Providers and Payers would be familiar with a situation where their medical coders struggle to proceed when there is more than one way to code a particular medical diagnosis and treatment.
The latest revision of WHO’s International Classification of Diseases ICD-11 contains 17,000 unique codes and more than 120,0000 codable terms yielding +1.6 million clinical situations! Searching for the most appropriate ICD code and mapping the available medical data accurately is one of the biggest challenges the healthcare system faces today.
Unstructured medical data is another perennial challenge. About 80% of medical data remains unstructured according to an NCBI journal. Extracting information from unstructured EHR data to map it to the right code is another big challenge.
The sheer volume of healthcare data that flows into the system comes next. Around 30% of the total data that gets generated around the world comes from the healthcare industry. And the compound annual growth rate of healthcare data is expected to reach 36% by 2025, according to RBC Capital Market. This suggests that the challenge of unstructured incoming data is going to grow further in the coming years.
Medical coding is therefore quite complex. And as healthcare evolves into a data-heavy industry, these complexities are only going to grow further.
As providers try to be more precise in how they bill their patients for the treatments to maximize their revenue cycle management, building resilience to cope with these rising challenges of medical coding becomes a necessity. And for this, the healthcare industry must turn to AI-assisted medical coding – the answer to all woes.
Research shows that healthcare organizations that use AI-driven medical coding solutions are able to reduce their coding process times by almost 80%.
Adopting New-age AI Technologies for Medical Coding and Billing
EHR systems store a lot of unstructured data. For e.g., narrative data like free-text clinical notes, discharge summaries, test reports, surgical records, notes on the patient’s family history of a medical condition, and other such data – all of which is valuable information.
Medical coders spend hours going through this unstructured EHR/EMR data to manually extract and map the notes to the right ICD-10 code. Spelling errors, abbreviations, and other ambiguities make it even more complex to understand this information. Such manual processes can be both time-consuming and error-prone. And accuracy of medical coding is very important in ensuring a positive patient experience
For a healthcare business looking to sustain and scale up, it must move towards AI/ML technologies and upgrade its existing traditional medical billing and coding processes. This way it can efficiently leverage all this data and bring in the highest level of accuracy and speed to medical coding.
How KANINI Can Help in Upgrading Your Manual Medical Coding Processes
KANINI helps healthcare enterprises build a resilient business model using advanced digital engineering solutions and accelerators powered by AI/ML technologies. Our experience and expertise in healthcare IT have empowered many healthcare businesses with the right insights for success. If you are facing challenges with your existing medical coding and billing processes and looking for a transformation through technology, get in touch with us.