# Development of computer aided decision support and EHR alerts for DOAC prescribing

> **NIH NIH R03** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $78,000

## Abstract

Project Summary/Abstract
This study will develop, assess usability of, and refine a a “diagnosis-driven” computer decision support system
(CDS) and a longitudinal alert system to improve safe and patient-specific prescribing of direct oral
anticoagulant (DOAC) medications. We will develop diagnosis-driven CDS tools for the two most common
conditions leading to DOAC use (atrial fibrillation and venous thromboembolism). Each will guide the clinician
through drug and dose selection based on critical patient characteristics, laboratory values, and concurrent
medication use. User-center design principles will underly an iterative approach of heuristic evaluation,
usability testing, and semi-structured interviews with 15 practicing clinicians. We will also develop a serial,
longitudinal system to continually re-assess DOAC prescribing appropriateness and alert clinicians when
issues develop. Following the “5 Rights” model endorsed by AHRQ, we will assess for the most acceptable
format, channel, and time in clinician work flor for these alert notifications to occur. Findings from this study will
lead to improvements in safe management and a reduction in adverse drug events for the three million patients
prescribed DOACs nationwide.

## Key facts

- **NIH application ID:** 10059571
- **Project number:** 1R03HL154205-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Geoffrey Douglas Barnes
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $78,000
- **Award type:** 1
- **Project period:** 2020-07-21 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10059571

## Citation

> US National Institutes of Health, RePORTER application 10059571, Development of computer aided decision support and EHR alerts for DOAC prescribing (1R03HL154205-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10059571. Licensed CC0.

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