How Big Data Is Changing Women’s Health

Endometriosis, polycystic ovary syndrome and other heart diseases affecting women were understudied, misdiagnosed or dismissed for decades.

Different technologies, like big data, wearable technology, tools for capturing and interpreting data to suggest treatments like Electronic Health Records (EHRs) and Machine Learning (ML) are taking women’s health research to new frontiers.

What is Big Data in Health?

Healthcare big data may include sequences of genetic information, numerous insurance claims and hospital records, various images and data collected by wearable and smart devices and more. What makes it ‘big’ isn’t just size but it’s the volume, velocity and variety of information being generated every second across millions of patients.

Big data within women’s health relates to menstrual cycle tracking apps, fertility monitoring, pregnancy records, mammography scans and long term studies that follow thousands of women over the course of decades. This information collectively provides an overview of women’s health that was previously unavailable.

Why is Big Data So Important?

Big data matters in women’s health for a few key reasons, such as closing in on the gap between men and women health, helps reveal patterns and can even craft personalised medications:

It Corrects a Historical Data Gap

Clinical trials have historically underrepresented women, partly due to outdated assumptions about hormonal variability complicating results. Big data lets researchers retroactively analyse real-world outcomes across huge, diverse populations of women, filling in gaps that trials never addressed.

It Helps Reveal Patterns Invisible at a Small Scale

A single doctor might see a handful of patients with unusual symptoms. Aggregated across millions of records, subtle patterns, like early warning signs of ovarian cancer or links between autoimmune disease and pregnancy complications that can become statistically visible.

Large Data Sets Allow For Preventive Care

Preventative care is possible with the use of Big Data and predictive modeling, which may help identify potential risks for conditions such as preeclampsia and gestational diabetes, among other prenatal complications.

Enables Personalised Medicine

With enough data, care can move away from one-size-fits-all treatment. Algorithms can factor in a woman’s genetics, lifestyle and health history to recommend more precise treatments, whether for breast cancer therapy or hormone replacement.

What are the Four Types of Big Data?

There are four main types of big data in healthcare; structured data, unstructured data, semi-structured data and real-time data:

1. Structured Data

This is arranged data that fills in rows and columns such as lab data, electronic health records, demographic data and billing codes. Searching and analysing data of this type is easier since this data is of a consistent format.

2. Unstructured Data

This includes data that is unstructured and unformatted, such as social media posts, observations made by health care providers, patient symptoms and diagnostic images. Healthcare data that is unstructured is more voluminous and harder to analyse and requires more advanced techniques.

3. Semi-Structured Data

This is a hybrid category that has some organisational properties but isn’t as rigid as structured data. Examples include data from wearable devices, fertility-tracking apps and XML or JSON files that log health metrics with tags or metadata but don’t follow a strict tabular format.

4. Real-Time (Streaming) Data

This data is analysed as soon as it is generated and includes real-time data from health monitors that allow health care providers to make clinical assessments and interventions  including data from glucometers, heart rate monitors, or a foetal heart rate monitor during childbirth.

How is Big Data Reducing Healthcare Spending?

Big data is helping reduce overall costs of healthcare through preventative care and much more:

Earlier diagnosis means cheaper treatment: Catching conditions like breast cancer or cervical dysplasia at an earlier stage through predictive screening tools is almost always less expensive than treating advanced disease.

Reducing unnecessary procedures: Data-driven risk models help doctors identify which patients actually need invasive tests or surgeries, cutting down on costly, unnecessary interventions.

Improving chronic disease management: Conditions disproportionately affecting women, such as autoimmune disorders and osteoporosis, are expensive to manage over a lifetime. Data analytics helps optimize treatment plans, reducing hospital readmissions and complications.

Streamlining maternal care: Predictive analytics applied to maternal health records can flag high-risk pregnancies early, reducing the likelihood of expensive emergency interventions and NICU stays.

Cutting administrative waste: Big data tools also streamline scheduling, staffing and insurance claims processing, reducing the overhead costs that make up a significant share of healthcare spending.

Remote monitoring can reduce patient intake: There are a number of remote monitoring solutions that allow patients to monitor their condition and reduce their need to physically go to the hospital.