# Meaningful Drug Interaction Alerts

> **NIH AHRQ R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $377,457

## Abstract

Meaningful Drug Interaction Alerts
Project Summary:
Clinical decision support (CDS) for electronic health records (EHR) and prescribing systems has been
promoted to improve patient outcomes. One type of CDS are drug-drug interaction (DDI) alerts. The Office of
the National Coordinator for Health IT meaningful use criteria includes the implementation of DDI detection and
warnings to physicians and other healthcare professionals. Nearly all healthcare organizations rely on DDI
alerts generated from commercial drug knowledge databases. Warnings are currently generated using simple
drug combination rules, ignoring drug attributes and the wealth of information available in the EHR that could
make the warnings specific to the patient. As a result, providers are bombarded with useless warnings and
often miss important ones.
Our approach is to change the framework for DDI alerting from basic look-up tables to a more complex, but
meaningful, clinical algorithms. Our plan is innovative because it will: 1) eliminate alerts for DDIs that are not
clinically important given the patient and drug context; 2) develop implementable and tested algorithms using
existing and new evidence; and 3) support the dissemination, implementation, and evaluation of these
algorithms across the spectrum of healthcare facilities and organizations. The central hypothesis of this
project is that individualizing DDI alerts to specific patient circumstances will result in a much greater proportion
of alerts that physicians, pharmacists, and other healthcare providers will be more likely to heed. We will
accomplish our objectives and test our hypothesis by pursuing the following aims:
Specific Aim 1: Design sharable evidence-based individualized DDI algorithms that capitalize on the wealth of
patient data located within electronic health records;
Specific Aim 2: Validate the function of newly designed DDI algorithms using electronic health record data;
and
Specific Aim 3: Conduct a prospective evaluation of DDI algorithms in a variety of healthcare environments
including ambulatory and institutional settings.
This project will greatly improve CDS for DDIs by incorporating contextual factors into evidence-based and
validated alert algorithms, which will reduce alert fatigue and result in more meaningful CDS. Our approach,
involving partners across multiple organizations and environments and experts in drug interaction and
biomedical informatics, will result in safer healthcare with respect to the use of medications.

## Key facts

- **NIH application ID:** 9883780
- **Project number:** 5R01HS025984-04
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** DANIEL C MALONE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $377,457
- **Award type:** 5
- **Project period:** 2018-05-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9883780

## Citation

> US National Institutes of Health, RePORTER application 9883780, Meaningful Drug Interaction Alerts (5R01HS025984-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9883780. Licensed CC0.

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