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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedFritz B, King C, Chen Y
Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study.
This paper describes a protocol for an ongoing study that hypothesizes that anesthesiology clinicians can predict postoperative complications more accurately with machine learning assistance than without machine learning assistance. This investigation is a sub-study nested within the TECTONICS randomized clinical trial. Study team members who are anesthesiology clinicians working in a telemedicine setting are currently reviewing ongoing surgical cases and documenting how likely they feel the patient is to experience 30-day in-hospital death or acute kidney injury. These case reviews will be randomized to be performed with access to a display showing machine learning predictions for the postoperative complications or without access to the display, and the accuracy of the predictions will be compared across these two groups.
AHRQ-funded; HS024581.
Citation: Fritz B, King C, Chen Y .
Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study.
F1000Res 2022; 11:653. doi: 10.12688/f1000research.122286.2..
Keywords: Surgery, Risk, Outcomes, Health Information Technology (HIT)