GenHealth.ai Unveils Large Medical Model, Surpassing Industry Benchmarks by Over 14% in Healthcare Cost and Risk Prediction

Revolutionizing Healthcare with Generative AI: A New Standard for Precision in Patient Care Forecasting

BOSTON, MA / ACCESSWIRE / September 24, 2024 / In a significant breakthrough in healthcare AI, GenHealth.ai today published a study detailing an advanced generative AI model that decisively outperforms established industry benchmarks by over 14% on healthcare cost and risk prediction. This model challenges traditional approaches by leading firms like Milliman, Cotiviti, and Johns Hopkins, setting a new standard for precision in healthcare prediction while enabling an unprecedented lens into the future of a patient’s healthcare journey.

The paper, “Introducing the Large Medical Model: State of the art healthcare cost and clinical risk prediction” articulates how GenHealth.ai has leveraged a healthcare-native approach to generative AI to achieve superior results. The Large Medical Model (LMM) combines a unique vocabulary tailored specifically for healthcare, the technology of neural network transformers (which underpins Large Language Models), and data from trillions of healthcare events across 140 million patients. The resulting model achieves a similar leap in performance for the healthcare domain akin to the improvement in LLMs over traditional natural language processing approaches.

“We’ve created the Large Medical Model to address a broad range of healthcare applications where LLMs and traditional analytics fall short. Our healthcare specific AI is built from the ground up to support everything from pop health analytics, to automating prior authorizations, and detecting fraud/waste/abuse.” said Ricky Sahu, CEO of GenHealth.ai. “This isn’t just about streamlining administrative tasks – though our model excels there. This is the first spark towards truly personalized patient care. It’s a shift in how healthcare organizations typically use data to find general patterns, to a model where, in seconds, each patient and encounter can have better than human insight that is actionable. It will be equivalent to the impact large language models had on text interaction.”

Highlights of the study include:

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  A New Tokenization Scheme: The paper introduces a new use of generative AI on healthcare specific data and tokens to predict patient futures holistically.

• State of the Art (SOTA) Performance on Cost Prediction: Achieves unprecedented accuracy in predicting patient total cost of care and risk factors, substantially surpassing legacy systems used by current industry leaders.

• SOTA Performance on Chronic Condition Prediction: In addition to predicting total cost of care more accurately, the paper details how the same model is used to predict a wide variety of chronic conditions.

• Impact on Healthcare: By delivering more accurate predictions at an event level detail, the AI model promises to be more actionable and explainable. These features help insights make it from research into practice to reduce wasteful spending and improve patient management.

“Our team brings years of hands-on experience with healthcare claims and clinical data. This deep knowledge has given us unique insights that many might miss, but are actually crucial for achieving such high performance and versatility in our AI model. We’ve been able to fine-tune it for a wide range of applications because we’re used to dealing with a lot of the nuance and complexity that’s present in so much of healthcare data.” added Eric Marriott CTO of GenHealth.ai

The GenHealth.ai team is now building applications on top of the Large Medical Model. Similar to how ChatGPT was the first application that democratized access to large language models, GenHealth has introduced applications on top of its medical model including a co-pilot for population health analytics and prior authorization automation software to make the LMM more accessible.

“I’m so excited about what we’re building and how it can help make life easier for both providers and health plans,” commented Ethan Siegel, COO at GenHealth.ai. “We can use our AI systems to help reduce the amount of time people have to spend on painful, manual processes so they can spend more time making sure patients get the right treatment at the right time.”

For more information, visit GenHealth.ai’s website for solutions and generative AI for healthcare. To access the full research paper, please visit GenHealth’s website.

Contact Information

Mike Maseda
Head of Operations
marketing@genhealth.ai

SOURCE: GenHealth.ai

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