By Dmitrii Evstiukhin, Director of Managed Services, Provectus
Ask any healthcare practitioner or medical office manager what causes the biggest headaches in their business operations, and you are likely to get the same answer: medical insurance claims processing.
Insurance claim forms come in a variety of sizes and formats, with data requirements ranging from simple to complex. The most complex and unstructured documents must be manually processed, with plenty of back-and-forth communications between medical entities and insurance providers.
Artificial intelligence/machine learning (AIML) solutions are transforming medical claims processes by automating and streamlining operations to reduce the risk of costly human errors, limit claim denials, accelerate payments, and dramatically improve operational efficiency.
Challenges of Medical Insurance Claims Processing
Most established and reputable medical practices accept patients’ health insurance from multiple providers. But lack of uniformity from one insurance provider to the next makes it difficult for medical support staff to efficiently process and submit dozens or even hundreds of claim forms, day after day.
At the other end of the pipeline, insurance companies need to vet and process thousands of claims, submitted from multiple practitioners every day. That means sorting through each claim to verify its accuracy and validity before moving it forward for payment.
Medical claims forms are complex, and claims processing is mundane and redundant work. Insurance adjusters spend much of their time extracting data prior to analyzing claims, and errors and inefficiencies abound.
A data- and analytics-driven approach to claims handling and automation of claims processes with AIML solutions in healthcare can significantly reduce the workload of medical support staff and insurance claims processors, allowing for more efficient and productive workflows at both ends of the medical claims spectrum.
Medical Billing Errors Lead to Claim Denials
While most medical claims are legitimate and straightforward, a certain percent of them need to be scrutinized for inaccuracies and fraud, and a large percentage of those claims are denied for various reasons.
According to data reported by the Centers for Medicare and Medicaid Services (CMS), in-network insurers participating in healthcare.gov (ACA) plans denied 48.3 million out of 291.6 million claims in 2021, a whopping 17% of all filed claims. Among reasons for claim denials, medical billing errors top the list.
Common billing errors that lead to claim denials include:
- Duplicate claim submissions for the same patient and procedure
- Failure to file within the payer’s requisite time frame
- Inaccurate insurance ID numbers
- Services provided do not match the stated diagnosis
According to the Change Healthcare’s 2020 Revenue Cycle Denials Index, medical claim denials are on the rise, up by 23% since 2016. Of all denials, half are due to billing issues such as registration and eligibility errors, unauthorized insurance claims, and claims for services not covered by the patient’s plan. The report asserts that 86% of denials are potentially avoidable, yet denied claims are rarely appealed, resulting in out-of-pocket costs for patients and lost revenues for practitioners,
Correcting and resubmitting claims can be costly for medical practices, taking time away from new claims submission and other critical tasks. Data from healthcare.gov indicates that, of the 48 million+ in-network claims denied in 2021, only 90,599 were appealed, amounting to less than two-tenths of one percent. Of appealed claims, insurers upheld 59% of denials.
Advanced intelligent solutions for medical insurance claims processing are designed to reduce costly billing errors and lower the rate of denials, so that medical practitioners can be quickly compensated for their services.
Customized AI/ML Solutions for Medical Claims Processing
To meet the growing challenges of medical claims processing, transformative AIML solutions are emerging that can reduce errors, enhance operational efficiency, and accelerate payment.
Intelligent Document Processing (IDP)
IDP solutions are designed to extract key data from complex and unstructured documents like insurance claim forms. Irregular and/or complicated forms often require manual processing, decreasing operational efficiency, increasing the risk of errors, and costing time and money. IDP solutions can be used to extract and classify essential data from unstructured documents, creating order out of chaos.
Automated Data Entry
When performed manually, transferring data from paper documents to electronic databases is a tedious process that is highly prone to human error. Automated data entry driven by optical character recognition (OCR) technology and/or Natural Language Processing (NLP) provides a powerful AIML solution for processing and interpreting medical claim forms. Automated data entry speeds up claims processing, reduces errors, and can be used to verify eligibility and coverage.
Automated Medical Coding
Claims submission forms require coding for specific medical services and procedures, a time-consuming and error-intensive manual process that results in delayed payments to practitioners. Automated AIML coding systems are designed to review medical records and automatically assign codes to services and procedures, increasing speed and accuracy, and reducing the workload of medical coders.
Medical insurance claim denials and appeals are costly and time-consuming, and resolving them requires extensive manual intervention. AIML models can be trained to analyze data from past denials and appeals, to identify and flag patterns in new claims that indicate a high risk of denial. Flagged claims can then be preemptively reviewed and corrected by medical staff prior to submission, to reduce the risk of denial.
To receive full payment for services rendered, medical practices must verify that patients are covered under their insurance plans. Automated verification systems use machine learning models trained in NLP and Natural Language Understanding (NLU) to understand key information in insurance eligibility requirements, to verify that patients are fully eligible for insurance coverage, prior to service provision.
In 2021, the median loss for healthcare fraud topped $1 billion, according to the United States Sentencing Commission (USCC). Fraudulent claims range from billing for fraudulent procedures to price gouging for rendered services, and most are detected by manual review processors. Machine learning algorithms can be trained on historical data to identify fraudulent patterns and flag suspicious claims for further review.
Streamline Your Medical Insurance Claims Processes with AI
Adoption of AI in medical claims processing is rapidly advancing as healthcare and medical insurance providers embrace more accurate and cost-efficient solutions to streamline their operations. AI adoption and ML development consultancies offer customized solutions for organizations of all sizes, to help you scale your business, improve operational efficiency and remain competitive in today’s tech-driven healthcare landscape. Utilizing their services, you can be certain that you are on the fast track to achieving success with AIML.