How AI for Medical Coding Can Help Healthcare Practices Boost their Revenue

Medical codes are a different language to a layperson, and they are. Clinicians and revenue cycle staff convert clinical encounters into billable codes in the highly complex medical coding process used for performance tracking and reimbursement. The codes that eventually lead to claims describe a patient’s experience.

Being able to comprehend a clinical encounter is crucial for both patient care and keeping the doors open. Medical coding is more complex than ever in a world where payer regulations and documentation standards constantly change.

The healthcare industry has become increasingly complex in recent years, and the variety of treatments and procedures is expanding at a rapid rate. As a result, medical billing and coding have undergone numerous changes.

AI-Enabled Medical Coding

To guarantee accurate and effective revenue cycle management, billing and coding, healthcare organizations must overcome numerous technical and administrative challenges:

• The complexity of the more than 70,000 billable codes significantly raises the need for medical coders. The high demand for qualified workers who can accurately and quickly translate EHR (electronic health records) data into codes needs to be met.
• It takes a lot of time and is prone to mistakes for coders to manually match each medical visit and procedure with one of the more than 70,000 available codes.
• The data must then be keyed into various systems for various functions, including accounting and creating patient statements, which is yet another laborious and error-prone process.
• Typically, audits happen near the conclusion of a revenue cycle. Even if errors are found, it’s frequently too late to fix them because doing so typically costs more than the original errors did.
• The complicated and drawn-out manual coding procedure cannot be scaled. Costly errors plague many organizations due to the scarcity of skilled coders and the complexity and workload that always seem to be around.
• The Centers for Medicare & Medicaid Services (CMS) reports that improper payments totaling $36.21 billion were made in FY2017 due to medical billing errors.
• The Covid-19 pandemic altered the healthcare environment in several ways, including hastening employee burnout, transforming healthcare revenue cycle management (RCM) and constricting resources.

Role of Artificial Intelligence in Healthcare

The Covid-19 financial and psychological burden has drawn much attention to AI technology in recent years to prevent care team exhaustion and lower operating costs. Academics are developing algorithms that use historical data to predict the likelihood of a claim being denied. Natural language processing (NLP) can, for instance, automatically translate physician notes into billable medical codes.

• Robotic Surgery with Artificial Intelligence Assistance

Using information from medical records to guide surgeons during the procedure with the help of AI.

• Bots that Resemble Nurses

Natural language interpretation to respond to simple questions, handle requests and alert the appropriate staff member to address patient issues.

• Making Diagnoses Using AI

The method of examining symptoms and producing objective diagnostic options for professionals in medicine.

• Medical Billing and Coding that is Automated

AI could aid in automated medical coding and billing and automatically reviewing medical records.

Transforming Healthcare Revenue Cycle Management (RCM) With Artificial Intelligence 

There are many areas of healthcare where artificial intelligence is being used, including disease diagnosis, primary care, electronic health records, and telehealth. However, applying AI to RCM can be a game-changer, assisting healthcare organizations to enhance patient satisfaction, increase collections, boost compliance, and lighten the workload of their RCM team members.

 A recent poll of healthcare executives reveals:

• Up to 75% of healthcare executives are implementing AI strategies or planning to do so.
• While 43% claim that automating business operations like revenue cycle management functions will be their first focus to cut costs.

The different rules governing charges and reimbursements result in frequently complicated business processes in the healthcare industry. Additionally, this situation is ideal for using artificial intelligence. In manual transaction processing environments, AI can closely mimic most human actions by using historical behavior as guidance. AI can boost sales and decrease denials by imitating the patterns of success it notices in historical data.

Uses of Artificial Intelligence Medical Billing and Coding

Healthcare organizations can streamline their billing processes while reducing expensive errors using AI medical billing and coding. Machine learning and natural language processing (NLP), two AI-driven technologies, can quickly and accurately interpret and organize a large amount of data. As a result, they’re ideal methods for finding and extracting information from an EHR, then matching it with pertinent medical codes.

Additionally, by examining data from various sources and making connections, these technologies can contextualize unstructured data. To make sense of multiple events, diagnoses, and procedures, for instance, an AI medical billing program can arrange data from various records into a logical timeline, reducing coding and reporting errors.

Pros of Using Artificial Intelligence in Medical Billing and Coding

Due to their numerous advantages, healthcare organizations will adopt AI-driven technologies to increase the effectiveness of their billing and coding procedures.

• By automating the coding process, a scalable solution that is less reliant on the availability of qualified billing specialists can be delivered.
• Enhancing cash flow by ensuring accurate patient statements are sent out on time.
• Admin staff should work fewer hours per day on average, which will enhance other aspects of their work.
• Real-time auditing is done to avoid the issue of errors being found too late in the process to be fixed without incurring high costs.
• Automating time-consuming, repetitive tasks so expert coders can concentrate on solving complex problems that require their knowledge and experience.
• A healthcare organization’s integration of unstructured data and extraction of pertinent data from various EHRs to produce integrated patient statements that will speed up the payment process, encourage patients to pay their bills on time and enhance patient experience management.
• It lower operational costs related to manually performing repetitive analyses and billing processes.

Takeaway 

The biggest issue facing AI in healthcare is not whether the technology will be effective but how to ensure its adoption in routine clinical practice. Clinicians may eventually gravitate toward jobs that call for special human abilities and the highest level of cognitive function. The only healthcare providers who may not benefit fully from AI in healthcare are those who choose not to cooperate with it. The only healthcare providers who may benefit partially from AI in healthcare are those who choose not to cooperate with it. Over time, the system will advance, and its error margins will get smaller.

Not to mention that more accurate and effective billing will enhance the patient experience by preventing overcharging and giving patients access to their medical records in a consolidated and organized manner.