# Digital Peer Support for Opioid Use Disorders: Scaling Chat Support Groups to meet Community Needs

> **NIH NIH R43** · BEACON TECH, INC. · 2022 · $252,131

## Abstract

PROJECT SUMMARY
90% of Americans with SUD are not in treatment. When patients receive care, they do so
inconsistently. The average length of time an American spends in treatment before seeing a full
year of recovery is 9 years. Peer Support is proven to be effective at engaging patients who are
unwilling to engage with traditional treatment. Outcomes from current face to face peer models
have shown reductions in inpatient utilization and positive clinical outcomes such as improved
recovery capital. While there is some evidence that group models such as AA and NA are
effective for some patients, their support is siloed away from other clinical care and community
support. Our solution, Marigold Recovery Service (MRS) is a novel, machine learning enabled,
digital peer support program that is built on top of an anonymous text-based social network. It
fulfills the need for a robust, 24/7, secure platform to support patients at all stages of their
recovery. This phase I proposal will demonstrate the ability of our service to engage and retain
patients with Opioid Use Disorders and will also develop novel techniques to automatically
analyze patient messages for clinical and social determinants of health-related needs.

## Key facts

- **NIH application ID:** 10485580
- **Project number:** 1R43DA056275-01
- **Recipient organization:** BEACON TECH, INC.
- **Principal Investigator:** Satya Prateek Bommaraju
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $252,131
- **Award type:** 1
- **Project period:** 2022-09-01 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10485580

## Citation

> US National Institutes of Health, RePORTER application 10485580, Digital Peer Support for Opioid Use Disorders: Scaling Chat Support Groups to meet Community Needs (1R43DA056275-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10485580. Licensed CC0.

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