A team of researchers at the Medical University of South Carolina (MUSC) has been awarded a $3.75 million grant to evolve an already existing informatics tool so that it can better identify emergency department (ED) cases of nonfatal opioid overdose.
The tool, known as "informatics for integrating biology and the bedside," or I2B2, will use natural language processing, which helps to make human language intelligible to computers. With that added capability, the tool will be able to look for clues suggesting opioid overdose in ED physicians' clinical notes in the electronic health record (EHR), MUSC officials noted.
What’s more, the enhanced tool should help researchers understand what constellation of clinical traits, discoverable in the EHR, reliably predicts for opioid overdose, and 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, officials attested.
The funding comes from the National Center for Advancing Translational Sciences and will be used over the span of five years.
The reason why the tool required enhancement, according to MUSC leaders, is because researchers need up-to-date information on the opioid epidemic since it is constantly evolving. “Once driven primarily by prescription opioids, the epidemic has now shifted more to fentanyl and other synthetic opioids. If successful and widely shared, the enhanced informatics tool could provide real-time data on how the epidemic is changing. It could also enable researchers to learn more about the situation in their region or at their institution so that they can design clinical trials appropriately,” officials said.
"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 L. McCauley, Ph.D., MUSC Health ED physician. "Developing a tool like this allows not just researchers, but from the surveillance perspective, it allows clinicians to stay on top of trends."
Currently, the data on opioid overdose in the EHR, which is based on discrete diagnosis codes, can be inaccurate or incomplete. "When the presenting condition is something else, such as a respiratory disorder, the OD might not be recognized or coded properly," added McCauley. "Sometimes when patients are delivered to the hospital, it's unclear what substance they've overdosed on or whether they've overdosed or are just in a comatose state."
And even when coded data in EHRs do identify cases of opioid overdose accurately, they may not be very informative on what treatment plans were recommended and whether the patients followed through, the researchers noted. "We don't always have good data on what the treatment plans are and where people are and how successful they were in getting into those treatment plans," said Leslie Lenert, M.D., the principal investigator for the award, and assistant provost for data science and informatics and chief research information officer at MUSC. "We don't know what the handoff was between the hospital and the treatment facility."
Lenert is also associate principal investigator and informatics director of the South Carolina Clinical & Translational Research (SCTR) Institute, which is the Clinical & Translational Science Awards Program (CTSA) hub headquartered at MUSC. The SCTR team comprises biomedical informatics experts, addiction science specialists, ED physicians and biostatisticians/methodologists.
Further explaining the improved technology, researchers noted that the tool being enhanced by the SCTR team will look for clues in a physician's narrative clinical notes that a case could have been a nonfatal opioid overdose. For instance, if the emergency medical responders administered Naloxone (Narcan®), that would not be in the hospital's coded data, but the physician might have mentioned it in the clinical notes.
"It is often difficult to retrospectively identify the chart of a patient who presented to the ED with an opioid overdose, because the diagnosis is not coded in a discrete, easily searchable field," said MUSC Health ED physician Lindsey Jennings, M.D., "Having a way to locate these charts that relies on how ED providers naturally document will improve research efforts, ultimately improving patient care."
The team will also create “smart forms” for EHRs, designed to encourage physicians to enter more information about patients with opioid overdose and their treatment plans. By the end of the five-year project, Lenert hopes the participating sites will use the tool to begin planning clinical trials.
With the tool, researchers will be able to enroll patients at any of the participating sites in their trials. They will also know which trials make the most sense for their own patients. "I think we're going to have a number of clinical trials that are launched based on the findings that we glean from these databases," said Lenert. "That's really our plan."