The technology, which has the potential to reduce costs and improve care, should be embraced by payers and providers alike.

With healthcare costs continuing to rise, patients, payers, providers, and pharmaceutical companies are all looking for ways to improve the affordability and accessibility of services. The timing couldn’t be better for the arrival of a technology superpower that has the potential to drive down costs and improve outcomes. I refer, of course, to generative AI.

While it’s early in the game, I find it fascinating to explore how AI could potentially bring payers, providers and pharmas closer to patients in order to improve the affordability and accessibility of healthcare services. The use cases we’ll look at could improve affordability by leaps and bounds across the healthcare ecosystem.

Flying colors

AI models have yet to ask patients to stick out their tongue and say “Aah,” but otherwise, they are developing an enviable track record when it comes to behaving like physicians. Generative AI models are reportedly now able to pass a key U.S. Medical Licensing Examination practice test. The latest version of Google’s Med-PaLM is said to be achieving  85% accuracy, and GPT-4 claimed to be answering the questions correctly more than 90% of the time.  Imagine a dedicated personal health secretary—an AI-powered avatar traveling as one’s best buddy—assigned to every patient, possessing comprehensive knowledge of their entire medical history. This secretary would consistently monitor the patient’s vital signs and deliver proactive, value-based care services. The realization of this concept is imminent, not too far in the future.

Putting the tech to work

There are plenty of use cases that demonstrate how AI is making healthcare more accessible and affordable:

• Fraud prevention. According to the U.S. Department of Justice, more than $100 billion is lost to healthcare fraud, waste and abuseannually.  By analyzing large datasets, AI can identify patterns indicating fraudulent activity, such as unusual claims or account access.
• Claims analysis. AI can also analyze medical claims data and identify areas in which costs can be reduced without compromising quality of care. For example, it can identify cost-saving opportunities in pharmaceutical spending or in the use of medical equipment.
• Provider rankings. AI can analyze performance data and identify the providers that offer the best value for money.
• Predictive care. AI can analyze data from various sources such as medical records, demographic information and claims history to predict the likelihood of future health events, thereby helping with risk assessments.

The generative turbocharger

While AI is already having a major impact on healthcare, generative versions have the potential to act as a turbocharger.

By analyzing patient information from health records and real-time data from wearable devices, generative AI could help drive clinical decision support and patient monitoring to the next level. The same set of analyzed details would be a game changer in supporting providers with personalized drug discovery.

And if you combine the power of AI with digital twins, the possibilities for pharmas are enormous in that sector’s drug formulation and discovery process. These advances will lead to huge savings that greatly reduce drug costs.

For their part, payers will gain the ability to assess risk for insurance underwriting purposes by analyzing data from various sources (medical records, demographic information, claims history, etc.) to predict the likelihood of future health events.

In nutshell, generative AI has the potential to transform healthcare by improving patient outcomes, reducing costs, and enabling more personalized and affordable care.