Doctor sitting at desk and writing a prescription for her patient When an individual files a medical insurance claim, they expect swift processing, approval, and treatment from the stakeholders involved. According to a recent KFF study of Affordable Care Act (ACA) 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. The stakeholders just have so much on their plates that they are making errors leading to denials, finding it difficult to process the claims on time, and even engaging in time-consuming reworks impacting the bottom line. While the problem means a dreaded experience for the insured, forcing them to pay out-of-pocket, it is not something that cannot be addressed. Case in point: the role of AI. The recent advances in machine learning, natural language processing, and deep learning can help automate and streamline the entire claims process end to end, leading to more precise and accurate decision-making and better handling of applications The Challenges Affecting Medical Claims Processing In an ecosystem where an ever-increasing number of medical claim applications are being handled manually, a number of challenges can crop up, starting with medical billing errors. Most providers today want to forward claim applications as quickly as possible but this effort could easily lead their staffers to make errors such as filing duplicate claims for the same patient, entering incorrect insurance ID numbers, or providing incomplete patient information. These gaps can directly result in claim denial or delayed reimbursements. 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. How AI Can Be The Saving Grace With
Artificial intelligence in the loop, a number of manual tasks associated with the processing of medical insurance claims can be automated. For instance, both healthcare providers and insurers can tap large language models to handle data entry and document verification bits of the process. This can streamline the workflow while improving efficiency and accuracy at the same time. In another workaround, stakeholders can use AI technologies like optical character recognition (OCR) and natural language processing (NLP) 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. This can help insurance providers distinguish between legitimate and illegitimate claims and deny the latter. But, that’s not all. Along with automating claims processes and helping crack down on fraudsters, AI and machine learning can also help improve approval chances. An ideal example could be the case of denial prediction, where healthcare providers could use AI to analyze historical data on denials and appeals and identify patterns indicative of a higher likelihood of denial. This can enable healthcare staffers to fix the flagged items before submission, ultimately reducing the risk of denials and improving outcomes for both insurers and healthcare providers. Similarly, insurance companies can also deploy AI-powered decision support systems, which can analyze comprehensive claim data to provide relevant recommendations, guidelines, and alerts aimed at ensuring strict adherence to healthcare policies, minimizing errors, and enhancing consistency throughout the claims-processing workflow. Impact On The Entire Healthcare Ecosystem When done right, AI-powered claims processing can impact all stakeholders in the healthcare ecosystem, including: 1. Healthcare providers: Automating and streamlining tasks with AI can reduce administrative burdens and paperwork, allowing providers to allocate more time and resources to patient care. Further, faster and more accurate claims processing can also lead to timely reimbursements, improving cash flow for healthcare providers. 2. Patients: When AI is used and claims are processed faster than ever, the wait times for patients will decline, allowing them to access necessary medical services more quickly. Additionally, timely and accurate claims processing would also reduce the likelihood of denials, ensuring a smoother patient experience with no-to-little financial stress 3. Drug manufacturers: Improved efficiency in claims processing can minimize administrative costs for drug manufacturers and enable smoother interactions with payers. This would allow them to focus more on research and development, potentially leading to the development of innovative and life-saving medications. Ultimately, a more efficient system would contribute to a more effective healthcare marketplace. 4. Payers: As mentioned above, AI can empower payers to make data-driven decisions, leading to more accurate risk assessment, faster claims processing, and optimized resource allocation. This would result in improved financial outcomes and enhanced service delivery, ultimately benefiting both payers and policyholders. Ethical And Legal Considerations While the integration of AI in insurance claims processing offers plenty of benefits, stakeholders must always take into account the ethical and legal concerns associated with the technology. 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. No matter the use case, teams should consider both by establishing clear guidelines, regulations, and ethical frameworks for parties using the technology. The Road Ahead As the adoption of AI in insurance claim processing increases, the stakeholders in the healthcare ecosystem are bound to benefit from faster approvals, reimbursements, and overall turnaround times. AI will enhance accuracy and fraud detection, minimizing false positives and negatives for insurers. Plus, it will create a personalized claims experience for policyholders (with tailored recommendations/guidance), enhancing their satisfaction and loyalty. In the long run, insurance companies are also expected to leverage AI for risk assessment, where they could offer more customized policies and pricing structures based on accurate individual risk profiles. The seamless integration of AI-powered claim processing systems with other healthcare technologies, such as electronic health records, will enable real-time data exchange, faster claim adjudication, and improved coordination of care.