Runs under the site’s own brand
Colour, logo and login portal are the clinic’s, not ours. Participants see the practice they already trust, not a third-party app they have never heard of.
OutPatient AI
Outcomes measured, not guessed
OutPatient AI follows people between visits. A wearable and a short check-in feed a simple dashboard, so a site can see what actually happened in the weeks after a visit instead of relying on memory. It is in active development and running now with partner sites.
ClinicsSites follow participants between visits, rather than waiting for the next appointment.
Health groupsOne dashboard across several locations, with the same measures used at every one.
GymsRecovery and training data collected steadily, in a place people already come to each week.
- Wearable data: sleep, activity, resting and variable heart rate
- Biomarker dashboards a site can read at a glance
- Check-ins written in the participant's own language
- The same measures everywhere, so results can be compared
- Data stays with the site and its study protocol
In development
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HRV ms · OutPatient AI
What a site actually gets
Built for the people running the programme, not just the people in it
Organised by group, location and department
A multi-site operator administers every location from one place, with the same measures defined once and applied everywhere, so two clinics’ numbers can actually be compared.
One activation token per participant
Each person is enrolled with a single-use token tied to their record. No shared logins, and no participant is created without a site deliberately enrolling them.
A baseline assessment sets the schedule
A short intake sets what is tracked and how often the check-ins come, so the cadence fits the protocol rather than a generic default.
The site chooses what it tracks
Measures are selected and prioritised per programme. A recovery study and a metabolic study should not be collecting the same fields, and here they do not have to.
Reads the devices people already wear
Fitbit, Garmin, Apple Health and Google Fit feed sleep, activity, resting and variable heart rate automatically, so the data does not depend on anyone remembering to write it down.
Aggregate view across the whole cohort
Operators see participation, completeness and trends across a group — where data is missing and which sites are falling behind on collection. It reports on the record-keeping, not on whether anyone got better.
The data leaves with the site
Records export to the site’s own systems and stay governed by its study protocol. It is their data; we hold it on their behalf.
One deliberate omission: there are no points, badges or leaderboards tied to what a participant reports. Rewarding a number is a fast way to corrupt it. Reminders nudge people to complete a check-in — never to report a particular answer.