# Digital Phenotyping for Computational Models of Relapse Prediction in Early Course Psychosis

> **NIH NIH K23** · BETH ISRAEL DEACONESS MEDICAL CENTER · 2020 · $108,130

## Abstract

Project Summary
 The candidate requests support for a one-year program supplement to evaluate the
impact of both clinician and patient training in digital health around care outcomes. The project
will be done in partnership with a network of community mental health centers who are ready to
begin today, and will utilize existing digital health training resources already developed and
piloted by our team
 In the proposed project, the candidate will build upon his previous experiences in
engineering, clinical informatics, clinical psychiatry, and ongoing K23 award. The goals of this
supplement are well aligned with the K23 as ensuring both patients and clinicians are
comfortable using digital technologies creates the exact environment where digital phenotyping
methods (the focus of the K23) can thrive.
 The digital skills taught to patients and clinicians in this proposal are timely and
actionable. Given these efforts were already in progress before the pandemic as evidenced by
early published papers, we are confident this approach is not rush but rather thoughtfully
designed to best serve the SMI clinical community. The patent facing digital skills group
(DOORS) has already been offered to over 100 patients and the clinician facing training (Digital
Navigator) has been fully designed with a detail curriculum in place. Expanding, improving, and
offering these trainings will offer immediate benefit in helping ensure technology can be used to
increase access to care for SMI patients.
Assessing the hypothesis around dual training (for patients and clinicians) and its potentially
synergistic nature compared to only provider and patient training is novel. Results of this project
will thus not only create new training resources that we will update in real time based on
outcomes, but may suggest a new approach of offering dual training that has never been tried
before with the SMI clinical community.

## Key facts

- **NIH application ID:** 10151726
- **Project number:** 3K23MH116130-03S1
- **Recipient organization:** BETH ISRAEL DEACONESS MEDICAL CENTER
- **Principal Investigator:** John Torous
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $108,130
- **Award type:** 3
- **Project period:** 2018-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10151726

## Citation

> US National Institutes of Health, RePORTER application 10151726, Digital Phenotyping for Computational Models of Relapse Prediction in Early Course Psychosis (3K23MH116130-03S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10151726. Licensed CC0.

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