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Making menstrual cycle data count
Why cycle data belongs in medical records
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Medical records were not designed to capture menstrual cycle data, so this information is often inconsistently recorded or left out entirely.
This has consequences for both patients and the healthcare system. Without structured cycle data, doctors lack a clear history of a person’s menstrual health over time, making it harder to identify patterns across cycles. This can contribute to delays in diagnosing conditions like heavy menstrual bleeding, PMOS/PCOS, endometriosis, and PMDD. At the health system level, it also becomes harder to measure and address the scale and impact of these conditions.
Data standards are necessary, but they are not enough on their own. Healthcare systems need clear standards for how menstrual cycle data should be captured, alongside routine consideration of menstrual cycles in clinical care. Both need to evolve together.
Real-world data is a critical part of the solution. Millions of people already track their cycles through apps and wearables, generating detailed records of symptoms and patterns over time. Connecting this data to healthcare systems could support better care and research, but this requires shared data standards.
Progress is underway, but alignment is key. Efforts to define and integrate menstrual health data into medical records are emerging globally, but continued collaboration across standards bodies, clinicians, researchers, and technology platforms is needed to fully realize their potential.
Dr. Talat Uppal, a gynecologist and the director of Women’s Health Road, a clinic in Sydney, Australia, is on a mission to reduce the number of women and people with cycles experiencing heavy menstrual bleeding (HMB).
It’s a problem she sees every day—one that is common, serious, and in most cases, treatable. Around 1 in 4 women experience HMB, defined as excessive menstrual bleeding that negatively affects quality of life (1). Yet menstrual symptoms are frequently normalized, making it difficult for many people to recognize when their experiences warrant medical attention. As a result, many delay seeking support, and even when they do seek care, their symptoms may be dismissed or not prioritized (2).
Part of the challenge is that menstrual health is difficult to understand through isolated medical visits alone (3). For conditions like HMB, endometriosis, or polyendocrine metabolic ovarian syndrome (PMOS; formerly called polycystic ovary syndrome or PCOS), important symptom patterns often only become clear across multiple cycles.
But healthcare systems were not designed to collect or use this kind of information. Too often, healthcare providers rely on snapshots of a patient’s health rather than a broader view of how symptoms change across cycles, months, or years. As a result, important menstrual health patterns are frequently missed. This limits support for individuals and makes it harder to understand menstrual health trends across populations.
Why is menstrual cycle data missing from healthcare systems?
Healthcare systems are increasingly moving towards longitudinal patient records. The goal of this is to bring together a person’s health information over time, across different providers, and across life stages.
In practice, however, health records are still often fragmented across systems, and “longitudinal” can mean little more than a collection of past healthcare interactions arranged in chronological order.
This information is not always organized in a way that helps healthcare providers identify trends and changes in health over time. Even in areas where longitudinal information already exists, such as repeated laboratory results, the information can still be difficult to access, connect across systems, or present in ways that are clinically meaningful.
Part of the reason is that medical records were not originally designed to capture evolving patterns. They were built around healthcare encounters: what happens during a visit, what is diagnosed, and what is prescribed. That approach works well for many aspects of medicine, but it is a poor fit for dynamic health signals like menstrual cycles.
Importantly, menstrual cycles are not only central to reproductive health; they can also provide insight into overall health (3). The menstrual cycle is closely linked to many body systems, including metabolic, immune, cardiovascular, and mental health processes.
Unexpected changes in cycle patterns or menstrual characteristics, such as irregular cycles, severe pain, or heavy bleeding, may indicate an underlying condition or be associated with future health risks, including anemia, metabolic disorders, or cardiovascular disease (4-7). Changes in the menstrual cycle can therefore provide important clues about a person’s broader health.
Despite this, healthcare systems still lack a standardized way to consistently capture and use menstrual cycle information. Part of the challenge lies in how health data is organized. International standards developed by organizations such as HL7 help determine how health information is recorded, categorized, and shared across different healthcare systems.
This standard includes frameworks such as FHIR (Fast Healthcare Interoperability Resources), which help define how health information is structured and exchanged between systems, as well as clinical terminologies that provide standardized names and codes for symptoms, conditions, and other health information.
Historically, menstrual cycle data has had little representation within these standards. Beyond isolated data points, such as the date of a person’s last menstrual period, cycle-related information is often recorded inconsistently, buried in clinical notes, or not captured at all.
How does missing menstrual health data affect diagnosis and treatment?
This is not just a documentation issue—it is a healthcare issue.
Modern healthcare relies on structured data to support care, research, population health efforts, and increasingly, AI-powered tools. Information that is consistently captured can be measured, analyzed, and acted on. Information that isn’t often remains invisible.
This has real consequences. When cycle-related symptoms or conditions are underdocumented, people may be more likely to have their concerns dismissed, repeat their health history across multiple providers and visits, and experience delays in diagnosis or treatment (2). This is one reason why conditions like HMB can be both common and underrecognized.
For clinicians like Dr. Uppal, missing data creates another challenge. Even when patterns are visible in individual patients, they are much harder to measure across a practice, healthcare system, or population if they are not recorded in a standardized way. This makes it difficult to answer fundamental questions, such as: How many patients are experiencing HMB? What does a typical diagnostic journey look like? Which treatments are leading to the best outcomes?
Without consistent data, healthcare systems also have fewer opportunities to learn from their own collective experience over time. Patterns that could inform better care, research, and policy decisions remain difficult to identify.
When menstrual health data is missing, it reinforces the women’s health gap by limiting what healthcare systems can understand, measure, and ultimately improve.
The core challenge: standards and clinical practice must evolve together
This is where the challenge becomes more complex.
Creating data standards alone will not ensure that menstrual health information is routinely collected or used in care. At the same time, despite growing evidence that cycle experiences can provide important insight into broader health, menstrual cycle-related data is still rarely incorporated into routine clinical practice. As a result, there is limited agreement around which data points, trends, or patterns are most clinically meaningful and how this information should be interpreted or used to support care.
This creates a chicken-and-egg problem. Without clear clinical use cases, it is difficult to determine what menstrual health data should be collected and how it should be structured. But without structured data, healthcare systems have a limited ability to consistently capture, evaluate, or learn from these patterns at scale.
Improving outcomes will therefore require data standards and clinical practice to evolve together. Standards can help make menstrual health data more accessible and usable, while routine use in clinical care can help determine which information is most valuable and how it should inform decision-making.
Defining the data: early efforts to close the gap
Efforts are already underway to address these gaps.
Dr. Uppal is working with Sparked in Australia, a national program that develops FHIR-based clinical data specifications, to help define how menstrual health information can be captured and used within medical records.
As part of this work, Sparked is expanding the women’s health component of the Australian Clinical Data for Interoperability (AUCDI) to include a more comprehensive gynecological history, with an initial focus on menstrual bleeding. The initiative brings together clinicians, researchers, and community members to determine what information should be collected and how it can be incorporated into healthcare systems in a consistent and meaningful way.
Although this work is taking place in Australia, similar efforts are emerging around the world. Because many of these initiatives are built on shared frameworks such as FHIR, lessons learned in one country can help inform how menstrual health data is captured and used elsewhere.
These conversations are also continuing through international standards communities and forums such as FHIR DevDays, helping build momentum toward a more consistent approach to menstrual health data across healthcare systems.
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Can period tracking apps help improve healthcare?
Importantly, while healthcare systems are working out how to capture and use menstrual health information, millions of people are already generating detailed cycle records every day.
Apps and wearables like Clue allow people to track symptoms, cycle timing, and changes over time, creating longitudinal records that often do not exist in clinical systems.
When available, self-tracked data can provide a more complete picture of a person’s experiences, reducing reliance on memory and helping identify patterns that might otherwise go unnoticed.
The challenge—and opportunity– is to translate these lived experiences into information that clinicians can use. To do that, menstrual health data needs to be integrated into medical records in ways that support decision-making without creating unnecessary complexity or administrative burden.
This data also has value beyond individual care. Cycle tracking apps like Clue make it possible to analyze menstrual health data across large and diverse populations, generating new insights into how menstrual cycles relate to overall health.
Research has shown, for example, how environmental factors like air pollution may influence cycle patterns and how insulin sensitivity can vary across the menstrual cycle.
At both the individual and population level, this data becomes most valuable when it can be connected to healthcare systems. Doing so can help clinicians identify patterns earlier, support research at scale, and enable healthcare systems to make better use of existing health information.
How can healthcare systems use menstrual cycle data?
Improving care requires closing a fundamental gap: menstrual cycles are still not meaningfully integrated into healthcare.
Addressing this gap requires more than adding another field to a medical record. It means recognizing menstrual cycles as valuable health information and building the systems, standards, and clinical practices needed to support their use in care.
When that happens, healthcare can move beyond isolated snapshots toward a more complete understanding of health across the lifespan. Patterns that are currently overlooked can be identified earlier, helping healthcare providers recognize conditions such as HMB, endometriosis, and PMOS sooner. It can also provide a clearer picture of how these conditions affect populations, helping inform research, healthcare planning, and future improvements in care.
The menstrual cycle is more than a reproductive health metric; it is an important indicator of overall health. When healthcare systems can consistently recognize and use that information, they will not only improve menstrual health care; they will improve healthcare itself.
