The patient at the centre of the record doesn't exist

She's fifty-one and cisgender. She had menopause at the usual age, not because of surgery, cancer treatment, early ovarian problems, or gender-affirming hormones. In her chart, she's labelled as peri, meno, or post. She's the average patient in studies, but in reality, almost nobody fits this exact profile.

Everyone else is treated like an afterthought: trans women on estrogen, women who had their ovaries removed at 36, breast cancer survivors still on tamoxifen being told their hot flashes are "just menopause." A 41-year-old in perimenopause is told she's too young. A 58-year-old still having symptoms is told she should be past it. When care is built for the average, everyone outside that average gets ignored.

This is a look at the schema choices inside Nila, the ones that look strange the first time you meet them, and why every one of them exists on purpose. It's also a quiet argument with how most of this space is built. Per PitchBook and Crunchbase data compiled by SJF Ventures, menopause-focused startups drew roughly $530 million in venture funding between 2015 and early 2023 alone, and the pace has only picked up since: Midi Health raised $65 million across two 2024 rounds to scale virtual menopause care, and a meaningful slice of Flo Health's $200 million 2024 Series C was earmarked for its expansion into menopause. Almost none of that money has gone toward fixing the parts of the record that keep people misdiagnosed.

Sex, gender, and why one field can't hold both

The HL7 Gender Harmony Project, a working group under Health Level Seven International (the organization that sets the global data standards most electronic health records are built on), has spent four years arguing, with clinicians and informaticists in the same room, that sex and gender are two different variables. Their implementation guide separates sex assigned at birth, gender identity, pronouns, and the sex recorded on a legal document into four distinct fields.

The reason is clinical, not political. Reference ranges, screening intervals, and medication dosing turn on anatomy and hormone status. Consent, safety, and the receptionist's greeting shape identity and pronouns. When those live in one box, the record is inconsistent about at least one of them for every patient whose sex and gender don't match, and it silently degrades care for cis patients too, because the box becomes an unreliable mix of both meanings.

A 2024 study of the Mass General Brigham health system, one of the few EHR builds that separates legal sex, sex assigned at birth, and gender identity into three distinct fields, found that even where all three fields exist, only 20 percent of patients had a completed Gender Identity entry and only 19 percent had a completed Sex Assigned at Birth entry (Foer et al., 2024). Most systems don't even have three fields to leave blank: a 2020 rapid review of gender, sex, and sexual orientation documentation across EHRs found the same pattern nationally, with most systems still collapsing sex and gender into a single field or omitting the distinction altogether (Lau et al., 2020).

Most menopause apps we've looked at collect one field labelled "gender" or "sex" and route the whole product based on it. That design choice has real, specific consequences: it's why a trans woman on estrogen gets asked when her last period was, and why a cis woman in surgical menopause at thirty-six gets a "you're young for this" flow.

Inside Nila, sex assigned at birth, gender identity, and pronouns are three separately answerable questions, all optional, all editable, all yours to keep private. Every one of them has a coded value for logic and a free-text "in your own words" companion beside it, following the pattern recommended in the University of Warwick's Gender and LGBTQUIA+ Data Collection guidance. If the closed options don't fit, you write what does. Both fields ride together into your export.

Peri, meno, and post are three bins where the science uses ten

Doctors and researchers use a much more detailed system, called STRAW+10, to describe menopause. Instead of just "peri," "meno," or "post," STRAW+10 breaks menopause into ten clear stages, based on real changes to periods, hormones, and symptoms. This helps catch what's really going on, not just guess based on age or rough categories. The biggest menopause research studies use this system, but most electronic health records do not.

Most doctors' systems only let them pick one of three stages. That's why a 41-year-old with changing periods and hot flashes is told she's "too young" (even though she fits a real stage in STRAW+10), and a 55-year-old with night sweats is told she should be done by now. In reality, symptoms like hot flashes often last over seven years, and even longer for some women.

Nila lets you pick the level of detail you want: a simple version for most people, or the full STRAW+10 stages if you want to get specific. The detailed version is there because the science supports it and because your chart should always have offered this choice.

The cause of menopause is known, and the record should know it

Natural menopause, surgical menopause, chemotherapy or radiation, GnRH analogues (gonadotropin-releasing hormone analogues, medications used to deliberately and temporarily shut down ovarian hormone production, often for endometriosis or ahead of certain cancer treatments), primary ovarian insufficiency, and gender-affirming hormonal physiology are six distinct clinical situations with six distinct evidence bases. The guidance on hormone therapy after breast cancer isn't the guidance for natural menopause. The International Menopause Society recommends that a person with POI replace hormones until at least the average age of natural menopause, on bone and cardiovascular grounds, advice that would be reckless for someone in natural menopause at fifty-five.

Most electronic records infer causation from procedure histories and medication lists rather than asking directly. Cohort papers routinely note that classifying "surgical menopause" requires manual chart review in a meaningful share of cases, because the field isn't captured directly anywhere. That's how someone on tamoxifen ends up reading a generic hormone-therapy explainer that doesn't apply to her.

I'm a case study in why this field can't be a single dropdown. On paper, I'm three of the six coded values at once: natural transition, chemical menopause from medication, and surgical menopause after that. A record that only let me pick one would have been wrong about two-thirds of my own history, at exactly the moment my history mattered most.

We ask you directly, in clear terms: you can pick from six options, or write your own if none fit. It might feel like an odd question, but answering it honestly means you get advice that actually matches your situation, not just generic info.

Hormone exposure has its own table, not a line in a med list

Medication lists hold drug names. They don't, in most systems, hold information on why the drug was prescribed, whether the route was recorded accurately, whether the dates are queryable, or whether the entry is current or past. That means an algorithm reading your chart can't cleanly tell apart combined oral contraception at twenty-five, ten years of transdermal estradiol from forty-eight, five years of tamoxifen from fifty-two, and continuous gender-affirming estradiol from thirty. It sees five entries with no way to tell what any of them were for.

When it comes to risks like blood clots, cancer, heart disease, or bone strength, doctors need to know the details of your hormone history: what you took, why, and when. That's why guidelines say gender-affirming hormones should always be tracked with all the details, not just a drug name or start date.

In Nila, your hormone history gets its own section: what you took, how you took it, why, how much, when you started and stopped, and if you're still taking it. You can always download this info and take it with you if you switch platforms.

Symptoms are measured with a scale that has a name

The Menopause Rating Scale (MRS), the Greene Climacteric Scale, and MENQoL (the Menopause-Specific Quality of Life questionnaire) are validated symptom scales that have been translated into dozens of languages and are routinely used in menopause research (Heinemann et al., 2004; Greene, 1998). They're almost never captured in primary-care EHRs, and when they are, they get pasted into free-text notes with no code, no domain subscore, and no way to reconstruct a longitudinal trajectory across clinics. Every time you change doctors, the story starts over.

We use the Menopause Rating Scale, the same 11 questions researchers use, to track your symptoms over time. You can add the date of your last period if you want. This way, you'll have a clear, useful chart to show your doctor, instead of starting from scratch at every visit.

The rule that most of this space quietly breaks

Everything above is data you told us. What Nila never does is derive identity or clinical history from your behaviour, the pathway you clicked on, or what came up in a chat. That distinction sounds academic until you notice how many products in this space quietly flip it: someone picks the surgical-menopause pathway to read an article, and the app writes "surgical menopause" onto her identity permanently. Someone visits a trans-affirming page, and the app codes her gender for the next five years of segmentation.

The University of British Columbia's Centre for Gender & Sexual Health Equity, through its Research Equity Toolkit series on gender and sex in methods and measurement, is direct about the fix: let people self-report, never infer, never auto-fill. We've made it a rule in the codebase. Identity fields are self-reported, free-text-capable, independently answerable, and never inferred from other data. Coded values exist for logic; your own words exist for the record. Nila's context window sees only what you told us.

The part that's different by design: it's yours

Most apps in this space quietly rely on the fact that you won't leave and won't ask for your data back. We're building on the opposite assumption.

We ask clear, honest questions, and every answer is optional. No trick questions, no required fields, and no dropdowns that exclude anyone. If a question will affect your advice, we'll tell you why before you answer.

Every closed question has a free-text partner. If the options don't describe your situation, you describe it, and we save both.

Every question we ask has a job. We don't collect anything we can't clearly point to a use for. If we ever want more, we'll earn it before we ask.

We keep your answers organized so each thing (sex, gender, pronouns, cause of menopause, and hormone use) gets its own place. That way, nothing is mixed up or lost.

You can export it all in one click: health profile, MRS scores, hormone exposures, bookmarks, journal, check-ins. If a standards working group finds our schema useful, it's available on request. If you find another platform you'd rather use, take your record with you.

It stays private by default. Nothing here is sold, ranked, or used to train a model. Sponsors, when they appear, sit in clearly marked, dismissible sections and never touch member data. Anything that leaves your account is opt-in, per feature.

The uncomfortable part

None of this is radical. Every change described above is documented in the peer-reviewed literature or in published standards work: HL7 has the schema, STRAW+10 has the stages, the MRS has the questions, WPATH has the hormone-log spec, Warwick has the free-text guidance, UBC's CGSHE has the never-infer rule. Your electronic record doesn't reflect any of it because the people with the leverage to move the vendors haven't demanded it, and a sector that's pulled in half a billion dollars in venture funding over the past decade has spent almost none of it closing that gap.

We can't move the EHR vendors. What a small, member-owned platform can do is refuse to build the same shortcuts: ship the boring, obviously correct version of what a midlife-care record should have been the whole time, one field at a time, and give it back to the people it's for.

Because the people it's for aren't a rounding error. I'm not one. Neither are you.

References

Foer, D., Rubins, D. M., Nguyen, V., et al. (2024). Utilization of electronic health record sex and gender demographic fields: A metadata and mixed methods analysis. Journal of the American Medical Informatics Association, 31(4), 910-918. https://pmc.ncbi.nlm.nih.gov/articles/PMC10990507/

Greene, J. G. (1998). Constructing a standard climacteric scale. Maturitas, 29(1), 25-31. https://pubmed.ncbi.nlm.nih.gov/9643514/

Harlow, S. D., Gass, M., Hall, J. E., et al. (2012). Executive summary of the Stages of Reproductive Aging Workshop + 10: Addressing the unfinished agenda of staging reproductive aging. Journal of Clinical Endocrinology & Metabolism, 97(4), 1159-1168. https://academic.oup.com/jcem/article/97/4/1159/2833253

Harman, S. M., Black, D. M., Naftolin, F., et al. (2014). Arterial imaging outcomes and cardiovascular risk factors in recently menopausal women: A randomized trial (KEEPS). Annals of Internal Medicine, 161(4), 249-260. https://pubmed.ncbi.nlm.nih.gov/25069991

Health Level Seven International. (n.d.). HL7 Gender Harmony Project: FHIR implementation guide. https://build.fhir.org/ig/HL7/fhir-gender-harmony/

Heinemann, K., Ruebig, A., Potthoff, P., et al. (2004). The Menopause Rating Scale (MRS): Reliability of scores of menopausal complaints. Health and Quality of Life Outcomes, 2, 45. https://hqlo.biomedcentral.com/articles/10.1186/1477-7525-2-45

Hodis, H. N., Mack, W. J., Henderson, V. W., et al. (2016). Vascular effects of early versus late postmenopausal treatment with estradiol (ELITE trial). New England Journal of Medicine, 374(13), 1221-1231. https://pubmed.ncbi.nlm.nih.gov/27028912

International Menopause Society. (2020). Recommendations on hormone replacement in primary ovarian insufficiency. Climacteric, 23(6), 600-604. https://www.tandfonline.com/doi/full/10.1080/13697137.2020.1804547

Lau, F., Antonio, M., Davison, K., Queen, R., & Devor, A. (2020). A rapid review of gender, sex, and sexual orientation documentation in electronic health records. Journal of the American Medical Informatics Association, 27(11), 1774-1783. https://academic.oup.com/jamia/article/27/11/1774/5906099

New Market Pitch. (2026). Femtech funding trends. https://newmarketpitch.com/blogs/news/femtech-funding-trends

SJF Ventures. (2023). Market analysis of menopause-focused startup funding (PitchBook and Crunchbase data). https://sjfventures.com/sjf-ventures-market-analysis-outlines-startups-disrupting-menopause-care-and-opportunities-for-investors/

University of British Columbia, Centre for Gender & Sexual Health Equity. (2022). Research equity toolkit series. https://cgshe.ca/news/2022/01/cgshe-launches-new-research-equity-toolkit-series-to-create-more-gender-inclusive-research/

University of Warwick. (n.d.). Gender and LGBTQUIA+ data collection best practice guidance. https://warwick.ac.uk/fac/cross_fac/academy/activities/learningcircles/transqueerpedagogies/datacollection/

World Professional Association for Transgender Health. (2022). Standards of care for the health of transgender and gender diverse people, version 8. International Journal of Transgender Health, 23(Suppl 1), S1-S259. https://www.tandfonline.com/doi/full/10.1080/26895269.2022.2100644