Building for Menopause? 12 Things to Get Right With Your Health Data
A practical guide for digital health teams, clinics and pharma-supported menopause programmes on the data decisions that determine whether a menopause tool can be trusted.
Suhina Singh
Founder & CEO, Jonda Health
October 4, 2026
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11 min read
Menopause is finally getting the attention it has long deserved. Symptom trackers, virtual clinics, employer benefits and pharma-supported programmes have all grown quickly, and many of them are doing important work for women who were underserved for decades. Menopause is also one of the clearest examples in healthcare of a condition where a tool is only as good as the data underneath it.
Perimenopause can unfold over several years. It rarely announces itself through a single test result, and the picture that matters is spread across cycle apps, lab portals, prescriptions, imaging reports, wearables and the notes of several different clinicians. A product that reads one piece of that picture in isolation can reassure the wrong person, worry the wrong person, or produce insights that nobody can act on.
We wrote this guide for three groups of readers. If you are a digital health or femtech team, it sets out the data decisions to get right before you scale. If you run a clinic or menopause service, it covers what it takes to keep a woman's record coherent across providers and years. If you lead a pharma-supported menopause programme, it explains what your data needs in order to show outcomes and stand up as real-world evidence. The twelve points apply to all three, and where the emphasis differs, we say so.
Where menopause data actually lives
Before the checklist, it helps to see the problem. A woman in her mid-forties may track her cycle and symptoms in one app, receive blood results through a laboratory portal, collect prescriptions from more than one pharmacy, and hold bone density and breast imaging reports as PDFs in her email. Her ring or watch records her sleep, and her GP, gynaecologist and perhaps a menopause specialist each keep their own notes. None of these sources were designed to work together.
The twelve points below are grouped into four themes: getting the clinical picture right, making the data usable, connecting the longer story, and building for trust and scale.
Get the clinical picture right
1Don’t build on a single hormone value
Hormone levels during perimenopause fluctuate considerably, sometimes from one week to the next. This is why the NICE guideline on menopause (NG23, updated in 2024) advises against using laboratory and imaging tests, including oestradiol and anti-Müllerian hormone, to identify perimenopause or menopause in otherwise healthy people aged 45 and over. It also advises against using FSH in people taking combined hormonal contraception or high-dose progestogen, and reserves FSH for narrower situations, such as people aged 40 to 45 with symptoms and cycle changes, or people under 40 in whom premature ovarian insufficiency is suspected.
For product teams, this has a direct design consequence. A tool that labels someone as perimenopausal, or not yet, on the strength of one FSH result, or that presents a single at-home hormone test as an answer, is working against the clinical evidence. Hormone results still have value when they are placed in context and viewed over time. The risk lies in treating them as a verdict.
Two tests, two months apart, tell different stories about the same woman. Symptoms, cycle history and age carry more weight than any single result.
What good looks like: stage and symptom logic built on age, cycle history and symptoms, with laboratory values used as supporting context and shown as trends rather than single verdicts.
Matters most to: StartupsPharma programmes
2Capture the context that changes the result
The same oestradiol or FSH value means different things depending on who it belongs to. Age, where she is in her cycle if she still has one, whether she uses hormonal contraception or a hormonal IUD, whether she has had a hysterectomy or her ovaries removed, whether she is on menopausal hormone therapy, and whether she has a history of breast cancer or another hormone-sensitive condition all change how a result should be read.
Many menopause tools collect some of this at onboarding and never update it, even though it will change over the years a woman is likely to use the product. A record that still says “no hormonal treatment” two years after she started therapy will quietly distort every insight built on it.
What good looks like: context stored as structured, dated fields that travel with every result and are prompted for review when life events change.
Matters most to: StartupsClinicsPharma programmes
3Use validated symptom instruments
Symptoms sit at the centre of menopause care, and they are also where products are most tempted to invent their own scales. Home-grown questionnaires are quick to build but hard to compare, and they rarely hold up when a clinician, a researcher or a regulator asks what a score actually means. Validated instruments such as the Menopause Rating Scale, the Greene Climacteric Scale and the Menopause-Specific Quality of Life questionnaire (MENQOL) exist for exactly this reason. Each has its own scoring rules and, in some cases, licensing terms worth checking before you build them in.
Cadence matters as much as the instrument. A structured assessment at regular intervals, alongside lighter day-to-day tracking, usually produces more usable data than asking a woman to complete a long questionnaire every day and watching adherence fall away.
What good looks like: a validated core instrument with clear scoring, a realistic cadence, and free-text symptoms mapped to consistent terms so they can be analysed alongside everything else.
Matters most to: ClinicsPharma programmes
Make the data usable
4Harmonise units and reference ranges
Many laboratories in the UK, Europe and Australia report oestradiol in pmol/L, laboratories in the United States typically use pg/mL, and laboratories across Asia use both (1 pg/mL is roughly 3.67 pmol/L). FSH may be reported as IU/L or mIU/mL. Reference ranges vary by laboratory, by method and by cycle phase, and the postmenopausal range printed on one report may not match another’s.
When results from different laboratories, countries or at-home kits land in one record without harmonisation, trends stop meaning anything and alerts fire on unit mismatches rather than clinical change. This problem grows quietly as a product expands into new markets or starts accepting results that women upload themselves.
What good looks like: every result kept with its original value, unit, method and reference range, converted to a consistent unit for analysis, with the source laboratory preserved so any figure can be traced back.
Matters most to: StartupsClinics
5Structure medication histories properly
Menopausal hormone therapy is one of the hardest parts of the record to get right. The same treatment appears under different brand names in different countries, and it is delivered as tablets, patches, gels, sprays, vaginal preparations or implants. It is often combined with a progestogen, which matters clinically because it protects the lining of the womb in women who still have a uterus. Doses change, products are switched when supplies run short, and many women also use non-hormonal treatments, supplements or complementary products. Prescriptions from abroad, and records that exist only as scans or handwriting, add another layer.
What good looks like: each medication recorded with its active ingredient, route, dose, start and stop dates and prescribing source, mapped to medical coding standards so that differences in brand, language and country do not fragment the history.
Matters most to: ClinicsPharma programmes
6Turn documents into data
Much of the clinical story in menopause care lives in documents: bone density (DEXA) reports, mammography and breast imaging results, pelvic ultrasound reports, cervical screening results and specialist letters. They arrive as PDFs, scans and phone photos, in different layouts and sometimes different languages.
Extracting the text is only the first step, as we explain in Reading a Lab Report with AI Is Not the Same as Making It Usable. The findings that matter, such as a bone density T-score, an endometrial thickness or a recommendation for follow-up, need to be identified, structured, checked and linked to the right date and the right person.
What good looks like: documents transformed into structured findings with clinical review, while the original is kept alongside for reference.
Matters most to: StartupsClinics
Connect the longer story
7Link the heart, metabolic and bone picture
Menopause is a window into long-term health, not only a set of symptoms to manage. The years around it are associated with changes in lipid profiles, rising cardiovascular risk, shifts in glucose metabolism and faster bone loss. A tool that tracks hot flushes but leaves out cholesterol, blood pressure, HbA1c and bone density misses much of what matters for a woman’s future health, and misses a real opportunity for prevention.
What good looks like: cardiometabolic and bone markers held in the same longitudinal record as symptoms and treatment, so trends can be seen side by side.
Matters most to: StartupsClinicsPharma programmes
8Use wearable signals honestly
Watches and rings can capture sleep, heart rate variability and skin temperature, and there is growing research interest in using these signals to detect hot flushes and night sweats. It is still an emerging area. Devices measure differently, firmware changes over time, and a rise in skin temperature may reflect a warm room, exercise or illness rather than a vasomotor symptom. The risk is presenting inferred events as if they were confirmed.
What good looks like: each signal stored with its device, version and sampling details, inferred events clearly labelled as inferred, and wearable data combined with what women report rather than replacing it.
Matters most to: StartupsPharma programmes
9Give clinicians summaries they can use
Many women arrive at a consultation with screenshots, exports and months of app data, and clinicians have minutes rather than hours. A menopause tool earns clinical trust when it can produce a concise, structured summary: symptom trends on a validated scale, current and past treatments, relevant results with dates and units, and anything that needs attention.
Some symptoms should never simply be logged. Bleeding after menopause, for example, needs clinical assessment, and a tool should direct a woman to her clinician rather than record it as another data point.
What good looks like: an exportable clinician summary in a consistent format, clear escalation rules for red-flag symptoms, and a record that can move into a clinic’s own system.
Matters most to: StartupsClinics
Build for trust and scale
10Know when your tool becomes a medical device
Many menopause products begin as wellness or tracking tools. The line into regulated software is usually crossed by the claims rather than the technology. Suggesting a diagnosis or stage, recommending or adjusting treatment, or predicting an individual’s risk can bring a product within medical device regulation in many markets, including Singapore, the UK, the European Union, Australia and the United States. Each regulator draws the line differently, and marketing copy, onboarding screens and in-app messages all count towards what a product is judged to claim.
What good looks like: an intended purpose written down early, claims reviewed against the rules in each market you serve, and evidence on data quality and validation gathered from the start, because retrofitting it later is slow and costly.
Matters most to: StartupsPharma programmes
11Treat consent and privacy as design decisions
Menopause data is among the most sensitive information a person can share. It brings together reproductive health, mental health, medications and sometimes cancer history, and it falls within the special categories of health data protected under laws such as GDPR, Singapore’s PDPA and, where it applies, HIPAA in the United States. Many women are understandably cautious about who can see this information, particularly where employers or insurers are involved.
Consent should be specific about how data will be used, including for research or product improvement. Data used beyond direct care should be pseudonymised, with access controls and audit trails in place.
What good looks like: granular consent recorded alongside the data, pseudonymisation for secondary use, a clear separation between identifiable and analytical datasets, and a documented approach to cross-border transfers.
Matters most to: StartupsClinicsPharma programmes
12Build for portability from day one
A woman’s menopause journey will outlast most of the products she uses along the way. She may change apps, clinics and even countries, and programmes come to an end. Data locked into one product’s own format loses much of its value when that happens.
Structuring data to interoperability standards such as FHIR, with results and medications mapped to medical coding standards, makes it possible to share records with clinicians, move them into a clinic’s system, contribute to research with the right consent, and combine data across programmes.
What good looks like: records that can be exported in a standard format with their provenance intact, so the data stays useful beyond the product that collected it.
Matters most to: StartupsClinicsPharma programmes
Where to start, depending on who you are
You do not need to solve all twelve at once. The right starting point depends on what you are building and what your data needs to do.
Digital health and femtech teams
Start with 1, 2, 4 and 10
These shape your core logic, your data model and your claims, and they are far cheaper to get right before launch than after. The rest can mature with the product, provided the foundation allows it.
Clinics and menopause services
Start with 4, 5, 6 and 9
Your challenge is continuity. Women arrive with results from other laboratories, prescriptions from other countries and app data you did not choose. These points decide how quickly a clinician can understand her history in the time available.
Pharma-supported programmes
Start with 3, 7, 11 and 12
Programmes are judged on the evidence they generate. These points decide whether your data can show outcomes, support publications or real-world evidence, and be combined with other sources once the programme ends.
How Jonda Health can help
Some of the twelve points are data problems, and some are questions of design, strategy and delivery. We support both, so you can come to us with the whole picture rather than splitting it across several partners.
JondaX
Make your data usable
Bring it together. Laboratory reports, medical device readings, wearable data and clinical documents in any format or language, turned into structured records.Items 4, 6 and 8
Make it consistent. Units harmonised, variant captures resolved, medications and results mapped to medical coding standards, with clinical review and quality checks built in.Items 4 and 5
Keep it longitudinal and portable. One coherent record over time, ready to share with clinicians, research and other systems.Items 7 and 12
Jonda Health Services
Design, build and prove your programme
Experience design. Symptom tracking that women keep using, and clinician summaries that fit into a real consultation.Items 3 and 9
Digital health strategy. Intended purpose, claims, where to start and what to build yourselves versus bring in.Items 1, 2 and 10
Automation and digital enablement. Programme operations, patient communication and responsible use of AI in menopause support.Items 9 and 11
Insight and value. Outcomes, benchmarks and evidence that show what your product or programme is delivering.Items 3, 7 and 12
Jonda Health is ISO 27001 certified, and our platform is designed to comply with HIPAA, GDPR and PDPA.
Talk it through with us
Whether you are designing a menopause product, running a menopause service or planning a programme, we can help at any stage, from shaping the experience and strategy to getting the data right and showing the value it delivers. You do not need to have everything figured out before we speak.
