Predicting Better Healthcare

It’s no secret that the U.S. healthcare system is broken. Americans spend $4 trillion on healthcare annually, but the U.S. has some of the worst health outcomes in the Western world. Better data and analytics can overcome some of these challenges.?

A McKinsey report estimated that a quarter of healthcare costs, equal to about $1 trillion, come from administrative burdens alone. Major healthcare decisions are currently guided by evidence-based medicine, which requires research and manual reports for each individual hypothesis, increasing the cost of identifying solutions.

GenHealth.ai is pioneering a new approach in healthcare with its transformer-based large medical model. Trained on a vast dataset of 140 million patient healthcare claims and financial data, this model offers insurers, providers, and pharmaceutical companies realistic projections of their patients’ future health based on their past. This collaborative approach, where humans and AI work as co-pilots, allows for real-time feedback that the AI can learn and improve from.?

In the near future, the team at GenHealth believes, most major health decisions will be supported by AI—and GenHealth intends to be that AI.

For example, GenHealth can help health insurance providers with risk adjustment, care management, and financial benchmarking. Pharmaceutical companies can use GenHealth to simulate clinical trials and identify potential side effects, diseases, or patient types that require more research. And life insurance can have more personalized actuarial analysis.?

GenHealth’s vision extends beyond the AI model—they are reimagining healthcare by building applications natively on their AI. Leveraging their large medical model, they have built applications that cut 90% of the administrative burden of prior authorizations and a healthcare analytics chatbot that can answer any population health questions of the past, present, and future.