ŌURA VP of Product on Building the Future of Women’s Health

What does it take to build health products that truly understand women’s bodies? In this podcast, Planned Parenthood Direct CPO Hannah Park sits down with ŌURA VP of Product Inessa Lurye to discuss how she’s building the future of women’s health with science-driven products that turn wearable data into meaningful health insights. She shares what it takes to adapt algorithms to the complexity of women’s health, balance product development with long-term research, and build for diverse populations. The conversation also explores AI, health equity, and how product leaders can make informed bets on where healthcare is headed.

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Show Notes

1. Inessa Lurye came to Oura from Hinge Health, where she built the largest digital pelvic health program in the country. What surprised her most about the transition was not the differences between the two companies but the convergence, as both Hinge and Oura were independently moving toward AI-enabled health delivery from opposite starting points.
2. Building across hardware, firmware, science, and software means managing multiple time horizons simultaneously. A new hardware sensing capability might require planning three or more years out, a new algorithm one to two years, and a software feature a matter of months, and all of these must be kept in motion at the same time without any single track blocking another.
3. Oura’s roadmap strategy in women’s health is built as a portfolio of bets at different risk levels. Some features are grounded in descriptive data where success is fairly predictable, and others are long-term studies aimed at detection capabilities that do not yet exist in the market. The roadmap is not built to depend on the long shots, but it is designed to amplify them quickly if early signals are strong.
4. For decades, most health products have treated women’s physiology as a variation on a generic male default. Oura’s response has been to adapt core algorithms themselves, not just add a cycle tracking layer on top, tailoring things like the symptom radar algorithm to account for how HRV and temperature naturally shift across the luteal phase so the system does not misfire for women.
5. Women’s health is not a single algorithm for a single population. Irregular cycles, hormonal birth control, perimenopause, pregnancy, and postpartum all require distinct modeling, and building for the full range of natural variability in the member population, rather than just the most common case, is where most of the time, effort, and research actually goes.
6. Because women’s health has been chronically understudied, Oura made a deliberate decision to generate the science it needs rather than build on top of what already exists. That has meant funding and conducting original research, including studies with Scripps on pregnancy and UCSF on PCOS, rather than waiting for the field to catch up.
7. The existing clinical standard for assessing midlife health is a survey validated on fewer than 500 women, conducted in person on paper in Germany years ago. Oura found that baseline insufficient and built a new instrument validated across several thousand women digitally, across multiple countries, adding dimensions like self-perception and quality of life impact that the original survey never measured.
8. Oura’s position in the healthcare ecosystem is deliberately as an intelligence and data layer, not a care delivery company. The goal is to give members enough context and information to make their clinical conversations more informed and more productive, rather than to diagnose or treat.
9. Oura has built partnerships with virtual care providers including Midi, Maven, Progeny, and Evernow, and more recently with Council, an AI-driven primary care platform that allows members to speak with clinical AI and connect instantly with a clinician, all while sharing Oura health data with permission. These partnerships extend the value of the data without Oura stepping into the diagnosis space.
10. Getting wearable data in front of clinicians in a usable form is a genuine design challenge. Clinicians operate in short visits and cannot realistically navigate a new interface mid-appointment, which means the data Oura surfaces needs to arrive in the systems clinicians already use, in a visual format that is thematic and immediately actionable rather than a raw dump of daily data points.
11. There is currently no standard for how cycle data is represented in an electronic health record. Oura is actively working to help define that standard, both because it benefits Oura’s members and because wearables are increasingly prevalent and the infrastructure needs to exist across the industry, not just inside one company.
12. Members regularly report that Oura data prompted them to seek care they otherwise would not have sought, from identifying postoperative infections to flagging dangerous drops in heart rate that led to hospitalization. These stories reinforce Oura’s strategic positioning: not diagnosing, but watching, alerting, and directing people toward the right clinical moment with better information in hand.
13. Every piece of copy and every eval written for Oura’s clinical AI is reviewed through a regulatory lens to ensure that the language stays within the wellness and informational realm. As AI takes on a larger role in the product, that discipline becomes harder to maintain and more important to get right.
14. Equity in wearable health tech is both a product question and a science question. If the population wearing the ring skews affluent and health-engaged, the algorithms learn from a biased dataset, which means the science itself reflects who has access. Oura addresses this by partnering with research organizations and donating rings to enable studies with populations that would not otherwise purchase the device.
15. Oura donated 10,000 rings to the Tidepool organization to enable research at the intersection of women’s health and diabetes, specifically to capture data from populations whose health needs are not reflected in the standard Oura member base. Diverse datasets are not just an equity imperative; they are a scientific necessity for building algorithms that actually work across the full range of bodies and conditions.
16. Expanding access over the longer term depends on making the clinical and economic case for insurance coverage and employer subsidies. Oura’s partnership with Essence Healthcare, which serves Medicare members, is an early example of unlocking access through coverage rather than individual purchasing power. The goal is for outcomes data to eventually justify broad coverage the same way any other effective health intervention would.
17. Inessa’s earlier career in healthcare policy in city government gave her a systems-level perspective that still shapes how she approaches product. That training is most visible in her habit of forming a thesis on where a particular health space is headed in five years and making product bets today that are sized for where the market will be, not where it is now.
18. In women’s health specifically, the ability to think in long arcs matters because most of the hardest bets, the detection algorithms, the foundational research, the clinical partnerships, take years to mature. Investing in them before the market needs them is what creates the moat when the market catches up.
19. Inessa’s view on a women’s health myth worth retiring: cycle syncing has been overhyped in popular culture to a degree that assigns symptoms to cycle phases in ways the science does not support. Real hormonal variation across a cycle exists and matters, but popular interpretation has overshot what the data actually shows.
20. For product people who want to enter women’s health, Inessa’s advice is to look beyond companies that are 100 percent focused on women’s health. Some of the most interesting and scalable work happens when a broader platform carves out a genuine women’s health vertical inside a product built for everyone, and that path opens up more companies and more opportunities than the purely women’s health startup route alone.