Elina Onitskansky

The through-line

Onitskansky's fixed position is that obesity is not one disease with one answer, and that any solution which ignores the economics of the people who actually pay for care will not last. She frames this through a definition of value-based care she guards jealously: "it means that you are driving great member experience at a supporting engagement it means that you're driving clinical value and health outcomes and in addition to that you are driving a financially compelling solution" [1]. The third leg is the one she says the field keeps dropping. Her complaint about the language is blunt: "we've started to use value based in the same way that beauty contestants Ed to talk about world peace which is people say a lot of things and then they put value based at the end" [1]. The same tension animates her discussion of GLP-1s as a genuine breakthrough that could nonetheless overwhelm payers if deployed without a care model around them [2][3].

The second thread is that she came at this from inside the system rather than against it, professionally as a health plan executive and personally as someone treated for obesity, and she designs accordingly: "any solution that was going to have long-term traction had to be one that worked for employers and health plans right that basically worked within the system" [1]. Her framing of the condition is whole-health rather than weight-focused, spanning obesity and cardiometabolic care [4].

On what value-based obesity care has to deliver

She describes Ilant Health as a value-based obesity management and cardiometabolic health solution built on three pillars simultaneously: "I need it to work for the member I need it to work clinically and I need it to work financially" [1]. Against that standard she sorts the market into two failed generations. The first was direct-to-consumer, "very focused on getting medications to people and so we're maybe very focused on one version of that member experience" [1]. The second is the step-therapy wave, solutions that are essentially "a better approach to utilization management and are just solving maybe some part of financial" [1]. Her alternative is deaggregation: "how do we de aage care to get the right member the right solution with the right level of support" [1].

On finding and engaging the highest-acuity patients

The financial case, in her account, is impossible without reaching the people who drive the spend. "Obesity is just like any other part of healthcare where the cost 80% of the cost right is driven by let's call it 20% of the population" [1]. Identifying that group is harder here than elsewhere "because there is so much shame and stigma because historically undercoded," which is why a large share of the company's analytics is pointed at exactly that problem [1][4]. Her conclusion is categorical: "I don't think you can drive a financial solution if you don't engage the highest security individuals" [1].

On why there is no miracle drug and no miracle diet

Onitskansky rejects both poles of the current argument: "most solutions are either gp1s for all or gp1s for none to me like neither makes sense" [1]. Her reasoning is one of scale and analogy. Obesity affects roughly half the US population, and no one would accept a single answer for a condition that large: "if I went to you and I said we're going to solve all of P with X diet or Y drug or Z procedure you'd be like that doesn't make any sense" [1]. She is generous about the tools themselves, calling GLP-1s transformative, bariatric surgery's evolution a game changer, and nutrition science far better understood than fifteen years ago, and still insists "there's like no one siiz fits-all solution you've got to match people to the right treatment" [1].

What she thinks keeps the field stuck is a residue of diet culture inside the solutions themselves: "every diet I've ever done was sort of structured as you're going to do this diet a miracle will happen you will become normal and then like it's all over" [1]. Speaking as someone who "tried literally everything out there," she has searched for "many a miracle diet" herself [1]. The move she wants is away from the miracle framing entirely, toward "figuring out how you solve for individual folks" [1], a personalised and holistic model of treatment rather than a weight-loss event [4].

On technology as an enabler, not the care itself

She is emphatic that digital innovation is a support layer around clinical care rather than a substitute for it. "One of the errors that was made was in the excitement at getting away from fax machines and doctor's offices that never called you back" was to throw out the high-touch elements along with the friction [1]. Remote patient monitoring, chatbots and AI are, in her framing, "all supporters and enablers of still good clinical care" [1]. Her line on the limit is direct: "the solution is not going to be an AI chat box sitting by itself it may be an AI chatbot like that occasionally pushes people to a physician," because "at the end of the day humans still need humans" [1]. The sequencing matters to her: right patient, right treatment, and then support, since "none of that matters if they don't sort of do the treatment if they don't feel supported" [1]. She also discusses the role of AI and data in clinical decision-making as part of where the company is heading [4].

On respecting the people already in the seats

A recurring irritation is founders who arrive assuming incumbent expertise is worthless. "When I look at a lot of digital Health Innovation I feel like there's a lot of folks running in being like everyone in healthcare doesn't know what they're doing and you're like guys it's the fifth of the US economy do you literally think every existing human here has never had a good thought" [1]. The alternative discipline she recommends is to "try to understand why do people sitting in the seats that they sit in act in the way they do" and then solve with that understanding in place [1]. She traces the habit to consulting, where the junior instinct is that a problem can be cracked from an Excel model and first principles until a client with twenty years in the work explains that "when you do a then B happens and that drives three other things" [1]. Sometimes the expert is wrong, she allows, but "a lot of times they're actually right" [1].

On what a big organisation teaches you that consulting cannot

Her account of moving from McKenzie to Molina Healthcare is largely about discovering constraints that are invisible from outside. In consulting and private equity "there's like literally always folks available to do XYZ," because staffing humans onto projects is the business; inside a health plan, launching something new means pulling people from other priorities or hiring from scratch, and competing for slots against everything else in the queue [1]. The deeper lesson concerns architecture: "everything is built on a certain infrastructure so like moving things right requires understanding how that infrastructure works," including whether a change touches quality, care management or network operations [1]. Hence her partial defence of the slowness everyone complains about in health plans and health systems. Some of it is aversion to change, but some is that these are "very large complex organizations where any given action probably has like four more implications than you may have initially sort of thought about" [1].

On choosing hard problems

The career logic she describes is consistent from the lab onward: go where the problem is large and the solution is not obvious. She chose healthcare partly because it was meaningful, with family who are doctors and family affected by health, and partly for a reason she delivers half-jokingly, that "it was so messed up that there were always going to be opportunities" [1]. The contrast is with earlier industrials and consumer work where the goal was to "move the needle like 1% um cost of a screw or something like that," against healthcare problems that were "fundamentally not working" [1]. She frames this as practical as much as altruistic: "any area that had a really massive problem where the solution was nonobvious was going to create opportunities" to take leadership roles and innovate [1].

The same instinct explains Molina. She was drawn by a company mid-transformation with a new leadership team, where she could build the first ever corporate strategy for a Fortune 150 company serving millions of people, many of them among the highest-need individuals relying on it for coverage [1]. Her view of prestige is dismissive: "no quote unquote job is sexy in it of itself it's like hey are you doing things on a regular basis where you're like hey that's really interesting I'm doing something pretty impactful" [1]. The early physics path ended for a more human reason, that twelve-hour days in a clean room suit a giant extrovert badly, since "the beers don't talk back and if they do start talking back like something went really wrong" [1]. From consulting she took two durable convictions: that "just about any problem is solvable," because what looks impossible in week one is manageable by week four, and "the value of starting from first principles," which she also credits to physics [1]. She adds a case for a non-linear career, arguing that as much as she loves data and analytics, pattern recognition built across industries transfers usefully into healthcare [1].

On the personal origin of the company

Ilant Health came out of what she calls a unique background, professional experience in healthcare and personal experience of obesity since childhood [1][4]. Having tried every exercise trend, diet and weight loss spa available, she referred herself into bariatric surgery and obesity medicine while still at Molina and lost over a hundred pounds sustainably for the first time [1]. The founding moment was sitting on a beach in Florida at Thanksgiving, for the first time not consumed by how much she would need to work out to compensate for dinner [1]. Two things followed. One was a market pattern: "I've seen this movie before its mental health 10 years ago it substance use disorder five years ago it sort of maternal health and fertility seven years ago" [1]. The other was a gap she had lived through personally, that as transformative as the journey had been, the support around it was terrible [1].

Takeaways

• Value-based care, in her definition, has three legs at once, member experience and engagement, clinical outcomes, and financial sustainability, and the financial leg is the one digital health most often omits [1].
• Roughly 20% of the population drives 80% of obesity-related cost, so a financially credible solution has to identify and engage the highest-acuity members, a job made harder by shame, stigma and historical undercoding [1].
• Reject both "GLP-1s for all" and "GLP-1s for none"; with obesity affecting about half the US population, the work is matching individuals to the right treatment among drugs, surgery and nutrition [1].
• Solutions that promise a miracle and an endpoint are importing diet culture into healthcare; the goal is sustained individual matching, not a cure event [1].
• AI, chatbots and remote monitoring are enablers of good clinical care, not replacements for it, because "humans still need humans" [1].
• Innovators who assume incumbents have never had a good thought about a fifth of the US economy are making a mistake; understand why people in those seats behave as they do, then solve around it [1].
• Health plan slowness is partly aversion to change and partly real complexity, since every action inside a large organisation carries implications for quality, care management and network operations [1].
• Pick problems that are large and whose solutions are non-obvious, because that is where both impact and opportunity concentrate [1].