# Using System Dynamics Modeling to Foster Real-time Connections to Care

> **NIH NIH R61** · YALE UNIVERSITY · 2023 · $257,974

## Abstract

Project Summary
The two objectives of our currently funded HD2A Innovation Project are: (1) to implement a novel, scalable,
evidence-based, intervention (i.e., our telehealth platform RecoveryPad) that links people who have overdosed
with access to medication for opioid use disorder (MOUD), harm reduction services, and recovery supports,
and (2) to collect high-quality data about the processes and outcomes associated with deployment of this
platform that can be integrated with our existing system dynamics (SD) model to determine if, where, when,
and what interventions should be implemented in the future. In this manner, our data (i.e., input and output
from the SD model) drives our action (i.e., provision and refinement of RecoveryPad) in a continuous feedback
loop. This administrative supplement will allow integration of an ethical artificial intelligence (AI) framework into
the refinement and evaluation of the telehealth intervention of the parent project. Specifically, we will evaluate
datasets, model assumptions, algorithmic inputs, development, and performance of the parent project for
potential biases, particularly in relation to exacerbating disparity of OUD-related outcomes among vulnerable
populations. Through the systematic detection and mitigation of algorithmic biases, we will enhance the
fairness of AI-augmented interventions, promoting equitable treatment engagement across diverse
demographics. The insights we gain will not only optimize our own RecoveryPad platform and system
dynamics model but will also contribute to wider ethical AI applications in healthcare. Moreover, our work
stands to improve outcomes for individuals with OUD and support national efforts to address the opioid crisis.
Specifically, we propose the following supplemental aims: 1) Aim 1 - to assess bias and fairness within the SD
model: This aim seeks to translate AI fairness assessment methodologies into iterative refinement of the
existing system dynamics model. By leveraging our integrated team that includes a bioethicist, AI experts, data
scientists, clinicians, and people with lived experience, we will examine key model inputs and their potential
bias implications on model outputs for sensitive demographic attributes. Furthermore, we intend to ensure
representation and mitigate any algorithmic bias. 2) Aim 2 - to assess bias and overall fairness of
RecoveryPad: Aim 2a) Fairness evaluation of datasets and the brief negotiated interview (BNI) process during
RecoveryPad Development: We will assess potential biases by analyzing whether our population-level
machine learning algorithms exhibit differential predictions for MOUD engagement across diverse groups using
historical electronic health record data, where ED patients have received a BNI from an in-person health
promotion advocate. Aim 2b) Bias and Fairness Assessment of RecoveryPad: We will evaluate bias and
fairness within RecoveryPad through simulated and real-time participant conversational encounters, l...

## Key facts

- **NIH application ID:** 10851137
- **Project number:** 3R61DA057675-01S1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Rebekah Heckmann
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $257,974
- **Award type:** 3
- **Project period:** 2022-09-30 → 2024-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10851137

## Citation

> US National Institutes of Health, RePORTER application 10851137, Using System Dynamics Modeling to Foster Real-time Connections to Care (3R61DA057675-01S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10851137. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*