# Identifying Opioid Overdose Predictors using EHRs

> **NIH NIH R01** · UNIVERSITY OF MASSACHUSETTS LOWELL · 2020 · $582,062

## Abstract

"Identifying Opioid Overdose Predictors using EHRs"
Pain and effective pain management are among the most critical health issues facing
Americans. In 2011, the Institute of Medicine reported that as many as one-third of all
Americans experience persistent pain at an annual cost of as much as $635 billion in medical
treatment and lost productivity. Prescription opioids are increasingly used to treat acute and
chronic pain. To date, epidemiologic research defining opioid-related adverse drug event (ADE)
risk factors has relied on broad, static categorizations of risk derived from diagnostic codes.
Though important foundational work, these studies have three important limitations: (1) they
focus on only the most catastrophic ADE (overdose) and thus miss the opportunity to identify
less severe, prodromal ADEs (e.g. fatigue, dizziness, sleepiness, over-sedation) that may
precede and predict overdose; (2) they do not reliably capture aberrant drug-related behaviors
(ADRBs)—risky patterns of use that may affect overdose risk; and (3) they rely on clinician-
coded diagnoses in structured data, which have notoriously weak sensitivity and specificity, and
neglect rich opioid-related information from unstructured clinical narratives. To address this gap,
we propose a stepwise approach that leverages the power of electronic health records and new
computational methdologies to explore associations among prodromal adverse events, ADRBs,
and overdose. This approach is critical to the development of next-generation opioid overdose
prevention tools.

## Key facts

- **NIH application ID:** 9895708
- **Project number:** 5R01DA045816-03
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS LOWELL
- **Principal Investigator:** WILLIAM C BECKER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $582,062
- **Award type:** 5
- **Project period:** 2018-07-15 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9895708

## Citation

> US National Institutes of Health, RePORTER application 9895708, Identifying Opioid Overdose Predictors using EHRs (5R01DA045816-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9895708. Licensed CC0.

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