# EHR Nudges: Optimizing a Clinical Decision Support System for Evidence-Based Statin Medication Prescribing to Reduce the Risk of Cardiovascular Disease

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $748,424

## Abstract

PROJECT ABSTRACT
Statins reduce the risk of major adverse cardiovascular events and mortality. However, providers fail to
prescribe statin therapy for about half of patients meeting guideline criteria for initiation. The electronic health
record (EHR) creates opportunities to develop clinical decision support systems (CDSSs) to support
cardiovascular disease (CVD) risk recognition, assessment, and management. However, low provider adoption
has limited the clinical impact of CDSSs designed to improve guideline-concordant statin prescribing.
Integrating insights from behavioral economics into CDSS design represents a novel approach to improving
adoption by minimizing key barriers - provider time and cognitive load burden. Behavioral economics studies
the effects of psychological, social, cognitive, and emotional factors on the decisions of individuals and uses
nudges to influence behavior at a largely unconscious level. Nudges are defined as positive reinforcement and
indirect suggestions which have a non-forced effect on decision-making. For example, “opt-out” options for
organ donation consent lead to striking differences in enrollment. Nudges represent an exciting and novel
approach to developing CDSSs that minimize provider burden and are, therefore, more efficient, scalable, and
impactful (i.e., optimized). The overall objective of this proposal is to develop and optimize a CDSS, including
several nudges, to increase guideline-concordant statin prescribing for CVD risk (Nudge-CVD-CDSS). We use
an innovative, engineering-inspired multiphase optimization strategy (MOST) framework to arrive at an
intervention that is not just efficacious or effective but efficient and scalable. Several potential intervention
components (EHR-nudges) will be developed, usability tested, revised, and evaluated. A randomized trial
using a specialized design will evaluate the individual and combined effects of nudges. We will seek the
combination of nudges that maximizes impact on guideline-concordant statin prescribing while minimizing
provider time and cognitive load burden. Specific Aims: 1) To develop, based on a conceptual model of the
prescribing process, a set of potential intervention components (EHR-nudges) to promote and support
AHA/ACC guideline-concordant statin prescribing, 2) To revise potential intervention components through
iterative usability testing, including real-time measures of provider time and cognitive load burden and 3) To
use a randomized trial with a specialized design to identify which EHR-nudges, or combinations of nudges,
contribute most efficiently to AHA/ACC guideline-concordant statin prescribing. The proposed work is
significant in its efforts to develop an effective, efficient, and scalable intervention to improve guideline-
concordant care for CVD risk management. It is innovative in its use of insights from behavioral economics and
the MOST framework to optimize a CDSS by balancing clinical impact with provider time and cognitive...

## Key facts

- **NIH application ID:** 10777564
- **Project number:** 1R01HL171292-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Safiya Richardson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $748,424
- **Award type:** 1
- **Project period:** 2024-01-01 → 2028-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10777564

## Citation

> US National Institutes of Health, RePORTER application 10777564, EHR Nudges: Optimizing a Clinical Decision Support System for Evidence-Based Statin Medication Prescribing to Reduce the Risk of Cardiovascular Disease (1R01HL171292-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10777564. Licensed CC0.

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