Past Projects
Promoting compassionate care in acute psychiatry with human-AI teams
(2023-2025)
Project Lead: Marta Maslej
With: Petal Abdool, Daniel Buchman, Masooma Hassan, Matt Ratto, Juveria Zaheer, Christine Bucago, Light Chiotu, Marzyeh Ghassemi, Sean Hill, Stephanie Sliekers, Sanjeev Sockalingam
Funding: AMS Healthcare
Project Summary. Powered by advances in AI, support tools are being developed to facilitate clinical tasks, like assessment and care. One potential application in acute psychiatry is in assessing risks of inpatient violence or aggression. Recent efforts to automate this assessment have involved training machine learning models to predict which patients are likely to exhibit these behaviours. However, both human- and AI-based assessment can be biased, which can lead to inequities in risk management. It may be possible to enhance risk assessment and other clinical tasks via human-AI teaming, which involves using AI to augment, not prescribe, decision-making. This project involves experimental research into the various social, emotional, and technical factors impacting teaming, as well as how teaming might interact with the complex care environment to impact patient care.
Predictive Care: An institutional ethnography of risk assessment in acute psychiatric care
(2021-2023)
Project Summary. Managing violence or aggression is an ongoing challenge in emergency psychiatry. Efforts to automate the assessment of risk involve training machine learning models on electronic health records to predict these behaviours; however, no studies to date have examined which patient groups may be over-represented in false positive predictions, despite evidence of social and clinical biases that may lead to higher perceptions of risk in patients defined by intersecting features (eg, race, gender). This project pilots a computational ethnography to study how the integration of machine learning into risk assessment might impact acute psychiatric care, with a focus on how electronic health records are compiled and used to predict a risk of violence or aggression.
Project Leads: Laura Sikstrom, Marta Maslej
With: Katrina Hui, Daniel Buchman, Juveria Zaheer, Zoe Findlay, Gillian Strudwick
Funding: SSHRC IDG & Google Award for Inclusion Research
