing. All the audit results then have to be combined. Such a process is time-consuming and error-prone. In turn, the process can turn unmanageable, and data integrity can be at risk.

No one really likes there are logistical challenges associated with performing coding audits that make the process ripe for improvement through technologies like artificial intelligence and automation.

A Smart Approach to Coding Audit Management

To decrease the overhead associated with coding audits, various healthcare organizations have turned to AI algorithms to establish a more intelligent approach to auditing. By allowing auditors to work in a central workflow system, such an AI-driven approach offers better coding accuracy, detects lost coding opportunities, and creates educational opportunities for coders in real time. Following are some of the ways that AI improves the management of coding audits.

Provides Central Visibility Across in-house and Vendor Coding Performance

With a central repository for all kinds of coding audit performance data, organizations can gauge the overall management of their coding teams. This helps users to check the systemic failures to manage workload volume and allocate charts, and detect trends in error rates to analyze if they are provisional or part of a larger trend.

Determines Whether to Contract or Expand Business with a Coder or Vendor:

Modern coding audit management platforms can detect strong and weak-performing team members by comparison of revenue-impact metrics across error rates.

Targets the Right Accounts to Audit

AI-driven systems help users target accounts to arrange for coding audits. This depends on factors such as the performance history of the coder and the effect on revenue. Other factors such as the historical disposition of like accounts, and attributes also may generate a high risk for coding errors.

 

 

 

 



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