# Reducing cognitive decline in patients with mild cognitive impairment and Alzheimer's Disease and related dementias by developing and testing clinician and caregiver deprescribing tools

> **NIH NIH R21** · BRIGHAM AND WOMEN'S HOSPITAL · 2022 · $510,979

## Abstract

Worsening cognitive decline is a hallmark problem in patients with mild cognitive impairment (MCI) and
Alzheimer’s Disease and related dementias (ADRD). Despite the well-recognized worsening of cognitive
burden by high-risk medications like benzodiazepines, medications with strong anticholinergic side effects, and
sedative hypnotics, they continue to be overprescribed in this population. Many factors contribute to their
overuse including clinical inertia and the need to involve caregivers. Fortunately, deprescribing has been
shown to improve outcomes in patients with MCI/ADRD. However, deprescribing efforts have often had
modest success due to lack of primary care provider (PCP) involvement and giving insufficient support at the
point of care. Interventions that have been successful have been resource intensive and thus difficult to scale.
 In contrast, electronic health record (EHR) systems offer a scalable strategy for changing provider
behavior that could be useful for deprescribing medications that worsen cognitive burden. Existing literature
demonstrates the potential for EHR-based tools to improve deprescribing, especially when augmented with
insights from behavioral science. EHR systems could also be leveraged to engage caregivers in the
deprescribing process but has not been done before. Despite the high potential for scalability, application of
specific deprescribing tools in EHRs for PCPs has been limited, especially for MCI/ADRD populations.
 To overcome this gap, we propose this R21 to develop and pilot test new EHR deprescribing tools for
PCPs of patients with MCI/ADRD that also involve caregivers. We will leverage learnings from our NUDGE-
EHR trial, an NIA-funded pragmatic trial evaluating whether EHR tools designed using behavioral science
improves deprescribing in general older adults and adapt them for the MCI/ADRD population to provide pilot
data for a subsequent large pragmatic trial and generalizable evidence about caregiver engagement strategies.
 The specific aims are: (1) to design and pilot test EHR tools using behavioral science for deprescribing in
patients with MCI/ADRD and (2) to identify strategies for engaging caregivers in EHR tools. The new tools will
include a PCP-facing EHR dashboard that identifies patients in need of deprescribing and facilitates
communication with caregivers, alerts enhanced with behavioral principles to encourage deprescribing, order
sets that provide easier ordering of dose-tapers and alternative medications, and post-visit monitoring tools.
We will conduct qualitative interviews and pilot testing within the EHR system with PCPs and caregivers to
demonstrate feasibility and usability of the EHR tools, also assessing implementation outcomes to identify
barriers to intervention scalability. In addition, we will evaluate the ability to engage caregivers using caregiver-
facing surveys delivered through EHR patient portal, emailed, phone, and mailed communications. The
expected overall impact o...

## Key facts

- **NIH application ID:** 10370471
- **Project number:** 1R21AG075928-01
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Julie Christine Lauffenburger
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $510,979
- **Award type:** 1
- **Project period:** 2022-02-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10370471

## Citation

> US National Institutes of Health, RePORTER application 10370471, Reducing cognitive decline in patients with mild cognitive impairment and Alzheimer's Disease and related dementias by developing and testing clinician and caregiver deprescribing tools (1R21AG075928-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10370471. Licensed CC0.

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