Healthcare providers rely on AI to streamline claims remediation, overcoming fragmentation and rising denials to protect revenue and patient care.
Thanks to Natural Language Processing (NLP), robotic process automation (RPA), and predictive analytics, artificial intelligence (AI) has become deeply ingrained in most large insurance carriers’ systems. From improving efficiencies to reducing costs, these tools have allowed insurers to gain powerful insights from their data. No longer a costly, challenging, and largely inaccessible tool, the rise of LLMs, the growing AI workforce, and the normalization of AI in everyday life, has enabled them to leverage AI with greater ease and efficiency. But for the healthcare providers on the other side of the healthcare reimbursement ecosystem, adopt AI in their claims processing has been a slow. But why?
Historically, the significant investment in technology, infrastructure, and skilled personnel made AI adoption prohibitive. Unlike big insurance carriers who had the financial resources and scale needed to absorb these large upfront costs, healthcare providers, particularly those in small and mid-sized practices and hospitals, lacked the technical expertise and/or financial backing needed to invest in these technologies. Worse, their existing landscape and the fact that AI requires data integration, staff training, and ongoing system maintenance in addition to software itself created challenges that healthcare providers were not able to address.
Another reason the insurance industry was so successful in leveraging AI is that it’s dominated by a few large carriers which makes the ecosystem pretty straight forward. For healthcare providers, it is the exact opposite. Hospitals, clinics, and private practices across the country are extremely fragmented and use a wide array of electronic health record (EHR) systems and billing platforms which makes it difficult to deploy standardized AI solutions at scale. What works for one provider may not be compatible with another’s. The lack of standardization among systems and workflows slows innovation and increases the complexity of integrating AI into everyday operations.
In addition, while insurers benefit from AI by analyzing massive datasets to predict claim validity or identify fraudulent activity, providers typically do not have access to the same breadth of information. Without a full view of payer behavior and claim outcomes, providers are unable to train AI models effectively. They are essentially operating with incomplete information, which limits the potential power of AI to improve their processes.
The Rise of AI in Health Insurance
With claim denial rates rising, healthcare providers and hospitals can no longer wait when it comes to claims remediation. It’s a major challenge and financial strain marked by rising claim denials, widespread data errors, and wasted resources. According to a recent survey, almost half (41%) of providers had a claim denial rate of 10% or higher, continuing an upward trend from 30% in 2022. The same study found that even though 70% of these denied claims are ultimately overturned and paid, it is only after the providers have spent substantial time, resources and costs in potentially unnecessary administrative expenses.
LLMs such as ChatGPT and Claude, have dramatically changed this landscape. Today, AI is easier to use and more effective, providing healthcare professionals with advanced ways to manage administrative burdens like insurance claim denials. These models are pre-trained on massive amounts of data and can understand and generate human-like text. Healthcare organizations no longer need to train models from scratch as they can leverage existing models that already “know” how to process, interpret, and interact with medical and insurance-related language. LLMs are improving at unprecedented rates. Also, with advancements in machine learning and natural language processing, newer models are becoming smarter, more reliable, and more adaptable with each release. These improvements enable AI tools to handle increasingly complex tasks, such as identifying errors in claim documentation, predicting claim outcomes, and even generating appeal letters automatically.
Another factor driving this change is the rapid growth of the AI workforce. There are now more AI engineers, data scientists, and machine learning specialists than ever before. This expanding talent pool makes it easier for healthcare providers to find the expertise they need to integrate and customize AI solutions. In parallel, the rise of no-code and low-code platforms means that even non-technical staff can deploy AI tools with minimal training.
Society as a whole is becoming more familiar and comfortable with AI. What was once viewed as a futuristic, AI has become mainstream. From virtual assistants to AI-powered chatbots, people use AI regularly and now it is being applied in healthcare. This increased comfort level makes the transition to AI-driven processes, like insurance claim denial remediation, smoother and more widely accepted by both staff and patients.
But thanks to the rise of LLMs, the growing AI workforce, and the normalization of AI in everyday life, healthcare organizations are taking a cue from insurance providers and are adopting artificial intelligence in an effort to streamline operations. This transformation marks a significant step forward by improving revenue and efficiencies and, more importantly, by improving patient care.
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