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AI in Computer-Assisted Coding: Defining the Future of Healthcare Documentation

By AGS Health

September 12, 2022

The introduction of AI-driven computer-assisted coding (CAC) has significantly reduced the workload for clinicians. Clinical coding has become more effective and efficient thanks to the learning capabilities of AI-based platforms, machine learning (ML), and natural language processing (NLP). These components and their advantages place CAC as a potentially cost-effective solution for the healthcare field.

At first, CAC took a while to produce the desired results. Because of this, healthcare organizations frequently doubted the advantages of switching to a CAC workflow. However, more healthcare institutions are becoming aware of the benefits of CAC software in a value-based care model that prioritizes clinical coding. According to the 2019 KLAS report, 94% of CAC users interviewed by KLAS were likely to purchase a CAC solution again.

How CAC is delivering results

Understanding the impact of AI on CAC

AI’s role in healthcare has evolved to address new and distinct operational challenges -primarily centered on customers’ needs. In its earliest implementations, AI was a straightforward system with a limited set of rules that could carry out tasks. Now, various components of healthcare are being revolutionized by complex models like ML, NLP, and deep learning (DL).

Almost 80% of healthcare data is unstructured, taking the form of text and images. Vast quantities of unstructured data can be understood with the proper application of AI. It is an obvious choice because of its natural ability to gather, analyze, and interpret patterns promptly and effectively. For instance, ML significantly improves CAC output and efficiency when combined with rule-based coding systems. With the healthcare industry generating approximately one trillion gigabytes of data annually, the speed of ML is essential. ML-powered solutions can spot intricate patterns of inconsistencies in human coding and lower the likelihood of inaccurate documentation.

In contrast, NLP can extract codes from unstructured data and map them to later code categories. NLP-enabled CAC software can identify document language patterns and assign codes using linguistic algorithms.

Outlining the Benefits/ Impact

The technology can be transformed into practical applications with the help of AI-enabled CAC platforms. Numerous transitive benefits facilitate the efficiency of healthcare coding. Among them are:

  • Increased Coding Productivity – AI-enabled CAC solutions can increase coding productivity and facilitate better decision-making through an effective workflow and real-time organizational and metrics visibility. CAC can help healthcare institutions improve efficiency by as much as 50%.
  • Coding Accuracy – Clinical codes must adhere to ICD-10-CM/PCS, CPT/HCPCS, and ICD-9 compliant coding standards. Additionally, precise coding techniques produce useful code repositories. AI-based CAC platforms meet modern compliance requirements and use automated abstractions to maintain code accuracy.
  • Better Financial Outcomes – AI-based CAC platforms help healthcare organizations in two key areas:
    • Reducing claim denials
    • Improving DNFC days
    Subsequently, they support a strong revenue cycle. Optimized coding procedures can help organizations achieve a claim denial rate of less than 5%, according to a report by the Medical Group Management Association (MGMA). Additionally, a system that combines advanced analytics with compliant automated coding practices can reduce the average DNFC days.

The Way Forward

With the ICD-10 guidelines, the American Medical Association outlined the requirements for healthcare coding. Directly linking documentation to compliances reinforced the significance of healthcare coding. Modern healthcare institutions spend a lot of time and money ensuring their evaluation and management (E&M) and hierarchical condition category (HCC) coding procedures are in place. Maintaining healthcare records through legal coding procedures is equal to offering first-rate services.

AI-enabled CAC platforms can ensure proper and legal coding procedures. ICD-10 coding guidelines leave little room for error. Additionally, CAC can help healthcare organizations accurately record outpatient preventive care and chronic conditions. This stops any differences in outpatient reimbursements from arising from coding mistakes.

Organizations can improve patient care while preserving a healthy revenue cycle with a robust clinical coding workflow powered by CAC. AI-based CAC is a future-proof option in healthcare technology as the cumulative value and ROI will increase over time.

AGS Health’s NLP-based HIPAA-compliant CAC solution streamlines workflow with minimal interference. A single platform that collects all patient data and boosts productivity, efficiency, and accuracy in coding.

AGS Health

Author

AGS Health is more than a revenue cycle management company–we’re a strategic partner for growth. By blending technologies, services, and expert support, AGS Health partners with leading healthcare organizations across the US to deliver tailored solutions that solve the unique needs and challenges of each provider’s revenue cycle operations. The company leverages the latest advancements in automation, process excellence, security, and problem-solving through the use of technology and analytics–all made possible with college-educated, trained RCM experts. AGS Health employs more than 10,000 team members globally and partners with more than 100 clients across a variety of care settings, specialties, and billing systems.

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