Every stakeholder turns the patient journey into intelligence
The same longitudinal, anonymized, aggregate patient-journey spine, read differently by each stakeholder. Here is what each one gets, concretely.
Pharma & life sciences
Real-world evidence on treatment adoption, persistence, switching and outcomes across the patient journey.
Real-world treatment adoption and line switching, who moves from chemo to targeted therapy, and when.
Persistence and adherence curves across months of therapy, fully anonymized.
Paraphrased voice-of-patient, concerns, side effects and needs, as an aggregate qualitative signal.
Medical centers & hospitals
Operational clinical intelligence for care teams, who to call first, where the risk is, how to prioritize.
Prioritization queues, who to call first by deterioration signals, with no patient identity exposed.
iwCLL quality adherence and gaps against the treatment protocol.
Aggregate AE / safety signals and trial-enrollment lists, anonymized.
Insurers & payers
Real cost-effectiveness, utilization patterns and outcomes, an evidence base for coverage decisions.
Real cost-effectiveness signals from real-world outcomes, not model assumptions.
Utilization patterns and detection of waste and sub-optimal use.
Value-based-care evidence and coverage / basket decision support.
Government health departments
Aggregate population view for policy-making, resource planning and national quality assessment.
A national population quality picture, where the system excels and where gaps remain.
Policy and resource planning grounded in aggregate, regulation-aligned outcomes.
Tracking national outcomes over time, aggregate and identity-free.
Health funds (Kupot Holim)
Insights into the member journey, early intervention and patient-experience improvement across the continuum.
Member-journey insight, where members drop off, wait, or progress.
Early-intervention flags before the next visit, from longitudinal signals.
Patient-experience metrics to improve the care and service continuum.
Researchers & academia
Longitudinal, anonymized, precise cohorts for real-world outcomes research questions.
Longitudinal anonymized cohorts, ready for real-outcome research questions.
Patient-reported-outcome (PRO) data, continuous and at scale.
Real-world outcome endpoints for use in research and publication.
Eight intelligence layers that feed every use case
Every use case above draws from the same longitudinal spine. These are the layers.
Real-world patient signals
Symptoms, emotional state and adherence, captured continuously, with consent, anonymized.
Longitudinal journey intelligence
From diagnosis through watch-and-wait and treatment to MRD-negative and retreatment.
Emotional & behavioral monitoring
Distress and behavior shifts as an early proxy for clinical change.
Personalized education
Evidence-based content by journey stage, in the patient's language.
Clinical-trial awareness
Eligibility matching and surfacing relevant research pathways.
Early-intervention signals
Lymphocyte doubling, hemoglobin decline and iwCLL triggers.
Aggregated health-org intelligence
An anonymized picture for pharma, hospitals, payers and government.
Continuous RWE generation
Every interaction becomes aggregate evidence, with no PII.
Every use case, aggregate, anonymized, no drill-through to a single patient
No use case ever exposes a patient identity. A k≥10 cohort floor, no PII, and every access logged.