Autonomous Vs. Manual Coding: What are the key differences?

Medical coding is, perhaps, the most important and labor-intensive aspect of revenue cycle management (RCM). It requires skilled and certified resources that can go through stacks of patient records and convert the data into universal alphanumerical codes.

This entire process not only takes up a lot of time but also consumes a significant portion of your practice’s budget. After all, you have to hire and train those medical coders to the best of their abilities.

But what if you had a coder who could code millions of charts per day with unparalleled speed and accuracy?

Enter autonomous medical coding—an AI-powered coding process that has the potential to revolutionize the field of medical coding.

But how?

To understand its impact, let’s first look at manual coding – its definition and challenges, and find out how autonomous medical coding addresses and overcomes these pain points.

Let’s dive in!

Manual Medical Coding Processes – A Quick Recap

The manual medical coding process starts with the coder reviewing medical documentation, like progress notes, lab results, and operative reports. They then assign the appropriate codes using specialized software and tools like Electronic Health Records (EHR). Once the coding is done, the claims are submitted to insurance companies for a payout.

The payer then reviews the claims, and up to 20% of those might be denied and sent back to the coder.

Here’s an alarming fact:

65% of denied claims are never reworked, which ultimately leads to revenue leakage for the practices.

For the claims that are approved, a bill is generated by the insurance company, and the payment is sent to the healthcare provider. The whole process takes about 5-7 days.

The Challenges of Manual Coding

Lack of Efficiency: One of the biggest drawbacks of traditional medical coding is the compromise on the efficiency of the overall process. Although medical coders are highly skilled and experienced, it does not change the fact that they are human beings with limitations.

For example, medical coding becomes very difficult in an emergency where there’s a sudden increase in the patient influx. This is because the complexity of coding demands that the medical coder pay attention to minor details, thus slowing them down.

Susceptible to Errors: With manual medical coding, one major disadvantage is the increased proneness to errors. Medical coders have a heavy responsibility resting on their shoulders as even the slightest error, be it a typographical error to an incorrect code, can lead to significant losses.

These errors can cost the provider dearly. How? Because misinterpretation can result in irrelevant prescriptions and treatments, thus posing a threat to the patient’s well-being and the provider’s reputation.

Limited Scope of Scalability: The sustainability of any organization depends on its ability to evolve with the fluctuating market demands. However, the conventional approach toward medical coding can slow down this process of scalability. No matter how good your medical coders are, they have a specific capacity and processing speed.

The usual solution?

Hire more coders and train them, thereby increasing the cost of labor.

The ideal solution?

Your medical coding process should be equipped to deal with evolving changes in the healthcare market. And the only way to do so effectively is by shifting gears towards autonomous medical coding!

What is Autonomous Medical Coding?

Advancements in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and clinical language understanding capabilities have enabled the development of the holy grail of the field: autonomous medical coding.

Autonomous or automated medical coding leverages these advanced technologies to extract relevant information from medical documents and suggest standardized medical codes such as ICD, CPT, and HCPCS to coders. These codes are used for billing, reimbursement, quality assessment, and research purposes. AI-powered autonomous medical coding can not only improve coding accuracy but also increase efficiency by reducing the workload for human coders.

How Autonomous Medical Coding Works

When it comes to autonomous coding, we start with an automated data transfer from the clinical practice to AI software. Artificial intelligence then autonomously codes 95% of the claims without any human intervention and forwards them to the payer for billing.

The remaining 5% of the claims that are complex and need human involvement are handled by medical coders. Denials are rare, and when they occur, they are immediately reanalyzed and re-coded by the autonomous medical coding software. The whole process takes less than 24 hours to complete.

Manual Vs. Automated Medical Coding: The Comparison

1. Efficiency and Accuracy: As we already know, conventional medical coding methods are labor-intensive and prone to human errors. Whereas, automated systems, armed with advanced algorithms and AI, can swiftly sift through patients’ complex notes within seconds.This helps dramatically cut down on coding time and errors, improving the efficiency and accuracy of your practice.
2. Consistency and Compliance: In the world of medical coding, compliance with established coding guidelines and regulations is of paramount importance. Any failure to do so leads to financial penalties.But with AI-powered autonomous coding software, you don’t have to worry about running that risk. Programmed with the latest coding rules and updates, they guarantee compliance with industry standards. Where manual coding can diverge based on the medical coder’s aptitude, autonomous systems ensure a uniform approach.The result? No coding inconsistencies and unexpected audit complications.
3. Increased Productivity: As a healthcare provider, the last thing you want to do is waste your medical coders’ potential. And because automated systems effectively handle repetitive coding tasks, human coders have much more time to tackle complex cases. Such newfound productivity can fuel improved healthcare delivery and reduce administrative overheads.For instance, a medical coder spends approximately 15 minutes reviewing a patient’s chart. On the other hand, an autonomous medical coding solution usually takes only a couple of seconds to do the same thing, thus freeing up the valuable time of your coders.
4. Continuous Improvement: While no coding solution, manual or automated, is perfect from the onset, autonomous coding systems offer a major advantage: they constantly improve over time. These advanced systems analyze vast amounts of data, learn from patterns, and continuously adapt to coding practices and guidelines.Such an ongoing refinement across architectures reduces the need for manual corrections and rework, resulting in enhanced efficiency and cost savings.

The Future of Autonomous Medical Coding

While it may sound like autonomous coding is a completely “hands-free” solution, the truth cannot be further than that. The future of automated coding will thrive through a continuous feedback loop that involves a collaborative effort between human coders and AI systems on a single platform.

One example of such a solution is Aidéo-Tech’s Gemini platform. Our AI-powered autonomous coding platform leverages AI and ML to generate codes for no-touch billing across numerous clinical specialties and settings. Our coding technology processes up to 12 encounters per second, or 43,000 per hour, measuring throughput in seconds or minutes.

The result—reduced turnaround time by more than 50% and improved coder efficiency by up to 65%.

Revolutionize your practice with Gemini’s AI-driven autonomous medical coding solutions. Unleash unparalleled accuracy, slash costs, and reclaim your time now!

Book a free demo now.

FAQs

Q1. What is autonomous medical coding?
Answer: Autonomous medical coding is a technology-driven process that analyzes electronic patient data to generate a complete, ready-to-use code set for billing. It combines the capabilities of machine learning algorithms, natural language processing (NLP), and advanced data analytics to streamline the coding process.

Q2. What are the key differences between manual and autonomous medical coding?
Answer:

Q3. Can automated coding replace human coders?
Answer: Not entirely. Human intervention is required for complex cases. AI coding systems and human coders need to work together for maximum efficiency.