Home PublicationsData Innovators 5 Q’s for John Loser, Chief Risk Officer at Oscar Health

5 Q’s for John Loser, Chief Risk Officer at Oscar Health

by Joshua New
John Loser

The Center for Data Innovation spoke with John Loser, chief risk officer at Oscar Health, a health insurance company based in New York City. Loser discussed how Oscar rewards users for using fitness trackers, as well as how Oscar uses data to taxonomize doctors differently than other insurers.

Joshua New: I’ve seen Oscar described as the “Uber of health insurance.” What does this mean? How is Oscar different than a traditional health insurance company?

John Loser: We often draw comparisons to other big tech companies because Oscar Health was founded as a technology-driven and consumer-focused healthcare company—a radical departure for the healthcare industry as a whole.

What really sets us apart from traditional health insurance companies is how we use data and technology to help our members find quality, affordable care. When someone becomes an Oscar member, their plan includes a personalized Concierge Team comprised of three care guides and a clinical nurse, our free 24/7 telemedicine service Doctor on Call, and a tightly integrated, curated network of first-rate physicians and hospitals. Our doctors, concierge teams, and virtual primary care physicians all have access to Oscar apps that surface patient information and generate machine learning driven alerts around likely health conditions, abnormal test results, and red flags so that they can better care for our members. It’s this kind of smart data feedback loop that we think will really make a difference when it comes to improving health outcomes and finding more affordable care.

New: In 2014, Oscar launched a program that gave customers wearable fitness trackers and rewarded them with cash for meeting physical activity goals. How can you determine how many steps per day each individual person should be getting?

Loser: Our step-tracking program is algorithmically-driven and personalized. We want to help our members stay active, and we recognize that goals will vary for each individual. The upper-bound goal for each person is 10,000 steps, though we’re happy if they want to walk more, because research has shown that 10,000 steps can lead to a decrease in chronic diseases. If someone isn’t in the habit of walking that much every day, though, we want to help our members build to that point at a comfortable rate. Our algorithm moves each member’s step goal up or down consistent with their walking trends. A member’s step goal will increase once they’ve established a pattern of walking more regularly, and will decrease if a member’s step count dips over a period of time. By allowing the step goal to exist on a sliding scale, we are able to inspire incremental and achievable improvements for our members.

New: Earlier this year, Oscar announced that it was cutting the amount of doctors available in its New York City network by half, yet managed to retain customers. What role did data science play here?

Loser: When constructing our networks, we use three sets of criteria to measure their quality. First, doctors and hospitals need to provide a complete set of care. Second, they have to provide high quality care. Finally, they need to be a fair value for our members. With these goals in mind, our network and data science teams use the Network Scorecard, a model they built that acts as a realtime “heatmap” to show us how good our members’ access to care is along those axes. As we constructed our new network, we used these models to ensure we built an optimal network that offered comprehensive access to high quality, affordable care.

Today, we are continuing to evolve these models, tracking against member experiences, claims data, and dozens of other sources to ensure that we stay on top of our continuously evolving network and proactively address any emerging gaps before they impact our members.

New: Oscar has a pretty interesting method for taxonomizing its doctors. Could you explain how it works?

Loser: There are a lot of different ways you could segment doctors into different categories. Oscar has decided to approach this task by analyzing the richest sources of data we have: insurance claims. We took a highly-detailed look into what services our doctors were performing for different members, and then put them into buckets based on what services they most regularly performed. This resulted in a taxonomy of 400 different categories of doctors. At Oscar, we don’t rely on broad designations like “gynecologist” or “dermatologists” because it’s crucial to us that our networks represent the full array of services our members might need. We may have a full slate of neurologists and an equally full slate of otologists, but unless at least a few of those doctors are further sub-specialized in neurotology, our network wouldn’t be able to cover the full range of services our members need.

New: Now that Oscar has a few years’ worth of data about how people seek out healthcare, what has surprised you the most?

Loser: The problem with health care isn’t that it doesn’t have enough data—it’s that it doesn’t have the right data. There are huge barriers for legacy insurers that are trying to fix this problem, and most companies go about trying to improve issues by making small tweaks in one department. At Oscar, we’ve come to recognize that no solution for health care can be siloed: different parts of an insurance company—claims operations, data scientists, infrastructure engineers, front end engineers, care delivery teams, and so on—have to rely on systems that allow for an easy transfer of data from one team to another so that they’re dealing with the same source of truth.

We’ve built tech that encourages members to engage with us—and they do: 23% of our members use telemedicine, at 8 times the national average of 3 percent—which in turn produces higher quality data for our data science team to be able to work with and analyze member behavior. As a result, we’re able to pick up irregularities and identify possible problems quicker than a larger insurer.

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