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

> **NIH NIH R43** · AUTONOMOUS HEALTHCARE, INC. · 2020 · $224,954

## Abstract

Drug diversion, defined as “the transfer of a controlled substance from a lawful to an unlawful channel of
distribution or use,” is a challenging issue in today's healthcare systems. 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. This
resulted in over $450M loss to healthcare systems due to drug diversion, a 50% increase compared to 2017.
Hospitals and medical centers constitute the single largest category affected by drug diversion accounting for
33% of all cases and 94% of drug diversion incidents involved opioids. 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 is complicated and time consuming. In this Phase I project, we propose to build on our earlier work in
machine learning and automated technologies in healthcare and consensus in a distributed and decentralized
architecture to develop a technology based on blockchains to track and document transportation and
administration of controlled substances in a hospital environment. The proposed system involves using a
smartphone app to scan uniquely generated barcodes for vials of controlled substances during the transport
process, digitally sign medication transfers between staff using secure digital certificates to eliminate current
paper-based systems, and finally document administration of a controlled substance to the patient by scanning
the unique barcode assigned to the vial and recording an after administration picture of the empty vial.
Specific Aims: 1) Developing and validating an in silico model of drug transport/diversion in the
hospital; we will develop a stochastic model of controlled substance vial movement in the hospital between a
series of locations at the hospital. The vials are exchanged between these locations by agents that represent
clinical staff. 2) Developing a blockchain-based framework to track medications; we will use the
Hyperledger Fabric, an open-source blockchain framework geared towards enterprise applications to design
and implement a blockchain framework. We will develop a software interface to record data in and retrieve
data from the blockchain (and in a potential Phase II, retrieve data from EMRs and automated dispensing
cabinets) for further processing. Finally, we will use the in silico model to quantify the computational power and
storage requirements for the blockchain framework discussed above; and 3) Development of an algorithm to
identify diversion, the goal of this specific aim is to develop a computational engine that uses data (recorded
in the blockchain) to detect drug diversion. We propose to use a framework based on machine learning to
detect anomal...

## Key facts

- **NIH application ID:** 9987189
- **Project number:** 1R43DA051084-01
- **Recipient organization:** AUTONOMOUS HEALTHCARE, INC.
- **Principal Investigator:** Behnood Gholami
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $224,954
- **Award type:** 1
- **Project period:** 2020-04-01 → 2022-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9987189

## Citation

> US National Institutes of Health, RePORTER application 9987189, Using Machine Learning and Blockchain Technology to Reduce Drug Diversion in Hospitals (1R43DA051084-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/9987189. Licensed CC0.

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