Medical billing and coding have been undergoing many changes in recent years as the healthcare industry increases in complexity while the variety of treatments and procedures grow by the minute.
As a result, healthcare organizations are facing many technical and administrative challenges when it comes to ensuring accurate and efficient billing and coding:
- There are over 70,000+ billable codes, the complexity of which significantly increases the demand for medical coders. The supply of skilled professionals who can translate EHR (electronic health records) data into codes accurately and quickly isn’t keeping up with the high demand.
- Coders need to manually match each medical visit and procedure with a corresponding code from the 70,000+ available, which is a labor-intensive and error-prone process.
- The information then needs to be keyed into various systems for different purposes, such as accounting and the generation of patient statements, which is yet another time-consuming process prone to human errors.
- Audits typically take place towards the end of a revenue cycle. Even if errors are discovered, it’s often too late to rectify them as the cost of doing so is typically higher than the amount associated with the initial errors.
- The complicated and time-consuming manual coding process isn’t scalable. With the limited supply of skilled coders as well as the ever-increasing complexity and workload, many organizations are plagued by costly inaccuracies.
- According to the Centers for Medicare & Medicaid Services (CMS), medical billing errors have led to $36.21 billion in improper payments in FY2017.
The healthcare industry is in urgent need of a scalable solution that can process the vast amount of patient data without compromising speed and accuracy of the billing procedure.
The Use of AI in Medical Billing and Coding
The use of artificial intelligence in the medical billing and coding industry can help healthcare organizations facilitate their billing procedures while minimizing costly errors.
AI-driven technologies, such as machine learning and natural language processing (NLP), have the ability to interpret and organize a large amount of data quickly and accurately. As such, they’re perfect solutions for identifying and extracting data from EHR, then matching the information with relevant medical codes.
In addition, these technologies can contextualize unstructured data by analyzing information from various sources and connecting the dots. For instance, an AI program can arrange data from different records into a logical timeline to make sense of disparate events, diagnoses, and procedures, minimizing coding and reporting errors.
The Benefits of Using AI in Medical Billing and Coding
AI-driven technologies will become widely used by healthcare organizations to improve the cost-efficiency of their billing and coding processes thanks to their many advantages:
- Automating the coding process to deliver a scalable solution that is less dependent upon the availability of skilled billing professionals.
- Improving cash flow by ensuring that accurate patient statements are issued in a timely manner.
- Reducing standard work hours of administrative staff, which can help improve the quality of their work in other areas.
- Eliminating costly human errors, not only by matching EHR data with the right medical codes but also through system integration so that a minimal amount of information needs to be manually keyed into different software.
- Performing audits in real-time to eliminate the problem of discovering an error too late in the process for it to be rectified without incurring high costs.
- Automating repetitive and recurring menial tasks so professional coders can focus on resolving complex issues that require their skills and expertise.
- Connecting unstructured data and extracting relevant information from various EHRs within a healthcare organization so that integrated patient statements can be generated to streamline the payment process and encourage patients to pay their bills faster.
- Reducing operation costs associated with conducting repetitive analyses and billing procedures manually.
- Reducing the number of customer support queries or disputes related to patient statements to lower operating costs.
AI-driven technologies have the capability to self-improve as they perform more tasks. The system will become more sophisticated over time and continue to narrow margins of error.
Not to mention, more accurate and efficient billing will improve the patient experience by ensuring that patients are not overcharged and providing them with access to their medical information in an organized and consolidated format.
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