Medical billing is a critical factor in the medical industry and involves submitting and following up on claims. These follow-ups are conducted with health insurance companies so medical practitioners can be reimbursed timeously for their services. In theory, this is a simple process. However, it remains complex as there are multiple considerations, like coding accuracy, compliance, and the law. Advanced data analytics has emerged as a powerful tool that offers efficiency in medical billing. This article explores how advanced data analytics can improve medical billing.
What Is Medical Billing?
Contrary to popular belief, medical billing is not as simple as the following scenario: a patient receives treatment, the patient’s treatment is processed during their appointment, and the patient pays for treatment once received. It is multifaceted and regulated to ensure medical practitioners are reimbursed on time and that patients receive the care they need. Here are some billing steps to consider:
Patient registration: This involves capturing patient details.
Verify insurance: Confirmation of insurance and coverage.
Medical codes: Assigning the correct code to the corresponding treatment. For example, there are specific ABA billing codes when Applied Behaviour Therapy (ABA) therapists treat patients with autism spectrum disorder.
Submitting the claim: Sending the coded information to the medical insurance company.
Review: The insurance company reviews patient details, and either approves or rejects the claim.
Payment posting: Medical practices are sometimes tasked with recording insurance payments and billing patients for any remaining balances, where applicable.
Claim denials: If a claim is denied, the reasons must be identified and explained to the patient.
Final billing: Billing the patient for any procedures or treatments not covered by the insurer.
The Role of Data Analytics in Billing
In the digital age, industries — including the medical field — must use data analytics to improve efficiency, understand trends, and process large data sets. There are sophisticated tools such as machine learning, predictive modeling, and natural language processing that analyze large data sets that enhance several processes. When applied correctly in medical billing, these systems lead to accuracy, efficiency, and better decision-making, and their role within the medical industry cannot be understated. There are five impactful areas to consider:
Data integration and management
Predictive analytics
Fraud detection
Revenue cycle management
Compliance
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