I am an artificial intelligence expert with close to half a decade of experience working with several Fortune 500 companies, such as AbbVie and IQVIA, among others. My professional experience cuts across different segments in AI, such as natural language processing, deep learning, machine learning, business intelligence, and reinforcement learning.
When an individual files a medical insurance claim, they expect swift processing, approval, and treatment from the stakeholders involved. According to aof Affordable Care Act plans, even when patients received care from in-network physicians, the insurers denied 17% of claims in 2021. One insurer denied 49% of claims in the same period, while another’s denials touched an astonishing 80% in 2020. The root cause of these turndowns appears to be the manual approach to handling claims.
In case the billing is not at fault, the involvement of multiple stakeholders – and their own set of requirements, documentation, and regulations can lead to a filing error and denial. There can also be instances where the staffer processing the claim might make an error in data entry, document handling, or verification, leading to a false denial.
In another workaround, stakeholders can use AI technologies like optical character recognition and natural language processing to extract relevant information from unstructured documents, such as medical records and claim forms, and file claims more swiftly and accurately. They can even use machine learning algorithms to analyze historical claims data and identify suspicious patterns or anomalies indicative of fraudulent activities.
The former would mean ensuring transparency in AI algorithms and decision-making processes, maintaining data privacy and security, and addressing biases in AI models that may impact claims outcomes. Meanwhile, the latter would involve compliance with existing regulations, such as data protection and privacy laws, and handling potential liability issues arising from AI-driven decisions.
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