# Using Machine Learning and Blockchain Technology to Reduce Drug Diversion in Hospitals

> **NIH NIH R44** · AUTONOMOUS HEALTHCARE, INC. · 2023 · $1,577,231

## Abstract

Based on an analysis, the volume of dosage lost due to diversion increased from 21 million in 2017 to 47
million in 2018, a 126% increase. Addressing the drug diversion problem is a multi-faceted problem involving
many components ranging from provider training to implementation of hardware and software systems to
manage access to controlled substances. However, despite recent improvements in controlling and monitoring
access to controlled substances, the process of identifying drug diversion and ensuring compliance is
complicated and time consuming. Our overall goal is to further develop a technology based on blockchains
to track and document transportation and administration of controlled substances in a hospital environment
and detect drug diversion. The feasibility of the technology and the associated machine learning-based data
analysis engine was established in the Phase I project. Our specific aims are: 1. Further Development of a
cloud-based software platform to leverage smartphones to capture drug transactions in clinic. We will
further develop the cloud-based software platform and its associated smartphone app and web-based
dashboard developed in the Phase I project. The software platform, which uses blockchains to create an
immutable audit trail, will be further developed to capture the “cradle-to-grave” documentation of controlled
substance use and location within an ambulatory surgical center. Prior to deployment, the platform will be
tested by simulation testing using the in silico model developed in our Phase I project, which is capable of
generating realistic transaction data by using a multi-agent simulation framework. 2. Deploying the software
platform at the collaborating health system. We will perform a two-stage rollout of our software platform,
where in the first stage (the focus of this Specific Aim), we plan to perform a pilot test of our smartphone app
and the associated administrator web-based dashboard to ultimately replace the paper logs. The goal of this
stage of the rollout is to understand and address challenges of deploying a new system and potential impacts
on the workflow and its adoption. Data related to adoption, adherence to protocols, and impact on clinical
workflow will be measured and any challenges will be addressed by fine-tuning of the software platform. 3.
Further development and deployment of an analysis engine to detect drug diversion. The goal of this
specific aim is to further develop and fine-tune the analysis engine that uses data (recorded on the blockchain)
to detect drug diversion. This effort involves two main tasks. Real data (collected as part of Specific Aim 2) will
be analyzed offline by the framework developed in Phase I. Through collaboration with our collaborating health
system, we will investigate the generated red flags and use the results of such investigation to fine-tune the
parameters of our model. Next, we will integrate the updated analysis engine and rollout the drug diversion
d...

## Key facts

- **NIH application ID:** 10761130
- **Project number:** 2R44DA051084-02A1
- **Recipient organization:** AUTONOMOUS HEALTHCARE, INC.
- **Principal Investigator:** Behnood Gholami
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1,577,231
- **Award type:** 2
- **Project period:** 2020-04-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10761130

## Citation

> US National Institutes of Health, RePORTER application 10761130, Using Machine Learning and Blockchain Technology to Reduce Drug Diversion in Hospitals (2R44DA051084-02A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10761130. Licensed CC0.

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