May 30, 2023
IMPACT members Antonia Bennett, PhD, Christine Ritchie, MD, MSPH, and V.G. Vinod Vydiswaran, PhD joined other authors in a scoping review of algorithms that used electronic health record (EHR) data to identify patients with Alzheimer’s disease and related dementias (ADRD). The authors reviewed 271 articles and found that many EHR-based algorithms can help identify populations with or at high-risk of developing ADRD and that further research can lead to improvements in the algorithms that are used with ERH data.
Abstract
Objective
The authors performed a scoping review of algorithms using electronic health record (EHR) data to identify patients with Alzheimer’s disease and related dementias (ADRD), to advance their use in research and clinical care.
Materials and Methods
Starting with a previous scoping review of EHR phenotypes, the authors performed a cumulative update (April 2020 through March 1, 2023) using Pubmed, PheKB, and expert review with exclusive focus on ADRD identification. They included algorithms using EHR data alone or in combination with non-EHR data and characterized whether they identified patients at high risk of or with a current diagnosis of ADRD.
Results
For the cumulative focused update, the authors reviewed 271 titles meeting our search criteria, 49 abstracts, and 26 full text papers. They identified 8 articles from the original systematic review, 8 from their new search, and 4 recommended by an expert. They identified 20 papers describing 19 unique EHR phenotypes for ADRD: 7 algorithms identifying patients with diagnosed dementia and 12 algorithms identifying patients at high risk of dementia that prioritize sensitivity over specificity. Reference standards range from only using other EHR data to in-person cognitive screening.
Conclusion
A variety of EHR-based phenotypes are available for use in identifying populations with or at high-risk of developing ADRD. This review provides comparative detail to aid in choosing the best algorithm for research, clinical care, and population health projects based on the use case and available data. Future research may further improve the design and use of algorithms by considering EHR data provenance.