We recently wrapped up our latest Hackathon –– an event that brought together Oscar employees from various teams to brainstorm new AI use cases and explore how our tech can enhance the healthcare experience for our members, providers, and brokers.

Research on AI is moving unpredictably fast. Though the use of AI holds great potential in the healthcare space to improve affordability, efficiencies and even outcomes, the stakes are inherently higher in use cases that involve patient care.

Yesterday, OpenAI dropped a preview of a new series of generative models focused on reasoning, dubbed “OpenAI o1.” Our initial observations are that the “o1-preview” model is more autonomous in generating a set of steps to solve a problem, more precise, and able to handle more complex tasks with higher consistency in output.

The healthcare industry is too complex, costly, and cumbersome. Since our inception, we have been at the forefront of using innovative technologies and leveraging our tech stack to simplify the insurance process and improve member outcomes.

Our team recently worked with OpenAI on a case study analyzing Oscar's use of AI across our business — and our opportunity to improve healthcare. We found success within the following use cases…

Part 2 of 3

Our providers leverage a repository of guidelines, varying by state, that outline conditions under which a service will be covered. These guidelines are specific to a member’s policy and plan type, and it can be a lot of information for providers to sift through. Our team is streamlining this process by teaching GPT to help clinicians filter through paperwork, allowing them more time to care for patients.

Part 1 of 3

We are constantly thinking about how to make the workflows of clinicians less tedious, so they can do what they do best: serve patients and provide care. In this next series of posts, we will share our learnings from a different application with the same goal: improving clinician workflows to deliver faster, better care for our members.

In the first step of our ‘find care’ pipeline, Oscar has an omni-search bar that’s able to distinguish between reasons for visit, providers, facilities, and drugs. The bar uses lexicographic (i.e., close in dictionary order) techniques to find autocomplete matches from each group type and a rules-based model decides which results from each group should be surfaced.

Language models are awesome and all, but my favorite research papers are those that show where they fail. It's easier to understand hard limits than soft capabilities. Here are four recent papers with good examples for the limits of current LLMs.