Participants
Start with story, receive a personal output, and decide whether anything contributes to community or research learning.
For research clients and evidence teams
The listening layer for Integrated Evidence Plans.
PatientStories.ai helps teams learn from health in the wild before assumptions harden into protocols, evidence plans, support programs, and research questions.
Patient value first.
Governed signals.
Aggregated insight.
PatientStories.ai does not position patient stories as a raw-data product. The client value is governed, anonymized, aggregated research insight built from participant-reviewed signals and appropriate consent.
Two conversations, one trust model
Participants get useful outputs. Clients get governed insight.
The participant conversation leads with Health Biography and Doctor Visit Reports. The client conversation leads with evidence gaps, patient-originated signals, cohort learning, and research-ready questions.
Communities
Receive anonymized reflections: patterns, failure points, workarounds, questions, and “people like me” lenses.
Research clients
Receive aggregated insight, signal maps, structured-question candidates, evidence-gap summaries, and recommendations for deeper study.
LEAN patient-signal intelligence
Listen. Extract. Analyze. Navigate.
LEAN is the operating frame for turning open narrative into useful evidence-planning signals without pretending that stories are already formal evidence.
Listen
Start with least-friction text or voice narrative intake and recent lived moments.
Extract
Use AI-assisted decomposition to identify concepts, context, burden, timeline, workarounds, questions, and candidate fields.
Analyze
Compare participant-reviewed signals across cohorts, subgroups, community lenses, and emerging themes.
Navigate
Turn signals into better prompts, structured survey follow-ups, evidence-gap summaries, advisory questions, and next-step research priorities.
Integrated Evidence Plans
IEPs need a lived-experience listening layer.
PatientStories.ai can sit upstream of evidence planning, protocol design, Phase IV commitments, patient support, medical affairs, advocacy partnerships, and real-world research prioritization.
Before protocol assumptions harden
Identify burdens, expectations, support gaps, and practical realities that may affect endpoints, schedules, visits, retention, and adherence.
Before patient support is designed
Reveal what people are actually struggling to manage: side effects, logistics, emotions, financial friction, family burden, and workarounds.
Before evidence gaps are finalized
Convert patient-originated signals into better questions for RWE, Phase IV, advisory boards, medical affairs, advocacy, and community research.
Collaborative research design concepts
Start broad, then structure what the community reveals.
The model supports open narrative intake, participant review, Me + Community lenses, public or private dashboards, signal-based deeper dives, and data-driven structured surveys that follow the signal rather than pre-empt it.
Open first
Least-friction narrative
Participants begin with text or voice. They are not forced into a chronology or rigid survey sequence before the signal appears.
Structure second
User-reviewed decomposition
AI extracts themes, variables, questions, and answer-set candidates for participant review, edit, approval, deletion, or research submission.
Deepen next
Signal-based follow-up
Aggregated patterns can generate targeted structured surveys, cohort filters, dashboards, and research questions for deeper investigation.
Client outputs
What research clients can use.
Community signal reports
Aggregated themes, patient language, burden maps, workarounds, and representative de-identified patterns.
Structured field candidates
Variables, answer sets, follow-up prompts, and survey modules derived from open story patterns.
Evidence-planning briefs
Patient-centered summaries for IEP teams, protocol design, medical affairs, Phase IV planning, and support-program strategy.
Me + Community lenses
Filters and dashboards that show subgroup experience without exposing individual narratives.
Feasibility feedback
Participant-informed feedback on burdens, logistics, comprehension, recruitment expectations, and operational friction.
Research-grade pathways
When appropriate, the architecture can support governed observational research, IRB oversight, and defined analytic plans.
Not raw data sales. Not ungoverned AI analysis.
PatientStories.ai separates raw user narrative, user-reviewed semantic structure, personal Health Biography outputs, anonymized community reflection, and client-facing research insight.
The model is designed to preserve participant trust while making lived-experience signals useful to teams that plan evidence, design studies, support patients, and ask better research questions.
Trust boundary
The participant-facing promise remains personal value first. Client outputs are aggregated, governed, anonymized, and consent-aware.
Research client contact
Build the first listening project, then let the data speak.
Use this path for research insight, sponsor partnerships, advocacy collaborations, LEAN signal generation, Integrated Evidence Plan support, or feasibility feedback.