AI Beats Out Clinicians in Triaging Postoperative Patients for ICU

How good is artificial intelligence in decision-making? Not bad according to findings from a pilot study that was recently presented at the American College of Surgeons Clinical Congress 2019. Findings from the study show AI in the form of a machine-learned algorithm correctly triaged the vast majority of postoperative patients to the intensive care unit in its first proof-of-concept application in a university hospital setting. As it stands now surgical teams typically rely on clinical judgment to decide which patients need postoperative intense care because there is no single set of fixed criteria to make the determination. The result is that clinicians typically over-triage, meaning if they are in doubt, they err on the side of caution and send a patient to intensive care. However, over-triaging may result in admitting a patient to the ICU who doesn't need to be there. "In those cases, the patient may be unnecessarily exposed to multidrug-resistant bacteria and have an increased overall length of stay. On the other hand, under-triaging means a patient that should have been in the ICU is sent to a recovery or step-down unit, and the opportunity for quick rescue of a deteriorating condition is delayed because monitoring is not as intense," Marcovalerio Melis, MD, FACS, an associate professor of surgery, New York University Langone Hospital System, New York City, and coauthor of the pilot study, said in a release. The resulting algorithm included 87 clinical variabl...
Source: MDDI - Category: Medical Devices Authors: Tags: Digital Health Source Type: news

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