The ProbLem
Health AI is only as good as the data it learns from. Right now, that data excludes most of the world.
Clinical AI models are trained predominantly on data from white, male, Western patients. Women are diagnosed later, treated with less precision, and excluded from the datasets that power modern medicine. The data to fix this exists - in apps, wearables, labs, and clinics. But it remains fragmented, untagged, and inaccessible.
25%
Women spend 25% more of their lives in poor health than men - driven by research bias, poor data, and a medical system designed around male physiology.
WEF & McKinsey Health Institute, 2024
WEF & McKinsey Health Institute, 2024
20-30%
Women and ethnic minorities are 20-30% more likely than white men to receive a misdiagnosis - an estimated 795,000 deaths or permanent disabilities per year in the US alone.
Johns Hopkins / BMJ Quality & Safety, 2023
96%
In 96% of cases where drugs metabolize differently in women, this results in significantly higher rates of adverse side effects. Women are routinely overmedicated.
University of Chicago / Biology of Sex Differences, 2020
The Solution
The data infrastructure layer that health AI has been missing.
We aggregate, structure, and surface health data from underrepresented populations - tagged by sex, ethnicity, age group, and geographic origin - delivering AI-ready, GDPR-compliant datasets to pharma, medtech, and clinical research clients.
Data aggregation
Wearables, health apps, clinical sources, and diagnostic tools - connected via a reciprocity model. Partners contribute anonymized data in exchange for access to enriched datasets.
Clinical tagging
Every dataset tagged by sex, ethnicity, age group, and geographic origin using OMOP CDM and HL7/FHIR standards. EHDS-compatible from day one.
Correction Factor
Our first billable product: a statistically derived adjustment model that corrects existing treatment protocols and diagnostic tools by sex and ethnicity.
Trust by design
Federated learning options, full audit trails per dataset, and user consent architecture embedded at source. GDPR-compliant infrastructure built for regulated healthcare environments.
Join the research
Are you working in health tech, femtech, or clinical data?
We aggregate, structure, and surface health data from underrepresented populations - tagged by sex, ethnicity, age group, and geographic origin - delivering AI-ready, GDPR-compliant datasets to pharma, medtech, and clinical research clients.
Health Data Intelligence Survey
5 minutes. Anonymous. Covers how you collect, use, and share health data today - and where the gaps are. Participants receive early access to survey results and can request a pilot partnership.
Already completed by founders and Manager from Clue, wild.ai, FEMNA Health, OCON Therapeutics, Wellster, and more health tech companies across Europe.
Get in touch
Interested in a pilot partnership or data collaboration?
Whether you are a health tech company, clinical institution, pharma team, or potential advisor - we want to hear from you.
Direct contact
Prefer to reach out directly or book a call?
Responsible: Frederike Engel
Email: hello@phima.tech
Phone:+49 176 80107529
Location: Valencia, Spain & Germany