Re-imagining diagnosis
We have a big idea to re-imagine traditional mental health diagnosis by using digital phenotyping of case formulations.
TheraPass
TheraPass
Our first product towards this movement is TheraPass.
With TheraPass, patients’ histories, insights, and goals are summarized into standardized, one page case formulations. Patients use their TheraPasses for intakes to expedite the delivery of care and improve therapeutic alliance.
See a sample TheraPass on the left. You can download this sample for a closer look.
As we collect data points on patients’ formulations, we can use these to digitally phenotype patients.
When each data point contains an entire mental health patient profile, including what therapeutic interventions have helped and not… and we collect thousands of these data points…
We can use machine learning to cluster patients into phenotypes that will enable:
-
Our phenotyping is built on rich patient narratives rather than a rigid symptom checklist like the DSM-5. This approach allows for a more nuanced, holistic categorization of mental health conditions.
-
Because TheraPass includes a section on past treatment successes and failures, mapping patients to a phenotype offers a more precise way to predict effective treatments than traditional DSM-5 diagnoses.
-
Phenotyping groups patients with others who share similar narratives, rather than those who check 5 out of 9 symptoms in a manual. This allows for connection with individuals who understand each other’s struggles more profoundly.
Who We Are
-
Vibha Puri
Lived experience in mental health treatment and work experience at 6 mental health organizations. Trained in psychology, computer science, and business at Stanford University.
-
Chris Raine
Founder and former director of two startups treating substance use disorder. Trained in policy and business from Harvard and Oxford University.
-
Mike Millard
Licensed psychiatrist. Founder of This Way Up, Australia’s largest online therapy program.
-
Jessica Yang
Founder of a mental health storytelling non-profit. Trained in artificial intelligence at Stanford University.