Ashwam is a personal health platform for women that consolidates daily observations, lab results, and genetic data into a single, longitudinal baseline. Users journal symptoms in their own words, which the system converts into a clinical summary that can be shared with clinicians while keeping the full journal private. The platform’s data framework contextualizes biomarkers against each user’s unique history to help detect and track health changes over time.
Funding
Funding not disclosed
Founders
Product
Problem
Women’s health data is fragmented across multiple apps and devices, and most clinical risk models rely on population averages that do not reflect an individual woman’s biology, especially during midlife and post‑reproductive years. This makes it difficult for women to recognize personal health shifts and to share meaningful information with clinicians.
Solution
Ashwam is a personal health platform that creates an N‑of‑1 longitudinal health record for each woman. Users spend five minutes a day entering observations across eight life‑domains, while the system also ingests wearable signals, lab results, and genetic data as they become available. All data are contextualized against the individual’s own baseline, allowing the platform to detect drift in physiological and symptomatic patterns before they become overt problems. The platform automatically generates a clinical summary that the user can selectively share with healthcare providers, keeping the raw journal private. Over time, aggregated, consented records form an evidence base for women‑specific prediction models, improving prevention and research. The system is designed to work with or without wearables or lab tests, ensuring accessibility for a broad population of women.
Target Audience
Ashwam is aimed at women—particularly those in midlife, perimenopause, and post‑menopause—who want a unified view of their health data and actionable insights for themselves and their clinicians. It also serves healthcare providers seeking personalized, longitudinal health information for female patients.
Features
- Five‑minute daily check‑in covering body, mind, emotion, sleep, food, activity, social and environmental factors to build a personal baseline.
- Integration of wearable metrics (HRV, resting heart rate, skin temperature, sleep architecture, CGM) from devices such as Apple Watch, Oura, Garmin, Fitbit, etc.
- Ability to import laboratory results (lipids, glucose, hormonal panels, inflammation markers) that recalibrate the individual baseline.
- Incorporation of genetic information (polygenic risk scores, pharmacogenomics, family history) with separate consent controls.
- Rolling 56‑day moving baseline window that continuously updates and is robust to outliers.
- Automated generation of a clinician‑ready summary that users can share selectively while retaining full ownership of their journal.
- Consent‑driven aggregation of anonymized N‑of‑1 records to create a women‑specific evidence base for future risk‑prediction models.
- Multi‑population support, ensuring relevance for diverse ethnic and geographic groups.