Use cases /
Aid Clinical Trials
Environmental data for every stage of a trial, in one place
From site selection to submission, environmental conditions greatly shape trial outcomes. Ambee helps at every stage.
Trusted Across Industries
From site selection to submission, environmental conditions greatly shape trial outcomes. Ambee helps at every stage.
Multi-site trials span geographies with different pollen, air quality, temperature, and seasonal profiles. In respiratory, allergy, dermatology, and inflammatory indications, these conditions function as extrinsic covariates. They influence symptom burden and patient-reported outcomes, shift symptom severity within assessment windows, and contribute to inter-site variability. Left unaddressed, that variability can surface in primary endpoint data.
Across underwriting, claims, and portfolio decisions
Ambee provides historical environmental baselines, helping trial teams compare sites against the allergen, air quality, and climate conditions most likely to influence study endpoints.
Ambee provides multi-year seasonality patterns across sites, helping researchers schedule enrolment and assessment visits during historically stable exposure windows.
Environmental exposure conditions can shift significantly during an active study. Ambee helps teams continuously monitor pollen, AQI, wildfire smoke, and severe weather conditions across investigational sites throughout the trial period.
Ambee provides a traceable environmental exposure record with site-level statistical baselines that can be integrated into endpoint interpretation and regulatory documentation.
Dermatology, respiratory, and allergic disease trials measure endpoints that respond to ambient conditions. Symptom severity, flare frequency, and patient-reported outcomes can shift with pollen, air quality, temperature, and humidity, complicating efficacy interpretation across sites.
Ambee mapped each participant's location against historical, present, and forecast exposure at the allergen and pollutant level, building a longitudinal exposure profile tied to participant ID,
That exposure profile can support:
Regression models with environmental covariates
Lag-effect analysis on symptom outcomes
Exposure-based cohort stratification
Site-level comparability checks across regions
Across sites in Europe, North America, Japan, and the tropics, this reduced confounding and supported cleaner efficacy interpretation.
