A new study out today from York University found proactive, continual and transfer learning strategies for AI models to be key in mitigating data shifts and subsequent harms. To determine the effect of data shifts, the team built and evaluated an early warning system to predict the risk of in-hospital patient mortality and enhance the triaging of patients at seven large hospitals in the Greater Toronto Area.

The study used GEMINI, Canada’s largest hospital data sharing network, to assess the impact of data shifts and biases on clinical diagnoses, demographics, sex, age, hospital type, where patients were transferred from, such as an acute care institution or nursing home, and time of admittance. It included 143,049 patient encounters, such as lab results, transfusions, imaging reports and administrative features.

As the use of AI in hospitals increases to predict anything from mortality and length of stay to sepsis and the occurrence of disease diagnoses, there is a greater need to ensure they work as predicted and don’t cause harm, says senior author York University Assistant Professor Elham Dolatabadi of York’s School of Health Policy and Management, Faculty of Health, a member of Connected Minds and a faculty affiliate at the Vector Institute.

The data to train clinical AI models for hospitals and other health-care settings need to accurately reflect the variability of patients, diseases and medical practices, she adds. Without that, the model could develop irrelevant or harmful predictions, and even inaccurate diagnoses. Differences in patient subpopulations, staffing, resources, as well as unforeseen changes to policy or behaviour, differing health-care practices between hospitals or an unexpected pandemic, can also cause these potential data shifts.

Source: https://www.yorku.ca/news/2025/06/04/new-research-finds-specific-learning-strategies-can-enhance-ai-model-effectiveness-in-hospitals/