Will AI Take Over Medical Coding?

Medical diagnostic and treatment coding has long been a difficult problem. Even in simpler times, it was challenging to translate a patient's complicated symptoms and a clinician's efforts to treat them into a distinct and unambiguous categorization code. However, in order to keep clinical records, review hospital operations, plan future hospital operations, and perhaps most importantly, be reimbursed financially, hospitals and health insurance providers now require extremely detailed information on a patient's condition and the steps taken to treat it. Machine learning and artificial intelligence are not simply trendy terms for contemporary innovation. It will determine how the technology sector, as well as virtually every other business, including healthcare, will develop in the future. In the healthcare sector, neither the business practices nor the methodology is largely unchanged. The systematization of medical coding has proven essential for the advancement of healthcare as AI automates the healthcare industry. There are no individuals who are familiar with medical coding outside of the medical community. The most recent International Classification of Diseases (ICD) edition has more than 72,000 billable codes, making it a complex system that needs expert oversight to be managed effectively. Medical codes appear to be a distinct language to a layperson, and in some ways they are. Clinical encounters are converted into billable codes by clinicians and revenue cycle employees in the very complicated medical coding process, which is used for performance tracking and reimbursement. The codes that eventually lead to claims describe a patient's experience. 

Being able to comprehend a clinical encounter is crucial for both patient care and keeping the doors open. Medical coding is possibly more difficult than ever, though, in a world where payment regulations and documentation standards are always changing. ICD-10, the tenth iteration of the World Health Organization's International Classification of Disease codes, is the current global standard for medical coding (WHO). Over 14,000 diagnosis codes may be found in ICD-10. ICD-11, the next revision to this global standard, was already formally accepted by WHO member nations in May 2019. ICD-11 implementation will start in the US and other WHO member states in January 2022. Over 55,000 diagnostic codes make up the new ICD-11, which is more than four times as many as the ICD-10 used by the WHO. In fact, at least in the United States, there are far more codes than the ones listed above. About 140,000 categorization codes, including 70,000 for diagnosing conditions and another 70,000 for classifying therapies, make up an improved version of IDC-10 designed specifically for use in the United States. Given that the US version also contains treatment codes and has previously featured a greater number of diagnostic codes, we anticipate the upgraded version of IDC-11 that will be specifically used in the US will include at least many times as many codes as the WHO version of IDC-11. The use of computer-assisted coding systems has progressively grown over the past 20 years throughout the healthcare sector as a way to deal with the growing complexity of coding diagnoses and treatments.Modern machine learning techniques and other aspects of artificial intelligence have been incorporated into more recent iterations of computer-assisted coding systems to improve their ability to analyse clinical documentation (charts and notes) and determine which codes apply to a given case.

AI Will speed Up Medical Billing Processes

Deep learning of how users interact with EHR and billing software may have the most influence on a medical biller's day-to-day operations. Nearly all of the major health IT companies place a high premium on using artificial intelligence to learn a user's behaviours, predict their requirements, and present the appropriate data at the appropriate time. The work required for manual billing duties may be greatly reduced with the use of automated information retrieval and manipulation, and staff could make better judgments about how to proceed with refusal resolution.

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Increased Accuracy Of Medical Coding 

The capability of AI to evaluate spoken and written language will be a critical component. In order to issue precise codes, systems will be able to learn the language for operations and diagnoses. This feature might significantly lessen the transition that occurs with coding upgrades by ensuring that proper codes are utilized and documentation is compliant following code set modifications. If AI had been employed, just think of how much simpler the switch from ICD-9 to ICD-10 would have been!

Pre-authorizations That Are Instant 

Making inferences and predictions will be one of AI's most useful capabilities. Today, obtaining a pre-authorization from a payer might take hours, sometimes even days. Future technologies could be able to quickly assess a patient's health information and determine if an operation is medically necessary. The good news for medical billers is that an automated procedure will guarantee authorization has been received and its associated data has been collected, considerably decreasing (or eliminating) pre-authorization denials based on a lack of an authorization number.Not just the healthcare sector, but all industries, will employ AI technology. Customer service innovations, such as bots that are used for patient contacts like appointment booking and payment collection, might have a significant impact on how patient communications are handled. The procedure can be standardized, which has considerable advantages for both billers and doctors. It decreases the likelihood of challenging patient contacts over billing and may even strengthen patients' relationships with billing personnel.

Future efficiency and profitability gains will likely be driven by artificial intelligence. However, the capacity of AI to evaluate data and make informed judgments may present an even greater chance to boost income. A situation where denials are increasing as a result of a lack of medical necessity, inadequate paperwork, or coding errors would be an illustration of this. AI would examine the rejections to determine their root cause, then provide prompts inside the EHR/PM to address the problems, collect all necessary data for accurate coding, and verify that the provider's notes are comprehensive. Advances in AI will assist payers as well as healthcare providers and medical billing personnel. The fiscal year 2018 had an overall wrong payment rate of 8.12%, or $31.62B, according to CMS. Funders may see significant cost reductions via "smart" AI systems, which should reduce the overall cost of healthcare for patients.

Topics : Voice Activated Computer Software