<!-- REVIEW CHECKLIST: [ ] Reviewed by patient advocate [ ] Reviewed by oncology nurse [ ] Approved by PatientOne — do NOT publish without all three checkboxes filled -->

# Frequently Asked Questions for Families and Caregivers

## Q: What is this platform and who built it?
**A:** This is an open-source precision oncology research platform built to analyze
High-Grade Serous Ovarian Cancer (HGSOC) tumor data using AI. It was created by a
team of researchers and engineers, inspired by PatientOne -- a real HGSOC patient whose
courage and story motivated this work. The analysis data (PAT001) is entirely
synthetic and does not contain PatientOne's actual clinical information. See
ACKNOWLEDGMENTS.md for the full story. The platform is publicly available at
https://github.com/lynnlangit/precision-medicine-mcp.

## Q: Is this platform approved by the FDA?
**A:** No. This is a research tool, not an FDA-cleared medical device. FDA clearance
means a regulator has reviewed clinical evidence that a tool is safe and effective for
a specific clinical use. This platform has not undergone that review process. All results
must be reviewed by a licensed oncologist before informing any treatment decision.

## Q: Is the patient data in this project real?
**A:** No. All data in this repository -- including PAT001 -- is synthetic. It was
designed to be clinically plausible for teaching and research purposes, but it does
not come from any real patient's medical records, genetic tests, or tissue samples.
PatientOne inspired the project; her actual clinical data is not in this system.

## Q: How is my family member's data protected?
**A:** If this platform were deployed in a hospital setting, it would operate with
HIPAA-aligned principles: all analysis would use de-identified data, access would be
logged in an audit trail, and no Protected Health Information (PHI) would leave the
hospital network unmasked. The demo and teaching modes use only synthetic data. See
docs/for-hospitals/ for compliance documentation.

## Q: Can this replace my oncologist?
**A:** No. This platform is decision-support software -- it surfaces patterns in data
to help oncologists form hypotheses. Every output requires clinical interpretation,
integration with the patient's full medical history, and professional judgment.
The platform itself says so explicitly in every report it generates.

## Q: What does "precision medicine" mean for HGSOC specifically?
**A:** HGSOC tumors vary widely from patient to patient at the molecular level.
Precision medicine means analyzing each patient's unique tumor profile -- its DNA
repair defects, mutations, immune environment -- to guide therapy choices rather than
applying a one-size-fits-all treatment. This platform automates that multi-layered
analysis.

## Q: How accurate are these predictions?
**A:** The platform has been validated against synthetic data (PAT001) and all 18 AI
servers produce reproducible results. It has not yet been validated against a real
clinical HGSOC cohort. Confidence intervals and concordance rates with oncologist
judgment have not yet been formally established. This is research-grade software.

## Q: What happens next after this analysis?
**A:** In a clinical deployment, the structured report generated by the platform would
be presented to the oncology team. They would integrate it with pathology reports,
imaging, prior treatment history, and clinical guidelines before discussing options
with the patient. The AI output is one input among many -- not a final answer.

## Q: How can our family contribute to future research?
**A:** You can contribute by: participating in data-sharing consent programs at your
cancer center; supporting patient advocacy organizations such as the Foundation for
Women's Cancer (foundationforwomenscancer.org) and the Ovarian Cancer Research
Alliance (ocrahope.org); and encouraging your oncology team to explore the platform.
The code is open-source and welcomes contributions from developers and scientists.

## Q: Where can we learn more?
**A:** These resources may help:
- This repository's patient docs: docs/for-patients/
- Foundation for Women's Cancer: https://foundationforwomenscancer.org
- Ovarian Cancer Research Alliance: https://ocrahope.org
- National Cancer Institute HGSOC: https://www.cancer.gov/types/ovarian

## Q: How can we thank the team?
**A:** Read ACKNOWLEDGMENTS.md -- it tells the full story of PatientOne, whose courage
and participation inspired every line of this project. Sharing this work with others
who might benefit, or opening a GitHub issue with feedback, means more than you know.

---

*This is not a clinical report. Discuss all findings with your oncology team.*
