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.

A practical observation on LLMs that is both encouraging and worrisome: it is surprising how much NOTATION matters. Simply how you write something down makes it much easier or harder for a transformer to understand the actual meaning behind it. It’s a little like LLMs are able to read a quantum physics book, but only if it’s written in 18-point Comic Sans to look like a Dr. Seuss book.

Brokers are paid commissions by insurance carriers on a per month basis, but they must follow certain guidelines in order to be eligible, otherwise they will not be paid their commission. If a broker believes that they were incorrectly denied a commission, they can reach out to Oscar to ask for an explanation, a process we call a “commission reconciliation.”

Last year, we expanded our AI functionality to include a new use case: generating initial drafts for providers to document their secure messaging-based visits with patients.

Summarization is considered a top use case for generative AI. Our call quality team ran a side-by-side evaluation, and you’ll see the results from these real calls. The initial findings show that AI performs comparably to our Care Guides in summarizing calls overall, but this performance isn’t evenly distributed.

The Claim Assistant starts by formulating a strategic plan to tackle the problem at hand. This plan is an array of thoughts or subgoals, much like breaking down a large task into smaller, manageable pieces.

Take a look at how user research helped inform our experimentation in two areas: automated messaging with members and clinical documentation for primary care providers.