My Story
A plain-language biography of what happened, what changed, what was hard, what helped, and what the person wants others to understand.
Health in the wild, organized for you
Turn your life and health story into a biography you can use.
PatientStories.ai helps people turn open stories into useful Health Biography outputs — a life-and-health narrative, timeline, doctor visit summary, family version, and personal archive built from their own words.
Your story first.
Useful outputs back.
Research only with consent.
PatientStories.ai starts with the person — not a survey, not a portal field, and not a sponsor question. Open stories become useful biography outputs first; permissioned cohort insight comes second.
Lead participant offer
Health Biography gives people something useful back from their own stories.
Most health tools ask people to give information away. PatientStories.ai starts with open narrative and returns practical biography outputs the participant can keep, edit, download, and choose how to share.
My Timeline
A longitudinal life-and-health record of symptoms, diagnosis, treatment decisions, turning points, setbacks, adaptations, and context over time.
My Visit Summary
A concise care-facing version with recent changes, concerns, questions, treatment issues, and what the person wants the clinician to understand.
My Family Version
A warmer version for family, caregivers, school, work, or support partners who need to understand what daily life is really like.
My Personal Archive
A durable record for posterity: what the person endured, learned, managed, lost, gained, and wants remembered.
My Opportunity Version
A participant-controlled foundation for research invitations, advocacy, community projects, author discovery, benefits preparation, or public storytelling.
How it works
Open intake first. Biography output second. Research only by choice.
PatientStories.ai is built around a simple idea: people should not have to fit their experience into a checkbox before their experience can matter.
The platform starts with what the person remembers clearly across life and health, adds lightweight context when useful, and organizes the material into outputs the participant can review, edit, download, and choose to share.
Start with an open story
A recent moment, a hard week, a treatment change, a side effect, a workaround, a question, or something no one seems to understand.
Add light context
Simple follow-ups can capture timing, treatment context, burden, support, and a Happy Score-style snapshot of how the person is doing in the moment.
Create biography outputs
The same source story can support different versions: life-and-health biography, doctor visit summary, caregiver handoff, personal archive, community learning, or research submission.
Participant remains in control
Raw stories remain preserved. Derived Health Biography outputs are editable, purposeful, and shareable only according to the participant’s choices.
User value
Make the story useful: save time, reduce cost, improve life.
The first value of PatientStories.ai is not research. It is helping people turn scattered life and health experience into a practical biography they can use in the real world.
Prepare for care
Use a one-page summary, timeline, and question list to make short appointments, specialist visits, second opinions, and follow-up conversations more productive.
Stop rebuilding the story
Reduce repeated explanations to clinicians, family, caregivers, schools, employers, insurers, or support partners by keeping reusable versions of the same lived experience.
Open future opportunities
A clear biography can support paid research invitations, advisory work, advocacy, benefits preparation, writing, speaking, or community projects when the participant chooses.
30-day listening projects
A campaign can ask the question. The intake stays wide open.
Listening projects are time-bounded community learning cycles. Outreach may invite a theme, but participants still share lived experience in their own words. After the listening period closes, PatientStories.ai reports back with the patterns, failure points, workarounds, and lived realities the community described.
Participant promise
A return, not a black hole
People who share their stories should get something back: a Health Biography output, a clearer version of their own experience, and a community report showing what others are living through.
- Open narrative intake
- Lightweight follow-up prompts
- Community report after 30 days
Cohort example
T1D in the wild
A 100-person listening project could capture recent diabetes moments that did not go as expected: lows, highs, food surprises, pump or CGM issues, school, work, sleep, exercise, caregiver friction, or burnout.
- Recent scenes
- Failure points
- Community learning
Cohort example
Life on treatment
For therapies such as GLP-1s, listening projects can surface patient-reported burden, workarounds, tolerability context, social embarrassment, dose-change challenges, and persistence pressure.
- Treatment burden
- Workarounds
- Persistence signals
Research and partner value
Biography first. Cohort insight second.
PatientStories.ai is designed to return value to participants first. With appropriate consent, selected stories and biography-derived themes can also contribute to anonymized, aggregated insight reports for advocacy organizations, researchers, medical affairs, clinical development, safety, commercial, and operations teams.
Cohort Signal Reports
Curated summaries of patient experience patterns, burden signals, unmet needs, subgroup differences, and community-described failure points.
Patient Language Intelligence
The words people actually use to describe symptoms, tradeoffs, confusion, relief, embarrassment, burden, adaptation, and daily workarounds.
Health Biography-Derived Themes
Participant-reviewed biography outputs can help reveal longitudinal patterns, care gaps, treatment turning points, and what people want healthcare to understand.
Fully Matrixed Cohort Maps
Structured maps across treatment journey, burden, behavior, support, access, care friction, emotional load, and practical adaptation.
Protocol and Product Risk Briefs
Patient-centered summaries that flag design assumptions likely to create burden, confusion, disengagement, or avoidable rework.
Better Questions
Open stories reveal what should be asked next, helping future structured prompts, surveys, cohorts, and research instruments become smarter.
Safety-aware patient intelligence
Built outside the trial stack. Informed by real-time clinical operations.
PatientStories.ai is not intended to replace a sponsor’s regulated clinical, safety, EDC, eCOA, pharmacovigilance, or trial master file systems. That separation is intentional.
From biometric telemetry and Clinical Decision Support to lived-experience signal.
The roots of PatientStories.ai trace back to Diabetech and early work in remote biometric telemetry, home-based disease management, Clinical Decision Support, and real-time patient-generated data workflows.
That work proved that meaningful patient signals do not have to wait for the next office visit. PatientStories.ai extends the same straight-through processing mindset into patient experience: capture the experience close to the moment it happens, preserve context, structure the signal, and make patterns visible sooner.
Biometric telemetry can show what changed. Clinical Decision Support can help organize what action might be considered. Patient narrative explains what it meant — the burden, behavior, care friction, tradeoffs, support gaps, emotional load, and daily decisions behind the signal.
Current position
The architecture could support prospective, real-time observational research under appropriate governance, including IRB oversight where required. That is not the default commercial model. PatientStories.ai is positioned as a safety-aware patient-experience intelligence layer outside the regulated system-of-record stack.
Safety Signal Awareness
Recognize when narratives may contain safety-relevant content, including possible AEs, SAEs, product complaints, worsening symptoms, or treatment-related concerns.
Human Review Pathways
Use AI-assisted detection to support structured review and escalation logic while keeping safety-sensitive interpretation under human oversight.
Data Integrity & Traceability
Design around consent clarity, timestamps, source preservation, version control, audit-conscious workflows, and responsible handling of patient-submitted content.
Standards-Informed Design
Informed by the principles behind the Declaration of Helsinki, GCP, ICH E6(R3), FDA 21 CFR Part 11, patient data safety, and practical patient journey UX.
Trust model
Your story belongs to you. We sell insights — never patient data.
PatientStories.ai is built around a simple boundary: participants share stories to create useful Health Biography outputs and, if they choose, to help their community reveal patterns, gaps, burdens, and unmet needs.
Partners receive curated, privacy-respecting insight outputs — not identifiable patient data, raw story exports, or participant-level datasets.
Who is behind this? PatientStories.ai is led by Kevin L McMahon. His professional background is available as a public evidence site so partners can evaluate the operating history, remote monitoring lineage, clinical operations experience, and credibility behind this effort.
Participant Value FirstHealth Biography outputs give people something useful back from their own stories.
Research Insights, Not Data SalesCurated intelligence is the product. Identifiable patient data is not.
Clear consentPlain-language participation and use expectations.
Aggregated outputsInsight reports and cohort summaries, not raw identity exposure.
No raw exportsNo default handoff of raw narratives or participant-level datasets.
Contact
Build a biography-first listening project.
For patient communities, advocacy partners, researchers, and sponsor teams interested in Health Biography outputs, wide-open narrative intake, 30-day listening projects, or patient-originated insight.