A Technology-Driven Intervention to Improve Early Detection and Management of Cognitive Impairment

Most experts advocate for early detection of cognitive impairment (CI) so that patients and caregivers can be prepared for making difficult decisions and to improve quality of life, but studies show that screening alone isn't sufficient to change clinician actions related to early detection. Using predictive modelling developed with machine learning methods and sophisticated clinical decision support (CDS) tools, it is possible to identify patients at elevated risk for CI and make it much easier for primary care to engage and support patients and caregivers in meaningful care planning. The goal of this study is to implement and evaluate a low-cost, highly scalable CI-CDS system integrated within the electronic health record that has high potential to improve early CI detection and care and translate massive public and private sector investments in health informatics into tangible health benefits for large numbers of people.

Inclusion Criteria:Primary care office visit at a randomized clinic during the accrual period, ANDPatient is age 65 or over, ANDPatient has no CI diagnosis documented in the EHR prior to the visit, ANDPatient has:Any abnormal score on a comprehensive cognitive assessment (MoCA, MMSE or SLUMS) in the prior 18 months, ORa MiniCog score of <3 in the prior 18 months and there is no evidence of subsequent comprehensive cognitive assessment (MoCA, MMSE, SLUMS), ORNo cognitive testing in the last 18 months and a risk of a dementia diagnosis in the next 3 years >=15% as calculated by the MC-PLUS algorithm developed in the R61 phaseAND-First visit during the accrual period at which all prior inclusion criteria are metExclusion Criteria:Patient has received active parenteral chemotherapy within the last yearPatient has stage 4 or equivalent cancer diagnosisPatient is enrolled in hospice care or palliative care program