Charleston-based Medical University of South Carolina researchers received $3.75 million to enhance an existing informatics tool to better detect cases of nonfatal opioid overdose in emergency departments.
Researchers will apply artificial intelligence and natural language processing to the "informatics for integrating biology and the bedside" tool, also known as I2B2. With the added NLP enhancements, I2B2 will be able to analyze physicians' clinical notes in the EHR to better identify ED cases of opioid overdose.
The added enhancements to I2B2 will also help researchers determine what group of clinical traits noted in the EHR can reliably predict for opioid overdose as well as provide real-time data on opioid addiction. Researchers plan to use the information to design more intelligent clinical trials and improve patient recruitment for trials.
In addition, the enhanced tool should help researchers understand what constellation of clinical traits, discoverable in the electronic health record, reliably predicts for opioid overdose. It will also provide real-time information on opioid addiction. That will help researchers design more intelligent clinical trials and improve patient recruitment into those trials.
"Right now, if I wanted to do a clinical trial where I needed to recruit patients who had had nonfatal overdose in the ED, the best, most up-to-date data would be about a year old," said Jenna McCauley, PhD, director of the South Carolina Clinical and Translational Research Institute, according to a news release. "Developing a tool like this allows not just researchers, but from the surveillance perspective, it allows clinicians to stay on top of trends."
The National Center for Advancing Translational Sciences granted MUSC the funding for the project.