Machine Learning Increases Accuracy of ASA Physical Status Scores in Certain Cases




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Differences in assigning American Society of Anesthesiologists (ASA) physical status scores to patients may one day become a thing of the past according to this latest study. Thanks to a new machine learning algorithm (ML-ASA) that determines the score using widely available patient data, according to this hospital registry cohort study.

“Some surgeons actually make an assessment of the patient’s ASA physical status, while others do not, and simply input a random number,” said Karuna Wongtangman, MD, the anesthesiology digital health laboratory chief at Montefiore Medical Center, in New York City.

Another recent study included data obtained between 2005 and 2021 from adult surgical patients presenting at both Beth Israel Deaconess Medical Center (BIDMC), in Boston, and Montefiore Medical Center (MMC) had certain similar findings in most cases, but there were still differences in others.

“At least the tool is doing the same job as an anesthesiologist in patients who don’t get to see one before surgery,” said Wongtangman.

Nevertheless, the researchers don’t see the ML-ASA as a supplement to clinical expertise, but rather as an adjunct to it currently.

For Jonathan P. Wanderer, MD, MPhil, who led similar research back in 2018 (J Med Syst 2018;42[7]:123), the current research helps demonstrate how machine learning can make anesthesiologists more efficient in the processes that power healthcare.

Read more about Machine Learning in the full article below from Anesthesiology News.

Machine Learning Increases Accuracy of ASA Physical Status Scores:

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