How to Boost Medical Billing Business Using Artificial Intelligence

Overstating the importance of Artificial Intelligence is difficult. When implemented efficiently, AI holds the capacity to boost your billing business tenfold. In many cases, AI is the thing that is scaling the business rather than the physical workforce. The question on many business minds is how does AI change the way business is done?

To help answer this question, we analyzed many billing and coding companies. Below is a summarized version of our findings from the research:

How a Traditional Medical Billing & Coding Process Flows?

Revenue cycle management is dependent on accurate medical coding, but the traditional workflow has major challenges. Modern billing and coding processes present a scary picture: 80% of medical bills have mistakes and a coding-related problem contributes to 42% of claim denials. Such inefficiencies impose a tremendous financial burden on healthcare organizations.

The complexity lies in managing the International Classification of Diseases (ICD-10-CM) system, which contains more than 70,000 active codes that undergo hundreds of updates each year. Billing departments, usually understaffed and overburdened, have to manually blaze their way through this complex coding environment. These often result in costly errors and delayed reimbursement.

The solution is attained through artificial intelligence medical billing technologies. AI medical billing services help healthcare systems perform more accurately with fewer manual interventions and workforce burnout to optimize the revenue cycle.

The traditional billing system involves a lot of manual documentation and paperwork. The paper claim is a time-consuming process where coders enter each code individually in the printed forms. All the paper forms are then passed on to the medical billing organization and later to the payers.In a paper-based setup, the average turnaround time from filing a claim to receiving payments is between 5 to 7 weeks, whereas in automated medical billing systems can be reduced to 2 weeks.

How Will AI Automation Boost the Medical Billing Process?

Today, the ongoing challenge is coding accuracy. To improve the efficiency and efficacy of the billing and coding process, many healthcare companies are finding ways to simplify manual coding labor with AI applications.

The emerging technology in AI is based on Computer-Assisted Coding (CAC), which works on Machine Learning and Natural Language Processing (NLP). The CAC automatically identifies and extracts data from documents and inserts them into the system.

The need of the hour is an automated web-based system that analyzes physician documentation for the text/treatment and automatically recognizes relevant medical codes.

Beyond processing codes and high volumes of data, AI can significantly reduce standard work hours and human error. 

Hospitals in the U.S. incur more than $200 billion a year in administrative costs, 25% of which are used in coding and billing. The increasing number of patients necessitates automated medical coding in healthcare organizations. Medical coding artificial intelligence changes the way things are done. It efficiently extracts and processes data found in the unstructured clinical documentation.

Advance Code Assignment through AI technology

AI for medical coding and billing leverages advanced Natural Language Processing (NLP) and Machine Learning to convert clinical notes to standard medical codes (ICD-10 and CPT codes).  This AI medical coding software analyzes physician notes, treatment records and diagnostic data to make sure that code is assigned correctly.

Key Advantages:

• Great Precision: A Recent study that used healthcare databases shows that BERT-based AI models can attain an 87.08% with top-10 code prediction and 93.76% in top-50 code cases, which is much higher than manual coding.
• Error Reduction: Automated medical coding reduces the dependency on human intervention to a great level and reduces inconsistencies and coding errors that cause claim rejections.
• Improved Productivity: Automation accelerates processing capability, enabling healthcare providers to process more cases at a time and meet the required accuracy level.

Intelligent Fraud Prevention and Accelerated Claims Processing

The number of claims daily is so high that most healthcare practices cannot review claims manually. AI medical billing software development incorporates advanced pattern recognition algorithms to detect billing abnormalities and possible fraud signs that human reviewers may miss. This capability ensures regulatory compliance while accelerating claim processing by automating repetitive verification processes to improve cash flow and eliminate revenue cycle management inefficiencies.

Cost Optimization and Enhanced Security Protocols

Turnover rates in healthcare revenue cycle departments are 11-40%, which is a huge blow to the organization’s revenue streams. AI medical billing solutions eliminate the staffing problem since they decrease the manual assistance needed in daily billing activities. Medical billing assistants have an average income of $55,442 per annum; thus, the investment cost of AI medical billing software solutions can save money in the long run by saving administrative overhead and reallocating resources to more valuable tasks.

The current AI medical billing services have enterprise-level security with high-level encryption, access control and malicious data filtering. This provides the full protection of the sensitive patient and financial data throughout the revenue cycle.

Scalability Across Healthcare Organizations

Whether it is a small practice or a large hospital network, custom AI solutions for medical billing fit organizations and provide consistent results. The American Hospital Association points out the major benefits of scalability:

• Scalability to process vast amounts of data and volume of transactions that can support an organization of any size.
• Less manual efforts allow scaling operations without increasing staffing expenses.
• Adaptive algorithms continuously learn new data patterns and adjust to new coding standards, payer demands and regulatory updates.

What Problems Does Artificial Intelligence Solve?

A natural concern of the popularity of AI applications is the fear, within the industry, that these emerging technologies will shrink the number of jobs available in the medical billing and coding spectrum.

It must be noted, however, that these applications come with the ability to substantially increase the efficiency and speed of human coders to undertake accurate coding but cannot entirely replace human coders. For example, when the coder makes a mistake, the application can immediately point it out with recommendations to rectify the error, and the correction is made as fast as possible. This takes care of the ‘too late’ issues and increases the speed at which the coder works.

Nonetheless, it is worthy of mentioning that this concern can be mitigated by looking at the targeted growth rate of employment within the healthcare sector is at an unprecedented rate over the next decade. According to the Bureau of Labor Statistics projects an 18 percent boost in employment for health information technicians between 2016 and 2026, far above the average growth rate for all other occupations, adding 2.4 million new jobs.

5 Key Impacts of AI on Medical Billing

Industry estimates show healthcare payers will save 13-25% of their administrative costs, 3-12% of their revenue and 5-11% of their medical costs through comprehensive AI medical billing software. 

Better Accuracy

Artificial intelligence in medical coding applies the most advanced machine learning and NLP to examine the whole clinical documentation, such as physician notes, lab results and procedure reports and automatically assign ICD-10 and CPT codes.

Faster Claim Processing 

AI-powered medical billing process automation can provide real-time verification of patient eligibility, code accuracy, and claim formatting. Automated validation systems detect discrepancies before submission, resulting in faster approval times and enhanced cash flow management.

Proactive Denial Management

AI medical billing software development enables predictive denial based on historical billing patterns and risk assessment algorithms. This proactive strategy enables billing departments to resolve possible errors before the claims are filed, saving a significant amount of overhead denial costs.

Cost Reduction 

AI medical billing solutions save enormous man-hours, time, and operational costs as they automate tedious administrative tasks such as verifying eligibility, payment postin,g and reconciling accounts.

Improved Revenue Forecasting 

Predictive analytics models powered by AI deliver advanced revenue forecasting, payment trend analysis, and identify patterns of underpayment or cash flow anomalies, allowing strategic financial planning and revenue optimization.

The Hurdles in the Current System – Our Perspective

The complex nature of medical billing and coding makes it a constant target of errors and sometimes, these can result in a considerably high loss.

This complexity also lends itself toward the requirement of a more significant workforce, where coders are spending more and more time executing menial tasks that could be undertaken swiftly and efficiently by
automated systems of AI technologies.

Considering the current growth of this aspect of healthcare and its expected rise in the U.S., a robust system is the need of the hour.

AI automation is that system, which is poised to address all the pain points that the current system is experiencing, such as inaccurate billing instance, etc.

Considering that the rectification of erroneous billing, when done manually, is a lengthy and complicated procedure that can incur further costs, adoption of AI can automatically point out the errors immediately and mitigate those added costs and time consumption.

Upcoming Trends Of AI in Medical Billing 

Let’s take a look at the upcoming trends of AI medical billing and their impact on healthcare revenue management.

Complete Billing Lifecycle Automation

The future of AI medical billing services will include end-to-end automation covering the spectrum of patient registration to claim submission and payment posting. Future systems will orchestrate whole billing lifecycles, with little human input, allowing smooth processes. AI will automatically retrieve, process, and validate patient data and interact intensively with EHR and billing systems.

Predictive Analytics for Strategic Revenue Management

With advanced analyses of data, AI will transform the sphere of revenue forecasting in healthcare. The systems will forecast future revenue trends, which will be used to make proactive adjustments to operational strategies by healthcare providers. AI will forecast cash flow fluctuations by analyzing historical data, payer behaviors, and seasonal trends. This will enable real-time optimization of collections, staffing, and resource allocation strategies.

Advanced Fraud Detection and Prevention Systems

Custom AI solutions for medical billing will improve fraud detection by detecting abnormal patterns and flagging potential fraudulent activity with a higher success rate than older methods. ML algorithms will sort through billing data to detect possible fraud, such as overbilling trends. AI platforms will give real-time alerts to billing departments for investigation and corrective action.

Enhanced Patient Experience and Billing Transparency

AI will improve billing efficiency and transparency, and create superior patient engagement system experiences. Patients will see real-time and accurate information on billing status, insurance coverage information and out-of-pocket costs to minimize the confusion and increase satisfaction levels. AI will automatically notify patients about balance information, while AI-driven chatbots will help with charge questions, insurance coverage questions and payment options.

Dynamic Revenue Optimization Strategies

The future of AI will lead to the optimization of revenues in the entire healthcare revenue cycle. By utilizing payer trend analysis and optimizing the billing processes, AI will enhance the likelihood of timely payments and decrease claim denials via enhanced medical claims management. AI will find the best billing strategies according to the payer behaviors. This will ensure claim alignment with current requirements while maximizing reimbursements and reducing undercoding errors.

Continuous Learning and Regulatory Adaptation

AI’s machine learning capabilities allow constant adaptation to changing healthcare regulations and payer demands.  As new billing codes or policies emerge, AI systems will automatically adjust to be compliant without manual intervention. AI will maintain current knowledge with medical codes, billing guidelines, and payer rules. It will also continuously learn from ongoing claims to enhance error detection and optimization suggestions.

Comprehensive Healthcare Technology Integration

AI medical billing will attain more integration with healthcare technologies, forming an interrelated ecosystem. AI-powered billing systems will automatically sync with EHRs, pull patient data and submit claims without any human intervention. AI will also integrate with electronic records to facilitate proper billing code usage and accurate submissions. It will also correlate patient care and diagnosis data with billing codes for appropriate charges.

Healthcare Cost Reduction Through AI Innovation

AI will greatly decrease the administrative costs associated with medical billing operations. More advanced systems will detect and diagnose coding errors before submission, which minimizes resubmission needs and reduces administrative burdens. Automated processes will decrease the need to have a large billing team and save on labor costs, enhancing accuracy.  Enhanced submission accuracy will reduce the rejected claims rate and rework requirements, which will bring significant time and cost savings.

The Way Forward

Medical billing and coding is the essential component of how healthcare is delivered and reported in the U.S. Inaccurate coding is a challenge that needs to be addressed with new technology. OSP Labs is working with health technologists to build impacting solutions for medical coding companies.

Schedule a quick online appointment with our AI specialist to dive deep into intelligence healthcare claim management and see some of our innovative AI projects. We analyze your business, understand your pain points and create an AI roadmap to solve your critical challenges.

Considering the current growth of this aspect of healthcare and its expected rise in the U.S., a robust system is the need of the hour.

References