Mike Deon, CEO of Anomaly, discussed the company’s initiative to use AI to reduce healthcare costs by addressing hidden expenses related to billing and insurance, which significantly burden consumers. He emphasized the importance of improving transparency in the healthcare system and highlighted the potential of integrating large language models to enhance data processing and decision-making.
In a recent discussion, Mike Deon, the CEO of Anomaly, shared insights on the company’s efforts to leverage artificial intelligence (AI) to reduce healthcare costs. He noted that traditional methods, such as outsourcing and offshoring, have reached their limits, prompting a shift towards technology-driven solutions. Deon emphasized that the healthcare industry is recognizing the need for innovation to address rising costs effectively.
Anomaly is focusing on the hidden costs within the healthcare system, particularly the billing and insurance-related expenses that consumers often cannot see. Deon highlighted that these costs, which include the complex interactions between providers and insurers, significantly impact patients when they receive bills for services. He pointed out that the current healthcare billing system is opaque, making it difficult for consumers to understand the true costs of their care.
The financial burden of healthcare is substantial, with billing and insurance-related costs amounting to approximately $500 billion annually, of which $200 billion is spent on processing claims alone. Deon illustrated the challenge faced by consumers, noting that many people do not have the financial means to cover unexpected medical expenses, such as a $20,000 bill for a procedure. This situation is exacerbated by the lack of transparency and information available to patients when they seek care.
Deon explained that the healthcare industry’s complexity has resulted in a lack of clarity for both providers and insurers regarding costs and billing. He believes that AI can play a crucial role in bringing together disparate data sets, such as diagnosis codes, procedure codes, pricing data, and contracts, to facilitate better decision-making. This integration of data can help create a clearer picture of costs and improve transparency in the healthcare system.
Looking ahead, Deon mentioned the potential for incorporating large language models (LLMs) into their processes to enhance data processing and provide human-readable answers. While LLMs are not the sole solution, they can complement the AI efforts by making complex data more accessible. Deon expressed optimism about the future of AI in healthcare, particularly in reducing costs and improving the overall patient experience.