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Short-term health plans offer flexible way to fill coverage gaps; here’s how they work FOR 12.2 MILLION Americans, signing up for health insurance in 2017 was a leap of faith: that Obamacare would make it through the year, that the health exchanges wouldn’t collapse, that premiums wouldn’t put their families on the street. For the 54,000 New Yorkers who used those exchanges to join Oscar—a millennial-beckoning insurance startup co-founded by Jared Kushner’s younger brother, Joshua —the 2017 enrollment period wasn’t just uncertain. It was, well, kind of bleak.

In July, Oscar’s members found out the company was halving its largest network, based in New York City, from 40,000 doctors to 20,000, from 77 hospitals to 31. That kind of forced breakup should have been a very tough sell.

But somehow, Oscar convinced almost all of those 50,000-plus members to stay. The pitch they made was simple, if not exactly intuitive: Choose better care and service over a thicker phonebook of doctors.

They threw in perks like concierge care teams and direct scheduling from your smartphone to sweeten the deal. And it worked. At the time, no one asked how, exactly, they were going to deliver a paradigmsmashing network—one that was smaller but also somehow better. And Oscar wouldn’t have been at liberty to say. But the answer, of course, was big data.

Yes, technically Oscar is in the insurance business. But it’s really a technology company. Its CEO, Mario Schlosser, is a Stanford-trained data scientist who has built Oscar’s core business by extracting insights from the flood of existing health care data—insurance claims and doctor directories and electronic medical records. It was a move straight out of the Silicon Valley playbook: limit choice, and deliver a better user experience instead. (See the gospel of Jobs, chapter 2010, verse D8.) Oscar’s network is not narrow. And it’s certainly not broad. It is, in the parlance of the Valley, “optimized.”

Oscar was working with the same sloppy, unstandardized data every other insurance company has. But a look inside their data science operation shows it’s what they did with it that was totally different.

Soft Launch When Oscar first launched in 2014, it didn’t have the time or the data to build a network from scratch.

So rather than run around New York negotiating prices, it leased a network from a third-party provider.

Those first two years were pretty rough: Oscar lost more than $100 million in New York alone. To get costs down, contracting director Mike Kopko and his team began meeting with hospitals and clinics to build their own network and ditch the 30 percent upcharge for renting. But Kopko and others at Oscar quickly realized that their only bargaining chip was saying no. That’s because health providers— increasingly consolidated over the past decade—hold all the power to set prices. Insurers, especially new ones, have to take whatever they can get.

Unless you’re Oscar.

The company had something else in mind: marshaling the data they had been collecting since 2014 and using it to build a smarter network—one where they didn’t contract with just any pediatric endocrinologist but the *right *pediatric endocrinologist. One who performed procedures Oscar’s

members needed, who didn’t screw up very much, who referred to the right people and had openings for new patients.

“It seems intuitive and rational in retrospect, but it hasn’t been done before,” says Schlosser. “The novelty of the analytical framework is what sets this apart—to design a network around a patient’s journey through the health care system.”

To do that, Oscar first had to transform its richest datasets—insurance claims—into something that could tell a story about who a doctor is. Claims speak in the language of Current Procedural Terminology, or CPT codes, which uniformly describe medical, surgical, and diagnostic services. They’re built for billing, not better care. But not once Vinod Mitta got his hands on them.

Starting last summer, it was Mitta’s job to dig through records and define groups of doctors by which of the 20,000 CPT codes they used. “The goal was to better understand what type of physician everyone really is,” says Mitta, Oscar’s vice president of clinical operations. “The best way to represent that is to look at the kind of services they provide.” Orthopedic surgeons, for example, tend to carve up the body into different niches—you’ve got your knee reconstructionists and your shoulder stitchers and your hip replacers, but a jack-of-all-joints is pretty rare.

Then Mitta put his clusters in front of focus groups of physicians. Sometimes the subspecialty labels agreed with how the doctors divided up their world. Sometimes, not so much. A 90 percent overlap in CPT codes suggested that sleep doctors and ear, nose, and throat experts could be grouped together— but a tonsillectomy to take out a cancerous tumor is different than a snip to prevent snoring. Mitta took these insights and reorganized the data. Then he went back to the doctors. Then back to the data, then back to the doctors again.

The result was a novel taxonomy of health care providers: 400 buckets based on what doctors do, rather than the title on their office doors. “We boiled the ocean of health care billing into something clinically relevant,” says Nick Reber, Oscar’s vice president of network construction. Starting with doctors instead of just blind data mining, he says, made for a richer model.

Most insurance companies have a far less granular classification system. By law, they’re required to carry enough specialties in their network to meet basic standards of care. On average, that works out to somewhere around 50, though numbers vary a lot state to state and from rural to urban areas. To do that, insurers rely almost exclusively on providers’ doctor directories. But those physician lists are notoriously outdated, incomplete, and filled with errors. A 2015 JAMA study found that as many as 14 percent of health plans sold on the federal insurance exchange are missing doctors in at least one common specialty. For patients, that means the plan you sign up for often isn’t the plan you get. A doctor you thought you could see might not be taking patients, might be in a new location, or might have left the network altogether.

To make sure that didn’t happen for their members, Oscar’s next move was to combine its new classification system with patient data. Using purchased Medicare data and Oscar’s own claims data, Reber’s team built a predictive model based on how often people accessed certain services. Then they

layered on information about where people lived, where doctors were located, and what services they provided. They had identified the doctors who could optimize their network.

For the most part, that’s where the hard data science ends. After verifying doctors’ specialties, locations, and availability, Kopko went out and negotiated. Throughout the fall, his team signed more than 5,000 contracts. Oscar launched its optimized network on January 1, 2017. https://oneillmarketing.net/industry-news/short-term-health-plans-offer-flexible-way-to-fill-coveragegaps-heres-how-they-work/