5. CONTAINING THE RISKS OF ANTIBIOTIC RESISTANCE
Antibiotic resistance is a growing threat to populations around the world as overuse of these critical drugs fosters the evolution of superbugs that no longer respond to treatments. Multi-drug resistant organisms can wreak havoc in the hospital setting, and claim thousands of lives every year.
C. difficile alone accounts for approximately $5 billion in annual costs for the US healthcare system and claims more than 30,000 lives.
Electronic health record data can help to identify infection patterns and highlight patients at risk before they begin to show symptoms. Leveraging machine learning and AI tools to drive these analytics can enhance their accuracy and create faster, more accurate alerts for healthcare providers.
“AI tools can live up to the expectation for infection control and antibiotic resistance,” Erica Shenoy, MD, PhD, Associate Chief of the Infection Control Unit at MGH.
“If they don’t, then that’s really a failure on all of our parts. For the hospitals sitting on mountains of EHR data and not using them to the fullest potential, to industry that’s not creating smarter, faster clinical trial design, and for EHRs that are creating these data not to use them…that would be a failure.”
6. CREATING MORE PRECISE ANALYTICS FOR PATHOLOGY IMAGES
Pathologists provide one of the most significant sources of diagnostic data for providers across the spectrum of care delivery, says Jeffrey Golden, MD, Chair of the Department of Pathology at BWH and a professor of pathology at HMS.
“Seventy percent of all decisions in healthcare are based on a pathology result,” he said. “Somewhere between 70 and 75 percent of all the data in an EHR are from a pathology result. So the more accurate we get, and the sooner we get to the right diagnosis, the better we’re going to be. That’s what digital pathology and AI has the opportunity to deliver.”
Analytics that can drill down to the pixel level on extremely large digital images can allow providers to identify nuances that may escape the human eye.
“We’re now getting to the point where we can do a better job of assessing whether a cancer is going to progress rapidly or slowly and how that might change how patients will be treated based on an algorithm rather than clinical staging or the histopathologic grade,” said Golden. “That’s going to be a huge advance.”
Artificial intelligence can also improve productivity by identifying features of interest in slides before a human clinician reviews the data, he added.
“AI can screen through slides and direct us to the right thing to look at so we can assess what’s important and what’s not. That increases the efficiency of the use of the pathologist and increases the value of the time they spend for each case.”