Drug overdose is one of the leading causes of death in various countries. By automating overdose death data through an artificial intelligence (AI) tool, public health officials can provide a timely response to reduce overdose deaths, researchers at the University of California found.

An automated process based on computer algorithms that can read text from medical examiners' death certificates can substantially speed up data collection of overdose deaths – which in turn can ensure a more rapid public health response time than the system currently used, new UCLA research finds.

As it now stands, overdose data recording involves several steps, beginning with medical examiners and coroners, who determine a cause of death and record suspected drug overdoses on death certificates, including the drugs that caused the death. The certificates are then sent to local jurisdictions or the Centers for Disease Control and Prevention (CDC) which code them.

This coding process is time-consuming as it may be done manually. As a result, there is a substantial lag time between the date of death and the reporting of those deaths, which slows the release of surveillance data. This in turn slows the public health response.

Researchers developed a machine learning model capable of estimating national weekly opioid overdose mortality trends in near real-time using proxy data sources such as public health information and law enforcement data.

These tools can combat issues around delayed overdose data so that health departments can effectively respond to overdose spikes.