Utilizing Machine Learning for Life-Saving (and More Efficient) Inpatient and Outpatient Triage

The act of triage is a foundational piece of modern medicine dating back to the Holy Roman Empire. During the?Napoleonic Wars, French military doctor Dominique Jean Larrey developed modern triage techniques combined with a newfound emphasis on sanitation when dealing with battlefield injuries.

A few hundred years later, triage is now a vital function for modern emergency departments (ED’s) often stretched to capacity limits due to patient volume increases, a rapidly aging population, chronic staff shortages, and a waxing and waning pandemic.

However, one element of triage that hasn’t changed since the 15th century is that it has been exclusively performed by humans. At least, until now.?

How machine learning helps improve triage (especially for outpatients)

Taking triage calls can be particularly challenging for humans in a telemedicine or remote health monitoring system (RHMS) environment when patients aren’t physically at the hospital for observation.

But telemedicine and RMHS systems are one of the healthcare industry’s?key tools?in combating hospital overcrowding.

That’s why researchers have begun applying machine learning (ML) tools to help scale triage activities, both at the hospital and for telemedicine applications. These models can also better support healthcare workers who must typically rely on their own knowledge and experience when performing triage.

Healthcare providers have used other clinical decision support systems (CDSS) since the 1980s: Statistics from 2013 show that?41 percent?of U.S. hospitals with access to electronic medical records used a CDSS. However, these systems are often little more than static online databases that don’t react intelligently to specific patient issues and medical histories.

Instead, ML models learn from real-world datasets and experience and?use algorithms?to “prioritize patients for us rather than expending the time to generate a rating that may not even be consistent between providers.”?

Aside from improving triage productivity and accuracy, ML models have the potential to save healthcare providers, payers, and consumers plenty of resources by facilitating more accurate assessments of outpatients. The?average cost?of a three-day inpatient hospital stay in the U.S. is around $30,000.

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