Nearly half of people currently living with dementia have not received a diagnosis, delaying access to treatment
as well as education and support for the patient and family. Thus, NIA has requested applications to support
pragmatic clinical trials of low-cost tools to improve detection of cognitive decline in clinical settings (RFA-AG-
20-051). With pilot funding from NIA, we used machine learning to develop a low-cost tool called eRADAR
(electronic health record Risk of Alzheimer's and Dementia Assessment Rule), which uses easily accessible
information in the electronic health record (EHR) to help identify patients with undiagnosed dementia. In
addition, we interviewed patients, caregivers, clinicians, and healthcare system leaders to inform pragmatic
implementation of eRADAR in clinical settings. Stakeholders felt strongly that such a tool should be
implemented through primary care, in the context of existing clinical relationships, and would need to be
accompanied by additional support for patients and clinicians. Our current proposal is heavily informed by this
development work. In Aim 1, we will use EHR data to evaluate eRADAR's performance in different
patient subgroups, including by race/ethnicity, in two healthcare systems to inform selection of cut-
points for use in clinical settings. We will select an optimal cut-point to use for targeted dementia
assessment with stakeholder input, balancing sensitivity, specificity, and positive predictive value. In Aim 2, we
will perform a pragmatic clinical trial to determine whether implementing eRADAR as part of a
supported outreach process to high-risk patients improves dementia detection. The setting will be
primary care clinics within Kaiser Permanente Washington (KPWA), an integrated healthcare delivery system
in Washington State, and the University of California, San Francisco (UCSF), an urban, academic healthcare
system with a diverse patient population. The study includes 6 clinics with ~24,000 patients age =65. Within
each clinic, primary care providers (PCPs) will be randomly assigned to have their patients with high eRADAR
scores targeted for outreach (intervention) or to usual care (control). Our clinical research staff—whose roles
were designed to reflect existing roles within these healthcare systems to maximize pragmatism—will reach
out to patients with high eRADAR scores, conduct an assessment for cognitive impairment, make follow-up
recommendations to PCPs, and support patients after diagnosis. Patients with high eRADAR scores in both
treatment arms will be followed to determine the impact of eRADAR on new diagnoses of dementia (primary
outcome) as assessed from the EHR (again, to maximize pragmatism). In Aim 3, we will explore the impact
of eRADAR implementation on secondary outcomes including healthcare utilization and experience of
patients and family members. If this pragmatic trial is successful, the eRADAR tool and process could be
spread to other healthcare systems, potentially improving detection of cognitive decline, patient care, and
quality of life.
