Revolutionizing Patient Care Through Artificial Intelligence

It impacts about one-fifth of the 3 million hospital patients seeking acute care each year in Canada. Because of it, the average length of a hospital stay in the United States has increased by 20 per cent in 2022 compared to 2019. In the United Kingdom, it costs approximately 100 million pounds per year. It has also impacted healthcare systems and patients in many other countries, such as Germany, Sweden, Scotland, Denmark, France, New Zealand, Australia, and South Korea.

It is a phenomenon called delayed hospital discharge.

One McMaster University researcher is helping to advance the management and prevention of delayed discharge using data analytics, artificial intelligence (AI), and optimization. His research — which can be applied globally — has the potential to reduce costs and hospital waiting times and improve patients’ health.

The Effects of Delayed Discharge on Healthcare 

Delayed discharge is a designation given to hospital patients who no longer need the level of care a hospital provides but still require additional care and support. However, their care journey is interrupted because the homecare support or long-term care they need is unavailable. As they wait to be discharged, the patient’s health has been shown to deteriorate, putting them at risk for adverse health outcomes such as readmission and death. At the same time, no other patient is able to use the hospital bed the delayed discharge patient occupies.

Using Big Data and AI to Make Informed Healthcare Decisions

Part of the reason healthcare systems encounter challenges such as delayed discharge, says Zargoush, is because they lag in employing data to make predictions for improving decisions. To combat the issue, he uses big data and artificial intelligence to predict patient outcomes in different scenarios and optimize healthcare decisions. For example, 18 years of rich data on almost all delayed discharge patients in Ontario inform Zargoush’s research in two projects.

“While the cost, data, and other parameters differ from one geographical location to another, the data-driven framework and procedure we have developed and proposed in our studies remain the same,” he explains.

So, what do the results of Zargoush’s delayed discharge research look like in motion? In a hospital healthcare setting, for example, a computer can use Zargoush’s algorithm with a patient’s record to inform various care and transition decisions. Policymakers can also use the research to prioritize budget allocation and long-term decision-making.

“The complex models are working behind the scenes while the proposed solutions are very simple to implement in something like an app or computer software,” he says. “It’s as simple as healthcare professionals using their phones and computers to improve their decision-making. We’ve shown that this kind of decision-making outperforms many other decision-making paradigms, including first come, first serve.”

Delayed discharge, says Zargoush, is just one area of healthcare where artificial intelligence can be applied to make more informed decisions. Another is optimizing medication prescriptions for chronic disease management. Currently, Zargoush is working on two projects to help physicians make better prescription decisions for diabetic and hypertension patients.

“Patients are complex, which means different medications work differently for different patients,” he explains.

Addressing the Dark Side of Artificial Intelligence 

However, Zargoush adds, it’s important to note that this research is not meant to replace physicians.

“This is not even possible, given the complex nature of human beings,” he says. “But I do see this misunderstanding sometimes. Instead, I view these methods as decision-support tools that help physicians, healthcare managers, and policymakers to make better choices.”

In addition to the concern that AI will replace healthcare providers, Zargoush also addresses the potential for unfair or unethical use of AI. In another recent study, for example, he has developed tools and methods to apply AI-based resource allocation in healthcare in a fair manner with respect to equity-seeking groups.

“Most of the time, the data we collect are inherently biased,” he explains. “So, if we train artificial intelligence on a biased data set, the resulting AI-trained predictions will be biased too. To this end, we have found ways to train our AI-based decision-making algorithms in a way to ensure fairness.”

*In this article, artificial intelligence is synonymous to machine learning.