Autonomous Medical Coding - The Step-by-Step Guide

As the industry moves towards value-based care, you cannot rely solely on manual workflows to meet Revenue Cycle Management (RCM) goals.

One function that can immediately benefit from automation is medical coding. You can now use AI to implement a hands-free system that scales coding capacity without proportionally scaling labor.

This post explains how autonomous medical coding works and how you can implement it in your healthcare organization.

What is Autonomous Medical Coding and Why Does it Matter for Healthcare Providers?

Autonomous medical coding uses AI to analyze clinical documentation and assign medical codes with minimal or no human intervention.

Because the goal is to replace manual workflows, this differs from Computer-Assisted Coding (CAC), which requires significant human support.

The automated workflows matter for healthcare providers for the following reasons:

• Shortage in medical coding talent: Coding needs are growing faster than the talent pool can expand, resulting in an estimated 30% shortage.

• Rising coding complexity: With payor rules changing and ICD-10 introducing over 70,000 diagnostic codes, the complexity is making it harder for manual workflows to maintain accuracy and fast turnaround times.

• Need for stronger revenue performance: Healthcare leaders are seeking ways to strengthen revenue performance, and autonomous coding has proven effective in reducing revenue leakage from undercoding and high denial rates.

How Does Manual Medical Coding Lead to Revenue Loss?

While the industry has highly skilled medical coders, revenue leakage can occur due to human error and the variability of manual workflows.

Here’s how these workflows are causing revenue loss:

• Scalability limits: Even with standard operating procedures in place, different coders may interpret the same chart differently. This variability creates gaps that make it difficult to scale operations and grow revenue.

• The cost of labor: Manual medical coding requires constant recruitment, training, and retention of skilled coders, which can be costly. In contrast, you can scale autonomous medical coding without proportionally scaling personnel costs.

• The cost of human error: Manual coding errors contribute to denials. While you can recover some through rework, others (such as documentation-related denials) result in permanent revenue loss.

Additionally, there is an opportunity cost to having your team focus on repetitive documentation or coding tasks. The time can be spent on higher-value projects that improve patient outcomes and revenue cycle management.

How AI Improves Healthcare Workflows?

AI can significantly improve your healthcare workflows by streamlining operations and enhancing compliance.

Here’s how:

• Validation workflows: Validation is necessary for compliance and quality assurance, but it is often time-consuming. AI-driven validation workflows can help you automate much of this process by flagging inconsistencies, identifying missing documentation, and ensuring coding aligns with payor guidelines.

• Continuous improvement: One of the benefits of AI-supported workflows is that models improve over time as they incorporate feedback and new data. You’ll have a system that continuously reduces errors and easily adapts to changing coding standards.

• Automating charting: You can use Ambient Clinical Intelligence (ACI) technology to automatically generate structured clinical notes during patient conversations. The best AI scribes for physicians have workflows that allow you to push the generated notes directly into your coding solution or EHR.

At DeliverHealth, we can help you implement an AI-powered solution to automate charting and reduce your documentation time by up to 75%.

We offer two solutions as follows:

• InstaNote: Our AI-powered documentation solution that automatically turns your patient conversations into structured clinical notes. It includes coding suggestions.

• InstaCode: Our AI-driven coding solution designed to improve coding accuracy and capacity. It supports autonomous medical coding. Also, it integrates with InstaNote for unified documentation and coding workflows.

Ready to see how these solutions can help you improve healthcare workflows?

Schedule your free demo to explore how we can help you accelerate reimbursements and maximize revenue.

How Autonomous Medical Coding Works - Step By Step

Autonomous medical coding replaces manual processes with automated workflows.

Let’s look into the typical workflow step by step:

Step 1: Ingest Data

Most popular coding solutions integrate seamlessly with popular EHRs, EMRs, and practice management software.

Integration allows the tools to ingest the data you need for coding, e.g., clinical notes and reports, without manual hand-offs.

If you’re using InstaCode, you have the option of pulling billable codes and coding suggestions directly from InstaNote.

Step 2: Interpret Clinical Context

The next step requires using an autonomous coding engine.

The engine uses AI capabilities, such as Natural Language Processing (NLP), to interpret the clinical context of the ingested documentation and identify diagnoses, procedures, and modifiers.

In the case of InstaCode, we use PerformPlatform, our autonomous coding engine that supports flexible execution, allowing coding to occur on our platform or directly in your EHR.

Step 3: Generate Codes

After the engine interprets the clinical context, it compares the interpreted data against current coding guidelines. It then applies the guidelines and assigns the relevant ICD-10, CPT, and HCPCS codes.

Some advanced systems, like InstaCode, run edits during the active coding session to improve accuracy and prevent downstream rework or claim issues. The capability is powered by robust rules engines and embedded audit features.

Step 4: Assign Confidence Scores

As a quality assurance measure, the engine assigns a confidence score to its work. High-confidence results are autosubmitted, while the low-confidence ones are pushed for human review.

InstaCode’s powerful technology typically achieves high confidence in 40% - 70% of cases (95% accurate), and the cases flow straight to billing. The remainder flows to your human coding team member for review.

Step 5: Learn and Improve

The system gets smarter over time by learning from edits made during the human review stage.

Also, it can use data from your claims management department to reduce common errors that cause denials. For instance, it can use acceptance rates and audit findings to fine-tune coding rules over time.

How To Implement Autonomous Medical Coding in Your Organization

How can you implement autonomous medical coding in your organization while ensuring compliance and some level of human control?

Here’s a tested roadmap:

• Vendor selection: You’ll need a vendor with a reliable Autonomous Coding Engine (ACE) to support the endeavor.

• Evaluate coding scenarios: Assess your existing processes for low-complexity encounters that are handled at a high enough volume to support coding automation. You and the vendor can target these services in the pilot program.

• Data integration: Ensure your documentation is clean and structured properly for coding logic. You can then integrate the coding solution into your EHR for seamless data ingestion.

• Put a human in the loop: You want to decide when your coding team intervenes. Possible scenarios include low-confidence cases or complex encounters.

• Run a pilot, then scale: Use the data from the workflow evaluation stage to run a pilot program. The results of the pilot will help you compare the coding solution to your manual workflows in terms of accuracy, turnaround times, denial rates, and more. If the pilot is promising, you can proceed with a full rollout.

Challenges That Limit the Adoption Of Autonomous Coding Solutions

With a good implementation partner by your side, you’ll avoid most of the challenges that hinder the adoption of autonomous coding solutions.

Nonetheless, it still helps to be aware of the following industry-wide issues:

• Integration issues: Some legacy EHR systems do not integrate well with modern AI solutions, creating an interoperability bottleneck. If you are running on a popular EHR system, such as Epic, Athena, PACE, Allscripts, or ModMed, this will not be an issue. We support seamless integrations with popular EHRs.

• Regulatory compliance concerns: Coding guidelines and payor-specific requirements change. Some providers fear that they may not be able to keep up with these guidelines if they hand over coding to autonomous solutions. It’s vital to remember that leading vendors update their systems as soon as new guidelines come up.

• Change management issues: Some RCM staff may fear that autonomous coding solutions will “replace” them, causing resistance to change. The reality is, autonomous coding allows you to elevate how you use your RCM team. It frees up their time to focus on higher-value tasks such as denial management to improve revenue performance.

Frequently Asked Questions (FAQs)

Let’s now answer some of the commonly asked questions about autonomous medical coding:

What Is the Difference Between Autonomous Coding and Computer-Assisted Coding?

The difference lies in the amount of human effort required.

Autonomous coding involves end-to-end automated workflows with no (or minimal) human involvement. However, CAC requires a human coder to review, validate, and finalize every case.

What Level Of Accuracy Can AI Medical Coding Systems Achieve?

Accuracy levels depend on the vendor and their coding engine.

As we mentioned earlier, InstaCode scores 40%-70% of cases as “high-confidence” (95% accurate), sending them straight to billing without human review. That said, many cases that require human review still turn out to be accurately coded.

What Risks Do Healthcare Providers Face With Fully Automated Coding?

It helps to be aware of the following risks that come with fully automated coding:

• Amplification of mistakes as automation makes errors scale quickly.

• Compliance and audit risk if your team can’t explain how codes were assigned.

• Missed quality improvement opportunities where a human review would have enhanced documentation integrity or coding accuracy.

You can mitigate risks by working with a reputable vendor that offers a tailored implementation process. Also, ensure the vendor offers ongoing support.

What Role Do Human Coders Play In AI-Driven Coding Systems?

The role of human coders shifts from coding every chart to reviewing and validating the AI-assigned codes.

They also help optimize the autonomous coding process over time for accuracy and compliance.

Conclusion

Manual coding can lead to revenue loss due to scalability limitations and the costs of labor and human error. Autonomous medical coding can help you address these inefficiencies by enabling scalability, faster coding, greater accuracy, and lower operational costs.

Also, you can implement autonomous coding without losing control.

At DeliverHealth, we offer InstaCode, a powerful autonomous medical coding solution that helps you scale coding capacity without your team losing oversight.

The AI-powered coding solution comes with SmartAudit features that support a hybrid coding approach. You can combine full autonomous coding with partial automated coding and manual review.

Explore InstaCode to see how we can help you transform your coding workflows to maximize revenue.