Imagine a world where medical billing doesn’t stress out healthcare providers. All that complex coding, denied claims, and endless paperwork are a thing of the past. And healthcare providers only focus on delivering the best care to their patients.
Well, it’s possible in the sense that doctors can outsource their revenue cycle management to a third-party vendor. That way, doctors don’t get to deal with all this, and the administrative nightmare gets transferred to an entity that specializes in dealing with all that.
But the inherent problem stays the same. Because if outsourcing them had had any operational improvement impact, we wouldn’t see a surge in the rate of denied claims in the US. It’s hard to agree on a single number for denied claims because the rate varies for each insurance provider. But it’s not something you can easily overlook.
So why is that? And how can we improve that? Can AI automate medical billing in healthcare as it has many other domains and industries worldwide? Come let’s find out.
Current State of Medical Billing
Medical billing, without any AI infusion, is a tedious process involving a lot of paperwork. These paper records are prone to human errors, and are only as good as the person filing them. Healthcare providers have to manually enter patient details, curate the documents from every stage of the process, and assign the right code.
Some common pain points of healthcare providers include:
Coding mistakes: The ICD-10-PCS has over 70,000 procedure codes, and almost the same number of diagnosis codes in ICD-10-CM. The code differs for even the slightest change in a procedure, which makes the coding process susceptible to incorrect input.
Claim submission delays: Manual submission process can cause delays in some cases. For example, the person in-charge forgot to submit them.
Eligibility verification: If the first contact person enters incorrect information, it leads to incorrect eligibility verification, which trickles down.
Compliance challenges: Healthcare regulatory bodies keep updating existing policies to widen their scope and adapt to current times. And keeping up with all the updates can get overwhelming for healthcare providers.
Denied claims: Those manual errors and incorrect eligibility verifications lead to denied claims, which add to the administrative burden on a healthcare provider’s end.
Key Areas Where AI is Automating Medical Billing
Here is how AI is simplifying medical billing processes so healthcare providers can focus on saving lives, and not worry about the paperwork.
Patient Registration and Eligibility Verification
Eligibility checks can be made easy with AI integration. AI-powered software can auto-fill forms by reading the text and filling forms using optical character technology (OCR).
It only gets better from there. These AI software applications can verify input from multiple databases in real-time for better accuracy. You can even take it a step further and use it to predict eligibility information based on historical data and current policy information.
Medical Coding Assistance
This is another benefit of such AI-powered applications. The software uses Natural Language Processing (NLP) to analyze clinic notes, identify correct issues, and suggest codes accordingly.
The same way, you can use machine learning to learn from historical coding patterns to improve its accuracy. To make sure that coding is compliant with all applicable regulations and standards, the software can validate the codes as well.
Claims Processing and Submission
Automated claim submission is becoming more and more popular nowadays. The software can automatically extract relevant information from medical records. It then checks the claims for completeness and accuracy before submission. And it can give you insights into historical patterns and allow you to gauge your performance.
Denial Management and Appeals
These appeals can also be simplified with the help of AI tools. Normally, a provider would have to identify the error, rectify it, and file the claim again. But the AI software lets providers analyze denial patterns so it doesn’t happen too often in the future.
Specialty-specific application
This is another developing area where vendors offer AI solutions tailored to the specific needs of a specialty. For example, a cardiologist would benefit more from an intelligent EHR system with integrated cardiology billing services component.
The software can interpret complex cardiac reports and suggest appropriate CPT codes, reducing the number of denied claims. The same way, it can help with accurate coding for bundled services and complex procedures in cardiology.
Challenges and Considerations
AI automation is facing some key challenges that need to be addressed before its widespread adoption in the healthcare industry.
High Initial Costs
AI tools require significant initial investment to develop and implement the technology infrastructure, which is one of the main hurdles as of today. Not everyone is ready to shell out the big bucks to make the switch to AI.
Trainings and ongoing support make up a significant part of the initial investment. And it also takes time for them to be fully proficient in using newer technology. This also means that the workflow may be affected during the implementation.
Patient privacy and data security concerns
AI models are run by data servers containing sensitive patient information, which must remain protected. Despite technological advancements, most systems can have some vulnerabilities because of human errors, which is another challenge in AI automation.
Integration with existing systems
Modern AI applications can have compatibility issues with some legacy billing and EMR systems, which is another challenge in its wide scale adoption. If a hospital is using a mix of digital tools and paper records in their processes, transferring data between systems is another challenge. Lastly, when new tech is being installed, making sure that workflows experience minimum disruption can get challenging.
Conclusion
AI is set to change how we see healthcare and quite possibly offer transformative solutions to long-standing challenges like medical billing in this industry. As we move forward, we’ll continue to see more and more applications of AI. And embracing it before it’s too late is necessary to stay relevant.
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