Project Summary
The prevalence of cognitive impairment (CI) is expected to triple by 2050, contributing to decreased quality of
life, increased medical care utilization, and additional burden on an already stressed primary care system.
Many clinicians lack confidence to assess, diagnose and manage CI, and more than 50% of patients with CI
are undiagnosed. To address these important problems, in phase 1 (R61) of this project, we developed and
validated a machine learning model called MC-PLUS using results from brief Mini-Cog screens completed
routinely at Annual Medicare Wellness exams and electronic health record (EHR) data to identify patients at
elevated risk of a future CI diagnosis. We also developed, validated, and piloted a CI clinical decision support
(CI-CDS) system to engage patients and clinicians in conversation about elevated CI risk, and to give clinicians
the confidence and tools they need to diagnose and manage CI. Both MC-PLUS and the CI-CDS system were
added into an existing web-based CDS platform that has high use rates and high primary care clinician
satisfaction and is already seamlessly integrated with the Epic EHR.
We are currently beginning phase 2 (R33), a large pragmatic trial with 30 primary care clinics randomized to
receive CI-CDS or usual care (UC). We will evaluate the change in CI diagnosis and clinician confidence in
diagnosing and managing CI among providers in CI-CDS clinics compared to those in UC clinics. If successful,
the CI-CDS system will improve rates of new CI diagnosis and narrow existing sociodemographic
disparities for adults with elevated CI risk identified by MC-PLUS at index visits in CI-CDS compared to UC
clinics.
The CI-CDS system will be available to 2 million patients annually at the study sites with the potential to
disseminate more broadly through the existing non-commercialized CDS platform built on Epic EHR. However,
the CI-CDS design needs to be updated and modernized from our established legacy Epic EHR pipeline to
ensure its robustness, sustainability, interoperability, and scalability for dissemination to the larger community.
The proposed grant supplement aims to engage our IT (Information Technology), software engineering and
internal Epic EHR IT teams to modernize the CI-CDS architecture to enhance its portability, scalability and
impact through the following steps: a) migrating CI-CDS to the OpenShift platform; b) converting its Epic EHR-
specific integration to Fast Healthcare Interoperability Resources (FHIR)-based application programming
interfaces (APIs); and c) re-architecting its patient data extraction and artificial intelligence (AI) inference
pipeline for our MC-PLUS model from batch-based to a real-time model. These activities will facilitate broader
impact of the tool by allowing integration into many different EHRs.