Clinical decision support
Software that gives a clinician patient-specific information at the moment of a decision: a drug interaction alert, a sepsis warning score, a suggested dose, a flag on an image. Hospitals have run it for decades as hand-written rules, and much of the AI in clinical use today is decision support with a learned model inside. The clinician stays the decision-maker, so the design question is whether they can see the basis for a suggestion and judge it in the time they have. In the US the same question shapes regulation. Software that meets four statutory criteria falls outside FDA device rules, and one of the four is that the clinician can independently review the basis for the recommendation. Software that analyzes medical images or physiological signals remains a device, and FDA guidance holds that a tool meant for time-critical decisions does not give the clinician a real chance to review its basis, whatever it displays. When you assess a tool, ask what it shows the clinician besides its answer.
A sepsis score fires on a patient the nurse thinks looks well. If the alert shows only a number, the nurse can trust it or ignore it, and has nothing to check. If it shows the rising lactate and the heart rate trend behind the score, the nurse can hold those against the bedside and make a call in a minute. Ask to see the alert screen as well as the performance report before you buy.
Terms like this come up in real clinical scenarios across the HelloAI courses: bite-sized modules with verifiable certificates. An account takes one minute, no password needed.
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