GenHealth.ai has been recognized on the Gartner Hype Cycle for Healthcare Data, Analytics, and AI, 2024. This highlights our commitment to pushing the boundaries of what’s possible through the use of generative AI and a deep understanding of industry needs.
GenHealth now has the ability to create the most accurate synthetic patient histories for patients with any combination of conditions, procedures, demographics, medications, etc.
Generative AI based analytics for healthcare can save a tremendous amount of time lowering the time from question to answer to seconds instead of weeks.
Mastering cost prediction is vital for healthcare. GenHealth.ai’s model achieves an 84.5% Normalized Mean Average Error, surpassing industry standards. Detailed analysis ensures accurate, fair predictions, helping healthcare organizations optimize resources and improve patient care.
We compare state regulations on prior authorization (PA) timeframes and requirements, highlighting differences in standard and urgent requests, electronic submission mandates, and Gold Card exemptions
Using GenHealth’s G-Mode application, health plans and providers can better predict and take action on quality gaps to improve patient outcomes and increase revenue
GenHealth launches the future of prior auth automation with an AI chatbot that understands medical policies and rules from health insurance companies across the US.
GenHealth.ai has developed the world's first prior authorization and medical policy form database, aiming to streamline the complex and burdensome processes in healthcare such as claims adjudication and prior authorization utilizing advanced AI and ETL processes
The proposed rule aims to streamline prior authorization in healthcare by integrating technical and operational changes to enhance health information exchange, reporting, and interoperability.
With generative AI based analytic models, there are many open questions in the industry about efficacy performance, and use cases. This space is just starting to be explored.
It is not an either / or, instead LLMs and LMMs can work together. Both have short comings and optimal use cases. Using them together gets you the best of both worlds.
Introducing the GenHealth Embeddings API: Identify relationships for any medical concept like similar episodes of care, patient matches, providers search, etc
Learn more about our expertise in Healthcare policy. From working with the health systems which are regulated to the people that write the regs, GenHealth is deep in the industry.
We are starting with a Beta product to get early feedback from researchers, industry, doctors, and app developers. Send a note to hello@genhealth.ai to get access!
Reinforcement learning with human feedback (RLHF) is a powerful technique in LLMs. However, RHLF can be counter productive in the case of large medical model (LMM) transformers trained on raw medical event data
The field of AI has made significant advancements in recent years, and the most talked-about models are LLMs, but there are now healthcare specific approaches that leverage transformers.