Did you know that healthcare providers spend an average of $48 to rework a denied claim for Medicare Advantage patients? As the volume of data in the healthcare sector continues to explode, the potential for mistakes in medical billing is a major risk. With AI emerging as a potential solution, this article explores whether this technology is a game-changer or poses more risks than rewards.
Challenges of Manual Medical Billing
While they work hard to offer excellent care, many healthcare providers face challenges with manual billing, coding, and documentation in order to submit accurate claims. Medical billing, when done manually, can be complex, laborious, and prone to mistakes. AI is playing a vital role in relieving these issues.
According to RBC Capital Markets, healthcare is producing the largest amount of data globally due to the vast amount of imaging and EMR information generated by each patient annually. This has significantly increased the complexity of medical revenue cycle management (RCM). Physician offices that are overwhelmed are likely to make manual errors, such as incorrect data entry or mismatched codes. This results in claims being rejected and denied, requiring time-consuming revisions, and having a negative effect on revenue. Below are some stats that back this up:
- According to a study by the Kaiser Family Foundation (KFF) on Affordable Care Act (ACA) plans, 17% of claims were denied by insurers in 2021, even if patients saw in-network providers. In 2020, one insurance provider denied 49% of claims, while another had an even higher denial rate of 80%. The main cause of these elevated denial rates seems to be the reliance on manual processing of claims (Forbes).
- In one survey, 58.6% of participants stated that insurance follow-up was the third most time-consuming activity, following denials management and prior authorization (Tech Target).
- Close to 15 percent of healthcare claims sent to private insurers for reimbursement are initially denied according to a new national survey of hospitals, health systems and post-acute care providers. The survey found that respondents had to conduct an average of three rounds of reviews with insurers for each denied claim. Each review cycle took between 45 and 60 days (Premier, Inc.).
Many factors combine to make manual medical billing slow, error-prone, and highly labor-intensive. These include complex codes, frequent regulatory changes, varying insurer requirements, time-consuming follow-ups and denial management.
Enter AI, and suddenly, medical billing tasks are no longer a tedious, time-consuming burden. AI automates and optimizes the entire claims process, from verifications and billing to claim submission, payments, and administrative tasks.
AI – A Game Changer for Medical Billing
Reworking denied claims is a costly affair and significantly impacts a provider’s bottom line. The Healthcare Financial Management Association (HFMA) estimated that the average cost to rework a denied claim is approximately $48 for Medicare Advantage and $64 for commercial plans. AI streamlines medical billing and improves these challenges in several ways:
- Automates repetitive billing tasks: AI automates repetitive medical billing tasks such as data entry, claim submissions, and payment posting. For example, by extracting and inputting patient and insurance information from various documents, AI reduces manual data entry and potential for human error. This frees up billing staff to focus on other tasks while improving claim processing efficiency.
- Enables real-time eligibility verification: When done manually, verifying health insurance coverage for each patient that comes to the office is a laborious and time-consuming task. AI tools can quickly verify patient insurance eligibility, improving the speed and accuracy of billing.
- Drives accurate and quick claims processing: AI technologies, such as Optical Character Recognition (OCR) and Natural Language Processing (NLP), play a crucial role in supporting fast and accurate claim filing. They can scrub claims before submission. AI algorithms identify and flag potential errors in billing codes or patient data, minimizing the risk of claim denials due to inaccuracies. This makes it much easier to submit claims for multiple patients.
- Performs predictive analytics: Healthcare providers can leverage AI for denial prediction. By using AI to analyze historical data on denials and appeals, they can detect patterns that suggest a higher chance of denial. This will allow them to address these issues before claim submission, reducing the risk of denials. Moreover, predictive analytics supports budget planning and resource allocation, helping providers make more informed decisions.
- Identifies potential fraud: Machine learning algorithms can analyze historical claims data to detect suspicious patterns or anomalies that may indicate fraudulent activities. This enables insurance companies to distinguish between legitimate and illegitimate claims, and deny fraudulent submissions.
- Optimizes patient payments: By leveraging AI, healthcare organizations can align RCM and payment integrity. AI offers a way to provide patient-centric financial experiences by creating transparent and flexible billing solutions. Chatbots address billing queries and remind patients of payments. By reducing claim processing time and denial rates, automated billing reduces wait times for claim resolution, benefiting both providers and patients.
AI has undoubtedly introduced several innovative applications that are transforming traditional billing practices. There are numerous examples of hospitals and healthcare providers that have reported high satisfaction following the automation of their billing processes. The American Hospital Association (AHA) reported on a community healthcare network in Fresno, California that uses an AI tool to review claims and flag those likely to be denied based on historical payment data and payer rules. The tool targets two main denial reasons: lack of insurance prior authorization and non-covered services. Since its deployment, the system has reduced prior-authorization denials by 22% and non-covered services denials by 18%, without adding RCM staff. The health system estimated that it saves 30-35 hours weekly by reducing the need for back-end appeals.
However, the utilization of AI in medical billing is not without shortcomings.
Potential Pitfalls of Automating Medical Billing and Coding
Automating medical billing and coding offers efficiency and cost savings, but it also comes with challenges.
Limitations in handling complex claims: While AI is great for processing large amounts of data and can effectively handle straightforward, routine claims, there are concerns about its ability to accurately code for more complex medical cases. For example, if a patient has multiple conditions, the coder must determine which condition is the primary diagnosis and how to code for complications. AI might not grasp the subtleties between these related conditions or fully recognize how to apply coding guidelines in such complex cases. This could potentially result in incorrect coding or missed billing opportunities that could affect reimbursement. Human oversight is crucial to ensure accuracy in complex claims.
Need for expertise and interpretation in medical coding: Medical coding demands a deep understanding of medical terminology, anatomy, physiology, and the latest coding guidelines and regulations. Accurately translating medical records into billing codes often requires interpretation. AI may find this a challenge as it lacks the ability to make nuanced judgments.
Rich, detailed data needed for accuracy: For an AI prediction model to be truly accurate and useful, the data used to train it must be incredibly detailed and capture subtle nuances, rather than just basic information. Simply providing a large amount of data isn’t enough; the data needs to be rich with specific details and complexities to accurately reflect the real-world scenario the AI model is trying to predict. For instance, to assign the correct diagnosis and treatment codes, the data for training AI must include detailed medical history, lab results, imaging scans, and other relevant nuances beyond just symptoms, not just a basic list of diagnoses. Without accurate, in-depth data, AI can make mistakes, leading to incorrect conclusions that can affect patient care as well as medical billing.
Evolving billing regulations: Billing regulations are frequently updated to reflect new healthcare policies, coding standards, and payer requirements. As a result, AI programs that rely on predefined data structures or schemas to process claims will need continuous updates to stay aligned with these changes. Without regular updates, the AI may use outdated rules, leading to errors in claim submissions, incorrect coding, or missed revenue opportunities. This need for ongoing adjustments can be a challenge in maintaining the AI’s accuracy and effectiveness in medical billing.
Patient privacy concerns: The use of AI in handling medical records containing protected health information (PHI) raises significant ethical and legal concerns. These concerns arise from the risk of potential for data breaches, unauthorized access, and the misuse of sensitive patient information, which can compromise patient privacy and trust. Moreover, there are legal consequences associated with failing to comply with regulations such as HIPAA, which mandates stringent safeguards for the handling of PHI. Ensuring that AI systems operate within ethical guidelines and legal frameworks is crucial to maintaining patient confidentiality and upholding the integrity of healthcare practices.
Without careful implementation, automation can lead to errors, compliance risks, and missed revenue opportunities, especially in complex cases requiring human expertise.
Integrating AI in Billing: Proceed with Caution
It’s evident that integrating AI and automation into patient billing and revenue management offers several benefits. However, healthcare providers need to recognize that these technologies are meant to complement, not replace, human expertise. Automation is only one aspect of the medical billing solution; even the most advanced technology provides positive results only when it is guided by knowledgeable professionals who understand how to implement it effectively.
Medical billing, coding, and revenue management still require human skills to oversee processes and make strategic decisions informed by AI-driven insights. Hospitals and healthcare practices looking to leverage automation to enhance their billing efforts should collaborate with an AI programming expert and a medical coding specialist to train AI effectively. Additionally, having a human review AI-processed information can help mitigate liability and reduce the risk of errors.
AI is here to stay. Healthcare providers should explore AI solutions while remaining mindful of the challenges. The winning combination of human intelligence and advanced technology can enhance operations, improve patient engagement and satisfaction, and increase overall efficiency in healthcare billing and revenue management.
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