A Brief AI Primer
We are using AI on a daily basis, even if we don’t think about it. Spam filters let us ignore millions of malicious and useless emails. Netflix recommends the TV shows and movies we watch.
The term was first used in 1955 by John McCarthy. It was based on the idea that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
There are two types of approaches to AI. The rules-based approach performs tasks based on programmed rules. The other is machine learning models. Earlier AIs such as Deep Blue from IBM were rule-based. These days, most AIs use sophisticated machine learning models.
What is Machine Learning (ML)?
Machine learning AIs use algorithms to learn from labeled data through iterative cycles. ML has applications across many fields, including medical billing and coding.
Medical coding and revenue integrity challenge
Medical coding and revenue integrity are at the heart of the American healthcare system. The global medical coding market size is expected to reach USD 25.4 billion by 2025, according to a new report by Grand View Research, Inc., registering a 10.0% CAGR during the forecast period.
A March 2016 NBC News report noted an error rate of 7 percent to 75 percent in medical claims, depending on the source of the statistics. In 2010, the Office of the Inspector General reported that 42 percent of Medicare claims were improperly coded and 19 percent lacked sufficient documentation.
One of the lasting challenges for revenue integrity is error free Medical coding that is auditable.
In this article, we’ll look at how artificial intelligence (AI) is being used to prevent coding and billing mistakes.
A persistent issue facing the medical coding industry is the nature of its audits, which take place towards the end of the revenue cycle. Even if errors are detected, it’s too late to correct them since the cost of rectification is very high.
With the rise in digital healthcare, billable codes have now crossed 70,000. This has led to an increased need for medical coders.
The complex job of medical coding is done manually. A Medical coder can handle only so many coding tasks efficiently. This deficit in the number of skilled coders has put an enormous strain on the existing workforce. This has been a contributing factor in the number of errors made which subsequently can’t be rectified because of the current auditing process and costs.
AI to the rescue – error free medical coding
The biggest ongoing challenge for the industry is coding accuracy. AI offers the robust ability to automate manual coding labor using AI – more specifically Machine Learning and Natural Language Processing (NLP).
A trained AI automatically identifies and extracts data from medical documents. With this data, the AI significantly reduces the manual effort of a medical coder, giving them much needed time to perform error checks on the data to reduce billing mistakes.
This does not replace medical coders. AI simply lightens the workload by taking advantage of its ability to process codes and high volumes of data with precision, automating tedious error prone facets of coding. This frees up trained coders to step in where necessary to correct errors.
Good for patients, healthcare providers and insurance companies
Medical coding and revenue integrity form a link between patients, healthcare providers and insurance companies. Speeding up this error prone area of the healthcare system is paramount to improve our collective well-being.
Faster claims processing can happen only if the medical codes are precise. This is what AI enables.
Faster revenues for you, the healthcare provider, means better, faster health services for the patients. It’s time to modernize medical coding and revenue integrity with the power of AI so you can get back to what really matters – healing people.
Exdion uses AI and deep learning to prevent coding and billing mistakes, and eventually improve revenue. Contact our experts to automate medical coding, increase overall revenue and enhance the customer experience.