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Mar 5, 2025
At the height of the COVID-19 pandemic, hospitals frequently ran short of beds in intensive care units. But even earlier, ICUs faced challenges in keeping beds available. With an aging American population, 11% of hospital stays included ICU stays. Artificial intelligence offers a possible solution, says Indranil Bardhan, professor of information, risk, and operations management and Charles and Elizabeth Prothro Regents Chair in Health Care Management at Texas McCombs. AI models can predict the lengths of time patients will spend in the ICU, helping hospitals better manage their beds and, ideally, cut costs.
But although AI is good at predicting length of stay, it’s not so good at describing the reasons, Bardhan says. That makes doctors less likely to trust and adopt it. People were mostly focused on the accuracy of prediction, and that’s an important thing, he says. The prediction is good, but can you explain your prediction? In new research, Bardhan makes AI’s outputs more understandable and useful to ICU doctors, an approach called explainable artificial intelligence (XAI).
With McCombs doctoral student Tianjian Guo, Ying Ding of UT’s School of Information, and Shichang Zhang of Harvard University, Bardhan designed a model and trained it on a dataset of 22,243 medical records from 2001 to 2012. The model processes 47 different attributes of patients at the time they’re admitted, including age, gender, vital signs, medications, and diagnosis. It constructs graphs that show a patient’s probability of being discharged within seven days. The graphs also depict which attributes most influence the outcome and how they interact.
In one example, the model calculates an 8.5% likelihood of discharge within seven days. It points to a respiratory system diagnosis as the main reason, and to age and medications as secondary factors. Running their model against other XAI models, the researchers found its predictions were just as accurate, while its explanations were more comprehensive.
Source: https://news.mccombs.utexas.edu/research/ai-can-open-up-beds-in-the-icu/