The advent of AI-powered computer-assisted coding (CAC) has been instrumental in easing the clinician’s workload. The learning capabilities of AI-based platforms, combined with machine learning (ML) and natural language processing (NLP), have enabled increasingly effective and efficient clinical coding. These features and their benefits position CAC as a potentially cost-efficient initiative for the healthcare industry.
At the outset, CAC solutions were relatively slow to deliver the expected outcomes. As a result, healthcare organizations were often skeptical of the benefits of moving to a CAC workflow. But with the shift to a value-based care model that emphasizes the primacy of clinical coding, more healthcare institutions are realizing the advantages of a CAC software. According to the 2019 KLAS report, 94% of CAC adopters interviewed by KLAS would like to purchase a CAC solution again.
How CAC is delivering results
Understanding the impact of AI on CAC
Over time, AI’s presence in the healthcare industry has evolved to meet new and unique challenges of largely customer-centric operations. In its earliest form, AI was deployed in the form of a simple rule-based expert system that could execute tasks based on a finite number of rules. Today, complex models like ML, NLP, and deep learning (DL) are revolutionizing multiple aspects of the healthcare industry.
Almost 80% of healthcare data is unstructured in the form of text and images. With the proper implementation of AI, it is possible to make sense of large volumes of unstructured data. Its inherent capability to gather, analyze and interpret patterns in a quick and efficient manner makes it a natural choice. For instance, ML, combined with rule-based coding systems, plays a crucial role in enhancing CAC output and efficiency. With the healthcare industry generating approximately one trillion gigabytes of data annually, the speed of ML is a necessity. ML-powered solutions have the ability to identify complex patterns of inconsistencies in human coding and reduce risks of erroneous documentation.
NLP, on the other hand, can identify codes from unstructured data and map them to subsequent code categories. By leveraging linguistic algorithms, NLP-powered CAC software can identify language patterns within documents to assign codes.
Outlining the Benefits/ Impact
The AI-enabled CAC platforms are able to translate the technology into real-world applications. There are numerous transitive benefits that enhance the healthcare coding workflow. Some of them are:
- Increased Coding Productivity – Efficient coding workflow coupled with real-time visibility of coding and organizational metrics brought about by AI-enabled CAC solutions can improve coding productivity and enable better decision making. In fact, CAC can help healthcare institutions improve efficiency by as much as 50%.
- Coding Accuracy – It is imperative that clinical codes follow the ICD-10-CM/PCS, CPT/HCPCS, and ICD-9 compliant coding practices. Moreover, accurate coding practices also create valuable code repositories. AI-based CAC platforms can align with modern compliance requirements and leverage automated abstractions to maintain code accuracy.
- Better Financial Outcomes – Reducing claims denial and improving DNFC days are two major areas where AI-based CAC platforms can assist healthcare organizations. As a result, they facilitate a robust revenue cycle. A report by the Medical Group Management Association (MGMA) revealed that optimized coding practices can assure claims denial rate of less than 5%. Moreover, a system that marries compliant automated coding practices with advanced analytics can reduce the average DNFC days.
The Way Forward
The American Medical Association has clearly outlined the parameters of healthcare coding with the ICD-10 guidelines. This further enshrined the importance of healthcare coding by directly associating documentation with compliances. Needless to say, healthcare organizations of today are investing substantial time and resources to ensure that their evaluation and management (E&M) and hierarchical condition category (HCC) coding practices are in place. The importance of maintaining healthcare records through compliant coding practices is at par with providing exemplary services.
This has a clear implication on the revenue cycle which is one of the key desired outcomes of a value-based care ecosystem. AI-enabled CAC platforms can ensure correct and compliant coding practices. Especially with the introduction of ICD-10 coding guidelines that leaves very little room for error. CAC can also enable healthcare organizations to capture chronic conditions and preventive treatment for outpatients accurately. This prevents any discrepancies with outpatient reimbursements occurring due to coding errors.
A robust CAC-powered clinical coding workflow can help organizations provide better care to their patients while maintaining a healthy revenue cycle. With time, the cumulative value and ROI on AI-based CAC are only set to increase making it a future-proof choice in the domain of healthcare-technology.
ezDI’s NLP-based HIPAA Compliant ezCAC simplifies workflow with minimal disruption. A unified platform that gathers all patient data and improves coding accuracy, efficiency, and productivity. To learn more, visit www.ezdi.com.
To learn more about ezDI’s AI-based mid-revenue cycle management solutions visit www.ezDI.com and to see the live product demo of our Clinical Documentation, Coding, Compliance/Auditing, Quality Measures, Encoder, and Enterprise Analytics request a live demo.