AI-enabled conversation platform to measure mental status and manage psychotropic medication use for older adults
Awardee Organization(s): George Washington University | Crosswater Digital Media
Principal Investigator(s): Lorens Helmchen, PhD
Official Project Title: AI-Enabled Conversations to Measure Mental Status and Manage Psychotropic Medication Use
AITC Partner: PennAITech
Website(s): www.gwu.edu
Continuous monitoring of cognitive function among the elderly is vital for early detection and proper management of Alzheimer’s Disease and related dementias. Similarly, continuous monitoring of changes in mood is indispensable for the appropriate dosing of psychotropic medication. Yet, current means of monitoring cognitive function and mood among the elderly are often infrequent, inconsistent, and imprecise because they rely on the completion of standardized questionnaires that may fail to flag clinically relevant leading indicators. This project aims to deploy and validate the use of digital “conversation companions”, a remote patient-monitoring technology that can be installed on tablet computers and smartphones by untrained caregivers or the elderly themselves. The recordings and transcripts of the conversations between elderly residents and these digital companions will be used to train machine-learning algorithms that can measure the presence and severity of dementia and depression and predict fall risk. Expert clinicians, family members, and community stakeholders will ensure that the predictions are clinically informative, actionable, transparent, and culturally appropriate. As the technology can be used by patients on their own and as the voice and the visuals of the digital conversation companions can be adapted to a patient’s linguistic and cultural background, this technology can reach traditionally under-served patient populations such as racial minorities and those living in remote areas. This technology will allow caregivers to detect small and subtle changes in an individual’s cognitive function and mood in a way that is less intrusive, more frequent, more consistent, and more precise than current practice.
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