Women's Health App Development

Menstrual cycle tracking with ML-based period and ovulation prediction, administered through a structured logging interface that collects cycle start and end dates, flow intensity, symptom data, and mood markers. The prediction algorithm starts with a rule-based model using the user's historical cycle length and standard deviation, then transitions to an individual ML model trained on that user's logged data as the data accumulates. Women with irregular cycles, common in PCOS, perimenopause, and post-hormonal contraceptive use, receive a prediction range rather than a point date, which more accurately represents their actual cycle variability.

Symptom and mood logging captures data across the full cycle, not just the period window: energy levels, headache patterns, skin changes, breast tenderness, and mood markers that correlate with hormonal phases. This structured data feeds the cycle anomaly detection in V3 and provides the longitudinal dataset that distinguishes a clinical-grade app from a standard consumer tracker. Push notifications remind users of predicted period dates and fertile windows without exposing health data in the notification payload, an iOS and Android requirement that most teams miss on the first implementation.

Fertility tracking for both consumer use and clinical integration. The consumer layer covers basal body temperature charting, OPK (ovulation predictor kit) result logging, cervical mucus tracking, and fertile window prediction using the sympto-thermal method. Wearable integration with Apple HealthKit and Oura Ring API pulls resting heart rate and HRV data for enhanced ovulation prediction accuracy, particularly for users with irregular cycles where standard rule-based prediction performs poorly.

The clinical layer, designed for fertility clinics and reproductive endocrinology practices, adds IVF protocol tracking: medication injection schedules with time-specific reminders for FSH, LH, and hCG trigger doses, lab result display (AMH, AFC, E2 levels from the clinic's lab system), and ultrasound measurement logging. Integration with the clinic's EMR system via HL7 FHIR resources allows lab results and clinical notes to flow into the patient-facing app without manual re-entry by clinic staff. The multi-provider coordination model, coordinating between the reproductive endocrinologist, the embryologist, and the nursing team on a single patient timeline, requires an access control model that most consumer fertility apps do not support.

Pregnancy tracking from confirmed pregnancy through the postpartum period. Week-by-week development milestones, weight and blood pressure logging, kick counter with session timing, fundal height tracking, and symptom logging for morning sickness, back pain, and oedema. Appointment scheduling integrated with the OB/GYN or midwifery practice calendar so the patient sees upcoming appointments and receives reminders without leaving the app.

Lab result and ultrasound report access for practices that choose to surface clinical documents to patients, with role-based access control separating what the patient sees from what the clinical team sees and what the partner or support person sees with explicit consent. Group prenatal education sessions for practices running group pregnancy care models, with video session infrastructure using HIPAA-compliant media. Transition to postpartum mode at the expected delivery date, triggering the Edinburgh Postnatal Depression Scale screening schedule at 2 weeks, 6 weeks, and 3 months postpartum.

Edinburgh Postnatal Depression Scale administration built into the care workflow rather than sent as a PDF attachment or a paper form at the 6-week visit. EPDS is administered as a structured digital questionnaire at configurable clinical intervals, with branching logic on question 10 (self-harm ideation) that triggers immediate clinician notification regardless of total score. Scores are calculated automatically on submission and stored against the patient record as a structured clinical observation, not a form attachment.

Session-over-session EPDS score trends display in the provider dashboard: a patient whose score was 14 at 2 weeks and 8 at 6 weeks is trending toward recovery; a patient whose score increased from 8 to 16 between appointments needs urgent follow-up. The crisis escalation path differentiates between postpartum depression with suicidal ideation, which routes to the clinician for urgent callback, and indicators of postpartum psychosis, which routes to emergency services. Postpartum psychosis requires psychiatric emergency response and cannot be managed through a telehealth escalation.

Symptom tracking for perimenopause and menopause covering the full clinical presentation: hot flash frequency and intensity, sleep disruption logging, mood and cognitive changes, vaginal dryness and discomfort, and cycle irregularity during perimenopause. Validated screening instruments for menopause symptom severity, including the Menopause Rating Scale (MRS) and the Greene Climacteric Scale, administered at configurable intervals with automated score calculation and clinician alert thresholds.

Telehealth integration for menopause specialist or OB/GYN consultations within the app, with HIPAA-compliant video sessions and structured pre-visit symptom summaries generated from the user's logged data. Hormone therapy tracking for users on HRT, with medication reminder integration and adherence logging. The data model accounts for the full perimenopause timeline, which can span 4-10 years, requiring longitudinal data retention and trend analysis across a longer period than most consumer health apps design for.

Reproductive health data requires a compliance layer beyond standard HIPAA. Post-Dobbs state laws in several US jurisdictions restrict the collection, retention, and disclosure of menstrual cycle dates, fertility information, and pregnancy status. The FTC has brought enforcement actions against period tracking apps for sharing user data with third parties without adequate disclosure. A consumer app with an ad-supported data model cannot credibly address this risk.

Our architecture response: reproductive health data stored in a dedicated AES-256 encrypted partition separate from general app data, with no third-party analytics, advertising, or attribution SDK receiving access to it. User-initiated data deletion removes reproductive health data within 30 days with a confirmed deletion audit record. Law enforcement data requests are processed against a documented policy that defaults to not disclosing reproductive health data without a valid court order reviewed by legal counsel. Business Associate Agreements are executed with every vendor that touches PHI. This architecture is not a feature add-on; it is the foundation we build on before the first product decision.