AI can Open Up Beds in the ICU

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 (AI) 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.

To test how useful their model might be in practice, the team surveyed six physicians at Austin-area ICUs, asking them to read and evaluate samples of the model’s explanations. Four of the six said the model could improve their staffing and resource management, helping them better plan patient scheduling.

The model has one major limitation, Bardhan notes: the age of the data. In 2014, the industry’s medical coding system changed from ICD-9-CM to ICD-10-CM, adding much more detail in diagnosis coding and classification.

"If we were able to get access to more recent data, we would have loved to extend our models using that data," he says.

His model need not be limited, however, to adult ICUs. “You could extend it to pediatric ICUs and neonatal ICUs," Bardhan says. "You could use this model for emergency room settings.

"Even if you're talking about a regular hospital unit, if you want to know how much or how long a patient is likely to need a hospital bed, we can easily extend our model to that setting."

Tianjian Guo, Indranil R Bardhan, Ying Ding, Shichang Zhang.
An Explainable Artificial Intelligence Approach Using Graph Learning to Predict Intensive Care Unit Length of Stay. Information Systems Research, 2024. doi: 10.1287/isre.2023.0029

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...