In the realm of healthcare operations, if you were to ask any medical professional or office manager about the most challenging aspect, chances are they’d all point to medical claim processing.

The Medical claims don’t always come in the standard format. It has its own complications. Unstructured documents, documentation errors, missing information, and so on. Processing such complex information manually can be a challenging task for medical executives. This age-old problem has begun to disappear. All Credits to Artificial intelligence and machine learning (AI/ML) solutions.

Artificial intelligence and machine learning (AI/ML) tools are revolutionizing medical claims processing by automating and streamlining the entire operations. This transformation reduces the risk of errors, minimizes claim denials, speeds up payments, and significantly improves overall efficiency.

The Problems of Medical Claim Processing

Insurance eligibility verification: A majority of renowned care providers accept patients’ health insurance from multiple payors. As claim forms differ from one insurance provider to another, it becomes challenging for medical office executives to efficiently process a sea of claim forms, day after day.

The complexity of medical claims: Medical claims forms are intricate, and the claims processing itself is a tedious and repetitive task. Medical executives often spend a considerable amount of time manually extracting data before they can even begin analyzing the claims, which inevitably leads to errors and inefficiencies.

Medical Coding and Billing Errors

As per the survey report by the Centers for Medicare and Medicaid Services (CMS), in-network insurers participating in healthcare.gov (ACA) denied 17% of medical claims. Medical coding and billing errors topped the list as the main reason for these claim denials.

A major problem leading to claim denials is medical coding and billing errors. While most claims are perfect and error-free, a portion requires scrutiny for inaccuracies and fraud. Among the reasons for claim denials, medical coding and billing errors top the list.

The most common billing errors include submitting coding errors, duplicate claims, missing filing deadlines, using incorrect insurance ID numbers, and providing services that do not match the diagnosis.

 

Solution

By adopting AI in medical claims processing, healthcare providers and insurance companies can optimize their operations, achieve cost-efficiency, and stay competitive in the rapidly evolving healthcare industry. AI/ML consultancies can assist organizations of all sizes in implementing customized solutions, ensuring a successful and tech-driven future for their businesses.

According to Change Healthcare’s 2020 Revenue Cycle Denials Index, medical claim denials have been on the rise, increasing by 23% since 2016. Half of all denials are attributed to billing issues such as registration and eligibility errors, unauthorized insurance claims, and claims for services not covered by the patient’s plan. Shockingly, 86% of these denials are potentially avoidable, yet very few denied claims are ever appealed. As a result, patients face out-of-pocket costs, and medical practitioners lose out on revenues.

Fixing and resubmitting denied claims can be costly for medical practices, diverting time and resources from processing new claims and other critical tasks. In 2021, of the over 48 million in-network claims denied, less than two-tenths of one percent (90,599 claims) were appealed, and insurers upheld 59% of those denials.

Advanced AI-powered solutions for medical claims processing aim to significantly reduce these costly coding and billing errors, thereby lowering the rate of claim denials and ensuring that medical practitioners receive timely compensation for their services.

AI-assistant for Medical Claims Processing

To tackle the growing challenges of medical claims processing, transformative AI-powered solutions have emerged, promising to reduce coding errors, enhance operational efficiency, and expedite payment.

1. 1. Intelligent Document Processing (IDP): IDP solutions are specifically designed to extract crucial data from complex and unstructured documents like insurance claim forms. Handling irregular and complicated forms manually can lead to decreased operational efficiency, increased error rates, and wastage of time and money. IDP solutions excel at extracting and organizing essential data from unstructured documents, bringing order to chaos.

1. 1. Automated Data Entry: Manual data transfer from paper documents to electronic databases is a tedious process prone to human error. AIML-driven automated data entry, powered by optical character recognition (OCR) technology and/or Natural Language Processing (NLP), provides a robust solution for processing and interpreting medical claim forms. This automation speeds up claims processing, reduces errors, and enables verification of eligibility and coverage.

1. 1. Automated Medical Coding: Medical claims submission forms necessitate coding for specific medical services and procedures, a time-consuming and error-prone manual task that often leads to delayed payments for practitioners. Automated AI coding systems such as MEDICODIO analyze medical records and automatically assign codes to services and procedures, increasing speed and accuracy while reducing the workload of medical coders.

1. 1. Denial Prediction: Claim denials and appeals are both costly and time-consuming, requiring substantial manual intervention. AI models can be trained on historical data to identify patterns in new claims that indicate a high risk of denial. By flagging such claims for review and correction before submission, the risk of denial can be significantly reduced.

1. 1. Eligibility Verification: To receive full payment for services rendered, medical practices must verify patients’ insurance coverage. Automated verification systems, utilizing NLP and Natural Language Understanding (NLU), can understand key information in insurance eligibility requirements to ensure patients are fully covered before receiving treatment.

1. 1. Fraud Detection: Healthcare fraud caused median losses exceeding $1 billion in 2021, as reported by the United States Sentencing Commission (USCC). Fraudulent claims range from billing for fictitious procedures to overcharging for rendered services, often requiring manual review for detection. Machine learning algorithms can be trained on historical data to identify fraudulent patterns and flag suspicious claims for further investigation.

Streamlining Medical Claims Processes with AI: As the healthcare and medical insurance industries embrace the accuracy and cost-efficiency of AI solutions, the adoption of AI in medical claims processing is rapidly advancing. Organizations of all sizes can now benefit from customized AIML solutions, scaling their businesses, improving operational efficiency, and remaining competitive in the tech-driven healthcare landscape. Utilizing these services puts you on the fast track to success with AI.

Meet Codio, AI-powered Medical Coding Assistant

Discover the transformative capabilities of CODIO, an advanced Software as a Service (SaaS) tool designed to streamline your medical coding workflow. By harnessing the power of AI, CODIO not only automates but also accelerates the medical coding process, resulting in a significant increase in revenue.

Say goodbye to inconsistencies that often arise from interpreting data in Electronic Health Records (EHR). CODIO acts as your intelligent assistant, utilizing API and Robotic Process Automation (RPA) to extract patient data from any EHR, EMR, or Physician Notes. It then suggests the most appropriate codes for the extracted information. To ensure precision, these codes are further validated by professional medical coders within your organization before integration into your billing systems. With CODIO, you can experience the future of efficient and effective medical coding.