Why disease flares, variability, and treatment response are difficult to understand in autoimmune conditions

Understand how autoimmune disease progression, flare dynamics, treatment experience, and outcomes unfold in the real world, including symptoms, function, and behavior that are not captured in claims or clinical data.

Autoimmune disease progression and flare dynamics are not well understood

Autoimmune conditions are defined by variability—fluctuating symptoms, unpredictable flares, and heterogeneous responses to treatment—but real-world disease patterns remain difficult to characterize. Life sciences teams can track diagnoses and treatment, but lack visibility into how disease activity and patient experience change over time.

Claims and clinical data show what happened. They don’t explain:

• How flares emerge, evolve, and resolve between clinical visits
• How symptoms and function vary within the same individual over time
• How patients respond to treatment in real-world settings
• What drives disease burden and variability across populations
• How disease activity and symptom burden evolve between clinical visits and across real-world settings

Questions life sciences teams are asking about autoimmune conditions

Life sciences teams across research, medical, and commercial functions are trying to understand what drives disease state and outcomes

• What triggers autoimmune disease flares in the real world?
• How do symptoms and functional impact change over time?
• How do patients respond to treatment outside clinical settings?
• What biological signals are associated with flare onset and resolution?
• How does real-world disease variability differ from clinical trial assumptions?

A new way to better understand autoimmune disease progression

Evidation connects directly with individuals living with autoimmune conditions, generating continuous, multimodal real-world data across symptoms, function, treatment experience, and biological signals.

This enables:

• Longitudinal tracking of disease activity within the same individuals
• High-frequency insight into symptoms, flares, and functional impact
• Triggered biospecimen collection at the time of disease activity
• A more complete understanding of treatment response beyond clinical settings

Data for a better understanding of autoimmune conditions

Through continuous, direct engagement with individuals, Evidation captures longitudinal, patient-level real-world data across symptoms, behavior, and biology, integrating patient-reported outcomes, digital measures, clinical data, and biospecimens collected at key moments such as flares.

Who patients are

• Age, sex, gender identity, race and ethnicity
• Income, education, employment, and household structure
• Insurance type, out-of-pocket costs, affordability perception
• Access barriers including transportation, food, and care access

What patients experience

• Daily flares, pain, fatigue, and disease activity captured through high-frequency patient-reported outcomes
• Validated instruments (e.g., HAQ-DI, BASDAI) to quantify symptom severity and variability
• Within-patient variability in disease experience over time

What patients do

• Activity levels, sleep, and heart rate captured through connected devices
• Impact on work productivity, participation, and daily function
• Behavioral patterns associated with disease activity

What shapes treatment experience and response

• Medication history, persistence, and adherence patterns
• Switching behavior and patient-reported treatment response
• Real-world treatment experience across therapies

What biological signals are associated with disease activity

• Biospecimen collection during flare events
• Biomarker data linked to symptom dynamics and disease activity
• Integration of molecular and patient-reported data

Why disease burden varies across patients

• Differences in symptom severity and progression
• Variability in treatment response
• Impact on quality of life and daily function

What clinical data adds

• Diagnoses, comorbidities, and disease history from clinical records (EHR)
• Procedures, specialist visits, and treatment history
• Healthcare utilization patterns across clinical settings

Real-world outcomes and experience

• Disease progression and flare patterns over time
• Functional impact and quality of life
• Long-term treatment response and disease control