AI at the Bedside: Digitalizing Clinical Decision Making

face
main_img

Much has been written on the potential for artificial intelligence (AI) to change the way in which healthcare is conducted around the world, from expanding access to care in remote regions to increasing the efficiency with which patient data is stored and creating the ability to monitor a patient’s health through wearable technology. One particularly promising area is the potential for AI to dramatically improve the accuracy of clinical decision making.

“AI’s biggest strength is perhaps its ability to serve as a clinical decision support tool – allowing AI to work with humans to provide better care and increase efficiencies”

Ausgust Calhoun, Siemens Healthineers

August Calhoun, senior vice president of North America Services at Siemens Healthineers, feels that “AI’s biggest strength is perhaps its ability to serve as a clinical decision support tool – allowing AI to work with humans to provide better care and increase efficiencies.” Jennifer Bresnick of HealthITAnalytics concurs, noting that AI will allow healthcare practitioners to move from being reactive to proactive in their clinical decision making. She posits, “As the healthcare industry shifts away from fee-for-service, so too is it moving further and further from reactive care. Getting ahead of chronic diseases, costly acute events, and sudden deterioration is the goal of every provider – and reimbursement structures are finally allowing them to develop the processes that will enable proactive, predictive interventions.” Bresnick continues, “Artificial intelligence will provide much of the bedrock for that evolution by powering predictive analytics and clinical decision support tools that clue providers in to problems long before they might otherwise recognize the need to act.”

This revolution will be particularly important in radiology, as Calhoun notes. “In radiology, CT and MR scanning have been growing at between ten and 12 percent per year for the last ten years, however the radiologist workforce is increasing at only three percent per year. This is resulting in over 230,000 patients waiting over a month for their imaging test results. AI will provide radiologists with tools to meet the rising demand for diagnostic imaging and actively shape the transformation of radiology into a data-driven research discipline.” Calhoun elaborates, “Specifically, AI algorithms are expected to help expedite clinical workflows, prevent diagnostic errors, and reduce missed billing opportunities, which will in turn sustain increases in productivity. AI could lead to more precise results and more meaningful prognostic risk scores and integrate diagnostic radiology even more into outcomes-oriented clinical decision-making.”

“If you have an AI algorithm and lots and lots of data from many patients, it’s easier to match up what you’re seeing to long term patterns”

Brandon Westover, MGH Clinical Data Animation Center

Brandon Westover, director of the MGH Clinical Data Animation Center, notes that AI can also be useful in helping decide whether or not to continue care for critically ill patients, such as those who have entered a coma after cardiac arrest. “Typically, providers must visually inspect EEG data from these patients,” he notes. “The process is time-consuming and subjective, and the results may vary with the skill and experience of the individual clinician. But if you have an AI algorithm and lots and lots of data from many patients, it’s easier to match up what you’re seeing to long term patterns and maybe detect subtle improvements that would impact your decisions around care.”

Writer: Patrick Burton

Related Content

Latest Report