Autonomous Coding: Towards a Promising Future of Medical Coding
Introduction
Medical coding is, perhaps, the most crucial and labor-intensive part of revenue cycle management, requiring skilled and certified resources. The United States is the world's largest market for medical coding and is expected to expand from 2022 to 2030 at a compound annual growth rate (CAGR) of 8 percent.
However, the coding process has become increasingly complex over the years due to the dynamic regulatory environment and clinical documentation requirements. The efficiency of the medical coders has undoubtedly decreased as a result of frequent modifications to the medical coding standards. Needless to say, this has led to a rapid increase in revenue leakage, a shortage of professional coders, and constant pressure to keep abreast with changing guidelines while enhancing productivity and quality.
Primary Obstacles in the Realm of Medical Coding
The key concerns in the healthcare sector pertaining to coding include claim rejections, inaccuracies, errors, and delayed time to payment. The coding workforce must be fully up to speed, given that inaccurate coding accounts for 68 percent of hospital denials, with these denied claims costing hospitals over $262 billion annually.
Training a coding workforce has become more challenging as a consequence of the transient and volatile coding standards. It is nearly impossible to remain up-to-date on the myriad of payer-specific coding guidelines while concurrently anticipating future revisions.
Several health systems have begun relying on technology to automate some of their coding and billing procedures in order to meet the growing complexity of medical coding. Due to its significant impact on the revenue cycle, it is crucial to identify which tasks can or cannot be easily automated and the right blend of technology as well as human expertise that would yield the best results.
Computer-assisted Coding: An Outdated Solution
Computer-assisted coding (CAC) software analyzes medical records and generates accurate medical codes for specific terms and phrases within the record. Additionally, this software also examines the context of the data enclosed within clinical documents to ascertain whether coding may be required. For instance, when referring to a diagnosis of hypertension, the software can deduce that the term "hypertension" has to be coded. In contrast, the same cannot be considered while discussing a "family history of hypertension".
Although the concept of CAC first surfaced in the 1950s, it garnered substantial prominence only around 2015, driven by the transition of US healthcare from the 30-year-old ICD-9 to the ICD-10 standards. The latter was significantly larger and more detailed. Thereby, CAC emerged as software with significant relevance and utility, by reducing the time spent per record by 22 percent compared to manual coding processes.
From a technology standpoint, computer-assisted coding presents a successful use case of NLP in healthcare. Natural language processing, a confluence of AI, linguistics, and computer science, enables computers to comprehend human writing to some extent and can solve different tasks, iincluding those of CAC.
Implementing CAC does not eliminate the need for medical coders, but rather increases their efficiency. This software enables the coders to review, revise, and validate medical codes. However, the downside to CAC is that it merely augments the clinical knowledge and coding experience of medical coders rather than fully automating the coding process. The software seeks human assistance, provides reduced accuracy in coding suggestions, and increases the likelihood of denied claims, thereby extending the revenue cycle and adding cost burdens. This is where intelligent autonomous medical coding differs from CAC and manual coding processes by providing a more holistic solution for the aforementioned issues.
Enter Autonomous Coding
Recent advancements in machine learning, artificial intelligence, natural language processing, and clinical language understanding capabilities have enabled the development of the holy grail of the field—a medical coding technology that can convert medical information into coded data to obtain reimbursement from insurance providers without any human intervention.
Autonomous coding is a solution pioneered to propel medical coders beyond the limitations of CAC and its predecessors. This software is designed to understand and translate unstructured clinical notes into structured medical codes without the need for human assistance.
One of the key benefits of autonomous coding is its positive impact on revenue. A healthcare organization's profit margin can be exponentially increased through the autonomous coding engines' ability to seamlessly integrate into the normal flow of RCM and swiftly process a large volume of charts. Furthermore, since the software produces consistent results, it aids in reducing claim rejections. Consequently, this modern technology saves money and time, while significantly shortening the revenue cycle.
Autonomous medical coding employs a new generation of sophisticated computer algorithms to code charts within seconds without any human intervention. The intelligent autonomous engines are fully cognizant of which codes are most accurate to assign and their relevance. If any information in the medical record is ambiguous, these unhandled charts may occasionally be flagged for manual coding.
Introduction
Medical coding is, perhaps, the most crucial and labor-intensive part of revenue cycle management, requiring skilled and certified resources. The United States is the world's largest market for medical coding and is expected to expand from 2022 to 2030 at a compound annual growth rate (CAGR) of 8 percent.
However, the coding process has become increasingly complex over the years due to the dynamic regulatory environment and clinical documentation requirements. The efficiency of the medical coders has undoubtedly decreased as a result of frequent modifications to the medical coding standards. Needless to say, this has led to a rapid increase in revenue leakage, a shortage of professional coders, and constant pressure to keep abreast with changing guidelines while enhancing productivity and quality.
Primary Obstacles in the Realm of Medical Coding
The key concerns in the healthcare sector pertaining to coding include claim rejections, inaccuracies, errors, and delayed time to payment. The coding workforce must be fully up to speed, given that inaccurate coding accounts for 68 percent of hospital denials, with these denied claims costing hospitals over $262 billion annually.
Training a coding workforce has become more challenging as a consequence of the transient and volatile coding standards. It is nearly impossible to remain up-to-date on the myriad of payer-specific coding guidelines while concurrently anticipating future revisions.
Several health systems have begun relying on technology to automate some of their coding and billing procedures in order to meet the growing complexity of medical coding. Due to its significant impact on the revenue cycle, it is crucial to identify which tasks can or cannot be easily automated and the right blend of technology as well as human expertise that would yield the best results.
Computer-assisted Coding: An Outdated Solution
Computer-assisted coding (CAC) software analyzes medical records and generates accurate medical codes for specific terms and phrases within the record. Additionally, this software also examines the context of the data enclosed within clinical documents to ascertain whether coding may be required. For instance, when referring to a diagnosis of hypertension, the software can deduce that the term "hypertension" has to be coded. In contrast, the same cannot be considered while discussing a "family history of hypertension".
Although the concept of CAC first surfaced in the 1950s, it garnered substantial prominence only around 2015, driven by the transition of US healthcare from the 30-year-old ICD-9 to the ICD-10 standards. The latter was significantly larger and more detailed. Thereby, CAC emerged as software with significant relevance and utility, by reducing the time spent per record by 22 percent compared to manual coding processes.
From a technology standpoint, computer-assisted coding presents a successful use case of NLP in healthcare. Natural language processing, a confluence of AI, linguistics, and computer science, enables computers to comprehend human writing to some extent and can solve different tasks, iincluding those of CAC.
Implementing CAC does not eliminate the need for medical coders, but rather increases their efficiency. This software enables the coders to review, revise, and validate medical codes. However, the downside to CAC is that it merely augments the clinical knowledge and coding experience of medical coders rather than fully automating the coding process. The software seeks human assistance, provides reduced accuracy in coding suggestions, and increases the likelihood of denied claims, thereby extending the revenue cycle and adding cost burdens. This is where intelligent autonomous medical coding differs from CAC and manual coding processes by providing a more holistic solution for the aforementioned issues.
Enter Autonomous Coding
Recent advancements in machine learning, artificial intelligence, natural language processing, and clinical language understanding capabilities have enabled the development of the holy grail of the field—a medical coding technology that can convert medical information into coded data to obtain reimbursement from insurance providers without any human intervention.
Autonomous coding is a solution pioneered to propel medical coders beyond the limitations of CAC and its predecessors. This software is designed to understand and translate unstructured clinical notes into structured medical codes without the need for human assistance.
One of the key benefits of autonomous coding is its positive impact on revenue. A healthcare organization's profit margin can be exponentially increased through the autonomous coding engines' ability to seamlessly integrate into the normal flow of RCM and swiftly process a large volume of charts. Furthermore, since the software produces consistent results, it aids in reducing claim rejections. Consequently, this modern technology saves money and time, while significantly shortening the revenue cycle.
Autonomous medical coding employs a new generation of sophisticated computer algorithms to code charts within seconds without any human intervention. The intelligent autonomous engines are fully cognizant of which codes are most accurate to assign and their relevance. If any information in the medical record is ambiguous, these unhandled charts may occasionally be flagged for manual coding.
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