Multidisciplinary collaboration to develop a digital health solution for early detection of cognitive decline in primary care

Developing digital healthcare solutions that facilitate rapid identification and effective management of patients with Alzheimer disease and related disorders (ADRD) is an area of intense study. A critical juncture for these efforts involves the early detection of cognitive decline. Because primary care providers (PCPs) are the first line of medical care, they are often the first to hear concerns about cognitive decline. However, under-diagnosis of ADRD in primary care settings is widely recognized, as are the many barriers to routine cognitive screening. While information about brief cognitive screening tools for detecting ADRD is plentiful, PCPs remain uncertain about which patients to assess, which tools to use and how to use them, and how to communicate results. The goal of this project was to design a risk assessment and cognitive screening (RACS) application that specifically addressed the needs and concerns of PCPs to facilitate identification of cognitive decline in primary care settings.We employed a multi-modal assessment approach in designing the RACS app, which first assesses risk for cognitive impairment and then assesses cognitive functioning using a working memory/processing speed task in combination with four speech/language tasks. We assembled a multidisciplinary team with the following expertise to develop and test the app: biostatistics, computational linguistics, computer science, computerized cognitive assessment, gaming/app development, engineering, neurology, neuropsychology, neuroscience, primary care, psychometrics, and speech-language pathology.Programming of app features and pilot testing with people with ADRD was completed in 3 months. Initial development of the connected speech analysis pipeline was completed in 4 months with ongoing testing. Data collection of 50 cognitively normal, 50 mild cognitive impairment, and 50 mild dementia participants is approximately 50% completed within 6 months. Preliminary results based on cognitive performance alone show good ability to discriminate groups. Reduction of speech-language variables for inclusion in a final cognitive performance score is underway using a variety of machine learning techniques, including the elastic net and random forests.The RACS app shows promise as a digital health solution to facilitate early detection of cognitive decline in primary care and may prove useful in other busy clinical settings.