Artificial intelligence tools to speed critical information on drug overdose deaths

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.

For this study, the researchers used "natural language processing" (NLP) and machine learning to analyze nearly 35,500 death records. They found that of the 8,738 overdose deaths recorded that year the most common specific substances were fentanyl (4758, 54%), alcohol (2866, 33%), cocaine (2247, 26%), methamphetamine (1876, 21%), heroin (1613, 18%), prescription opioids (1197, 14%), and any benzodiazepine (1076, 12%). So, the researchers highlighted that if these algorithms were embedded within medical examiner's offices, the time could be reduced to as early as toxicology testing is completed, which could be about three weeks after the death.

Reference:

DOI: 10.1001/jamanetworkopen.2022.25593