Digital health has plenty of data and analysis, but a less developed understanding of how that information should change the support each person receives.
We’re developing Pattervia to connect longitudinal health data to care decisions: who needs support, what kind, and when.
Longitudinal data: Wearables, symptoms, clinical records and interaction history feed into measurement and interpretation.
Measurement and interpretation: Features, personal baselines and state estimates inform decisions.
Decision: Who needs what support, and when? This informs delivery.
Delivery: A coach, clinician, app or treatment device delivers the intervention.
Observed outcomes: Behavior, symptoms, function and resource use feed into evaluation.
Evaluation: Did the intervention cause an improvement?
A separate branch connects measurement and interpretation to research measurements—biomarkers and trial endpoints—which also feed into evaluation. A dotted feedback arrow returns from evaluation to decision, labeled “Evidence to improve decisions.”
ILLUSTRATIVE EXAMPLE
Participant 014 · a change in participation
01 Previous pattern
Consistent check-ins and regular replies to their coach.
02 Recent change
Fewer check-ins and slower replies. Their latest message mentions new working hours.
03 Question for the coach
Could the new schedule be making participation harder?
An illustration of the proposed approach, not a live product result. Your team interprets the context and decides how to respond.
Research behind the approach.
Pattervia is being developed by Adi Choi, a final-year PhD student researching how longitudinal behavioural data can reveal individual patterns and changes over time.
Her publications include:
npj Digital Medicine ·
Personalised modelling of routine variability and affective states
These publications inform the approach. Our first evaluations will test whether that research translates into useful support decisions. Our longer-term aim is to help digital care teams use longitudinal context to make support more responsive across conditions.
Looking for design partners
We’re looking for digital care providers with an existing coaching or support team who want to improve program completion.
Together, we’ll evaluate one decision: which participants need a check-in, and when?
What you bring
A defined participant cohort
Historical participation and support records
Someone who understands the workflow
What you receive
A comparison against your current approach
Example participant reviews
A recommendation on whether a live pilot is justified
The partnership starts with a scoped, paid evaluation. We’ll test whether changes in individual patterns add useful information beyond your current review process.
