A practical guide for fertility clinics and IVF laboratories, fertility and femtech apps, employers and insurers offering fertility benefits, and pharma programmes on the data decisions that shape fertility care.
Infertility is common, and often carried quietly. The World Health Organization (WHO) estimates that about one in every six people of reproductive age worldwide experience infertility in their lifetime. It defines infertility as a disease of the male or female reproductive system, marked by the failure to achieve a pregnancy after 12 months or more of regular unprotected sexual intercourse.1, 2
Assisted reproduction has grown into a large and data-heavy field. The latest world report from the International Committee for Monitoring Assisted Reproductive Technologies (ICMART) covers 83 countries and more than 3.3 million reported treatment cycles in 2018 alone,3 and at least 13 million babies have now been born through assisted reproductive technology (ART).4 Alongside clinics, a growing number of apps, employer benefits, insurers and pharma-supported programmes now support people through fertility care.
Fertility data is unusual in several ways. Treatment often involves more than one person. A single treatment cycle can produce outcomes over several years. Eggs, sperm and embryos are stored, moved and used long after the records that describe them were created. And the data is among the most personal information anyone can share. We wrote this guide for four groups of readers: fertility clinics and laboratories, digital health teams building fertility tools, employers and insurers funding fertility benefits, and pharma programmes supporting patients through treatment. The twelve points apply to all four, and where the emphasis differs, we say so.
A couple going through IVF may have had initial tests with a gynaecologist and a urologist, blood tests at a laboratory chain, scans at the fertility clinic every few days during stimulation, and an embryology report from the clinic's laboratory. Medicines may come from a specialty pharmacy with injection support from a pharma programme. One partner may track cycles and symptoms in an app, and if a pregnancy follows, care usually moves to an obstetrician in another system altogether.
Each source holds part of the story. The value for patients, clinicians and programmes comes from bringing them together, across both partners and across every cycle.
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. After them, we set out what to capture at each step of treatment, including egg freezing.
Most health records are organised around visits and results. Fertility treatment is organised around cycles. A single IVF cycle can include weeks of stimulation and monitoring, one egg retrieval, several embryos, a fresh transfer, and frozen transfers months or years later. Each step produces data, and each outcome only makes sense when it is linked back to the cycle it came from.
The International Glossary on Infertility and Fertility Care, developed by ICMART with partner organisations, sets out 283 consensus definitions, including what counts as an ART cycle, a live birth and a cumulative delivery rate.5 Using these definitions consistently is what allows data to be compared between clinics and countries.
An illustrative cycle. Counted per transfer, this is one success in three. Counted per egg retrieval, it is one live birth, with an embryo still in storage. Only a cycle-based record can show both.
What good looks like: every stimulation, retrieval, embryo and transfer linked to the cycle it belongs to, using the international glossary definitions, so that outcomes can be counted per cycle, per retrieval, per transfer or cumulatively.
Matters most to: Fertility clinics and labs, Insurers and employers
Fertility care is one of the few areas of medicine where the patient is often more than one person. The American Urological Association (AUA) and the American Society for Reproductive Medicine (ASRM) note that a male factor is solely responsible in about 20% of infertile couples and contributes in another 30% to 40%.6 Yet partners' results frequently sit in a separate record, under a different doctor, or on paper brought to one appointment.
Semen analysis is a good example of why structure matters. The WHO laboratory manual (6th edition, 2021) gives lower reference limits, the 5th centiles, including a semen volume of 1.4 mL, a sperm concentration of 16 million per mL, a total sperm number of 39 million, total motility of 42%, progressive motility of 30% and normal forms of 4%.7 Laboratories that still print older reference values, or report only a narrative comment, make results hard to compare.
Families are also formed in many ways, with donors, surrogates, same-sex partners and single parents. A data model that assumes one woman and one man will not fit many of the people it is meant to serve.
What good looks like: every person contributing to treatment (partners, donors and surrogates) represented with their own record, linked to the treatment with their own consent, and results recorded with the reference values and edition used.
Matters most to: Fertility clinics and labs, Digital health teams, Pharma programmes
Hormone tests are central to fertility assessment, and some are widely misread. Anti-Müllerian hormone (AMH) is useful for planning ovarian stimulation, but the American College of Obstetricians and Gynecologists (ACOG) advises against using it to counsel women who are not seeking fertility care about their future fertility.8 AMH results also depend on the assay: a comparison of three commercial immunoassays found differences in calibration and called for an international standard.9
This matters most for consumer tools. An app that turns one AMH result into a "fertility score" or an egg-freezing recommendation, without saying which assay produced it, risks both false reassurance and needless worry. Other hormone results depend on the day of the menstrual cycle they were taken, which is often missing from the record.
What good looks like: hormone results stored with the laboratory, assay, units and cycle day, interpreted for the purpose they were ordered for, and never presented on their own as a verdict on someone's fertility.
Matters most to: Digital health teams, Fertility clinics and labs
Few areas of medicine store biological material for as long as fertility care. In the United Kingdom, since 1 July 2022, eggs, sperm and embryos can be stored for up to 55 years, provided consent is renewed every 10 years.10 Over that time, clinics may merge, change systems or close, and the people who created the records move on.
Every stored item needs to stay linked to the people it came from, the consent that governs it, where it is stored and what has happened to it. When that link depends on a spreadsheet or a paper log, the risks are serious, both for the people concerned and for the clinic.
What good looks like: each egg, sperm sample and embryo given a unique identifier linked to its source, cycle, consent status, storage location and history of movements and use, with reminders when consent renewal is due.
Matters most to: Fertility clinics and labs
Embryology reports carry some of the most important information in a cycle: how many eggs were retrieved and mature, how many fertilised, how embryos developed and how they were graded. These reports vary widely between clinics. The Istanbul consensus on embryo assessment, published by ALPHA Scientists in Reproductive Medicine and the European Society of Human Reproduction and Embryology (ESHRE), noted that the many variations in oocyte and embryo grading make comparisons between laboratories extremely difficult.11 Its 2025 update notes that assessment remains prone to variability between observers and laboratories.12
When a patient moves clinics, or a programme combines data from several clinics, these reports usually arrive as PDFs or printouts. A grade of "4AA" means little without knowing which grading system produced it.
What good looks like: embryology and monitoring reports extracted into structured fields with clinical review, the grading system recorded with every grade, and the original report kept alongside.
Matters most to: Fertility clinics and labs, Insurers and employers
Ovarian stimulation involves several medicines, often injected at home, with doses adjusted every few days in response to scans and blood tests, followed by a trigger injection timed to the hour. The record of what was actually taken, and when, is often split between the clinic's protocol, the pharmacy's dispensing record and a patient's notes.
Safety depends on this record. The ESHRE guideline on ovarian stimulation treats moderate or severe ovarian hyperstimulation syndrome (OHSS) as a critical safety outcome and sets out recommendations to prevent it.13 ESHRE describes OHSS as the most common complication related to ART, with an incidence of less than 0.2%.14
What good looks like: each medicine recorded by active ingredient, dose, day of the cycle and time of administration, linked to the monitoring results that prompted each change, with complications such as OHSS recorded as structured events.
Matters most to: Fertility clinics and labs, Pharma programmes
The outcome that matters most to people having fertility treatment is a healthy baby, yet many fertility records stop at a positive pregnancy test. Once a pregnancy is confirmed, care usually moves to an obstetrician, and whether it ended in a live birth, a loss, a preterm birth or a multiple birth may never make its way back to the clinic or programme.
This matters for how success is measured. As one analysis put it, most people agree that an individual's chance of having a baby after fresh and frozen embryo transfers should be described as a cumulative live birth rate, although views differ on how to calculate it.15 It also matters for safety. ASRM and the Society for Assisted Reproductive Technology (SART) note that clinics performing more elective single-embryo transfers had fewer multiple pregnancies, with no significant effect on live birth rates, and that even twin pregnancies carry significant additional risks.16 Our pregnancy guide covers the data that matters once a pregnancy begins.
What good looks like: every transfer followed to a recorded outcome, including pregnancy loss, live birth, gestational age at birth and number of babies, with a clear process for obtaining outcomes from the clinicians who provide care after the fertility clinic.
Matters most to: Fertility clinics and labs, Insurers and employers, Pharma programmes
Many people change clinics during fertility treatment, and many travel for it, to reach a specialist, a lower cost or treatment that is not available at home. Rules also shape where treatment happens. In Singapore, for example, women up to the age of 37 have been able to freeze their eggs for non-medical reasons since 1 July 2023, and only legally married couples can use frozen eggs for procreation.17, 18 Assisted reproduction in Singapore is a licensable healthcare service under the Healthcare Services Act.19
Each move leaves a record behind: stimulation protocols, embryology reports and results, sometimes in another language or format. Without them, the next clinic starts with an incomplete picture of what has already been tried.
What good looks like: records from previous clinics and countries brought into one structured history, in any language, so that past protocols, responses and outcomes inform the next cycle.
Matters most to: Fertility clinics and labs, Digital health teams
Fertility treatment is physically and emotionally demanding, and the burden itself is a reason many people stop. A systematic review of 22 studies and more than 21,000 patients found that the most common reasons for discontinuing treatment were postponing treatment (39%), the physical and psychological burden of treatment (19%), and psychological burden alone (14%).20
Validated tools exist to capture this. The Fertility Quality of Life tool (FertiQoL) was developed and tested with more than 1,400 people across five countries and has been translated into 20 languages.21 Programmes that collect only clinical data miss the signals that predict whether someone will continue, and where support would make a difference.
What good looks like: quality of life and experience captured on a validated instrument at meaningful points in treatment, stored alongside clinical data, and used to trigger support rather than only to report.
Matters most to: Pharma programmes, Digital health teams, Insurers and employers
IVF success rates are among the most quoted, and most misunderstood, figures in healthcare, because they change so much depending on the denominator. In the ICMART world report for 2018, the delivery rate using a woman's own eggs was 23.8% per egg retrieval but 31.3% per fresh embryo transfer.3 ESHRE reports a delivery rate of 15.4% per aspiration alongside a cumulative delivery rate of 32.3%.14 The same clinic can look very different depending on which figure it chooses to show.
Delivery rates from two sources, each shown against two different denominators. The figures come from different datasets and should not be compared across sources.3, 14
National registries show how consistent reporting works in practice. In the United States, the Fertility Clinic Success Rate and Certification Act of 1992 requires clinics performing ART to report data to the Centers for Disease Control and Prevention (CDC).22 In the United Kingdom, the Human Fertilisation and Embryology Authority (HFEA) has collected data on all IVF and donor insemination treatment since 1991.23 Australia and New Zealand report every ART cycle to the Australian and New Zealand Assisted Reproduction Database (ANZARD).24
What good looks like: every reported rate stating its denominator, its definition and the patient group it covers, calculated from cycle-level data so it can be recalculated for registry reporting, benefits reviews or research.
Matters most to: Fertility clinics and labs, Insurers and employers
Fertility data can reveal a person's reproductive plans, relationships, sexual orientation, donor conception and pregnancy losses. It is health data within the special categories protected under laws such as the General Data Protection Regulation (GDPR) and Singapore's Personal Data Protection Act (PDPA). Donor information adds another layer, because rules on donor identity are set nationally and can change. In the United Kingdom, anyone who registered as a donor from 1 April 2005 can be identified once a child born from their donation turns 18, and the first people eligible reached that age in October 2023.25
Many cycles do not succeed, and each one can carry grief. A negative pregnancy test, a cycle cancelled for poor response, or a loss after a positive test is part of the record, and of the person's life.
What good looks like: granular consent for each person and each use, donor information held under the rules of the relevant country, pseudonymisation for any use beyond direct care, and outcome pathways designed and tested with people who have experienced them.
Matters most to: Digital health teams, Fertility clinics and labs, Insurers and employers
Fertility benefits are increasingly offered by employers and insurers, and they raise a particular question: who can see what. Most people are comfortable with their employer knowing that a benefit exists and is used. Very few would want an employer to know that they, specifically, are having treatment, how many cycles they have had or whether it worked.
Good programme design keeps individual data with the clinic and the benefits provider, and gives employers only aggregated, pseudonymised reporting large enough that no individual can be identified. The same principle applies to insurers using fertility data for anything other than paying for care.
What good looks like: a clear separation between individual clinical data and employer reporting, minimum group sizes for any aggregated figures, consent that explains exactly who sees what, and outcomes reported with the denominators from point 10.
Matters most to: Insurers and employers, Digital health teams
The twelve points apply across fertility care, but each step brings its own data. This summary shows the core items most programmes will need, from the first assessment to storage.
You do not need to solve all twelve at once. The right starting point depends on what your data needs to do.
Teams tend to get the most from these twelve points when they use them as a working tool rather than a reading list.
If you would like support with this, the Health Data Foundations work within Jonda Health Services takes a programme through exactly these questions, from what data you hold and where it sits to what it needs before it can support analytics, AI or evidence generation. Our Experience Design work helps teams design journeys people can trust, including the moments that are hardest to get right, and our insight and analytics work helps turn a harmonised dataset into outcome measures and benchmarks your stakeholders can use.
Much of what sits between a fertility programme and a record that supports patients well is data work. It means bringing together assessment results for every partner, hormone and laboratory results, monitoring scans, embryology reports and records from previous clinics, making them usable across formats and languages, cleaning them, resolving the many ways the same thing gets captured, reviewing them clinically and mapping them to medical coding standards. This is the work we do every day.
JondaX, our health data transformation engine, turns laboratory reports, clinical documents, medical device readings and wearable data into structured, harmonised data through a single API, with other clinical data types, such as embryology reports, configured to your needs. Every dataset goes through human-in-the-loop review by clinically trained reviewers, so that the burden of checking does not fall on your team. JondaX supports more than ten languages, can be deployed and processed within your country where required, supports pseudonymisation of data for secondary use, and works with your own ontology and code sets. Jonda Health is ISO 27001 certified, and our platform is designed to comply with HIPAA, GDPR and PDPA.
At a minimum: each treatment cycle as a linked unit from stimulation to outcome, assessment results for every person contributing to treatment, hormone results with the assay and cycle day, medication protocols by day, embryology findings with the grading system used, storage and consent records, the outcome of every transfer through to live birth, and patient-reported experience on a validated instrument.
Success rates change a great deal depending on the denominator: per cycle started, per egg retrieval, per embryo transfer, or cumulatively across all transfers from one retrieval.3 Many experts describe the cumulative live birth rate as the most meaningful measure for patients.15 Every reported rate should state its denominator and definition.
ACOG advises against using anti-Müllerian hormone (AMH) to counsel women who are not seeking fertility care about their future fertility.8 AMH assays also differ in calibration, so results from different laboratories are not always directly comparable.9
The WHO laboratory manual (6th edition, 2021) gives lower reference limits, the 5th centiles, including a semen volume of 1.4 mL, a sperm concentration of 16 million per mL, a total sperm number of 39 million, total motility of 42%, progressive motility of 30% and normal forms of 4%.7 These are reference limits, not thresholds for fertility on their own.
Make it simple to record the outcome, stop any content or reminders that assume a pregnancy, avoid asking the person to repeat what happened, offer appropriate support, and keep the history accurate so that future treatment decisions are well informed.
This guide is for teams designing and running fertility services, programmes, apps and benefits. It is not clinical advice for individuals.
