Machine Learning-Enabled COVID-19 Prognostic Tool Supports Clinical Decision-Making for Emergency Department Discharge

Researchers who evaluated the real-time performance of a machine learning (ML)-enabled, COVID-19 prognostic tool found that it supported clinical decision-making for emergency department discharge at hospitals.

A multidisciplinary team of intensivists, hospitalists, emergency doctors, and informaticians at the University of Minnesota Medical School (Minneapolis, MN, USA) evaluated the tool which delivered clinical decision support to emergency department providers to facilitate shared decision-making with patients regarding discharge.

The University research team successfully developed and implemented a COVID-19 prediction model that performed well across gender, race and ethnicity for three different outcomes. The logistic regression algorithm created to predict severe COVID-19 performed well in the persons under investigation, although developed on a COVID-19 positive population.

A logistic regression model ML-enabled can be developed, validated, and implemented as clinical decision support across multiple hospitals while maintaining high performance in real-time validation and remaining equitable. The researchers recommend that the effect on patient outcomes and resource use needs to be evaluated and further researched with the ML model.

“COVID-19 has burdened healthcare systems from multiple different facets, and finding ways to alleviate stress is crucial,” said Dr. Monica Lupei, an assistant professor at the U of M Medical School and medical director M Health Fairview University of Minnesota Medical Center - West Bank. “Clinical decision systems through ML-enabled predictive modeling may add to patient care, reduce undue decision-making variations and optimize resource utilization — especially during a pandemic.”

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University of Minnesota Medical School