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Hospitals bought a lot of prediction in the past three years, and much of it went unused. A risk score lights up on a dashboard nobody checks during rounds. An alert fires into an inbox that fills faster than any clinician can read. The model works in the statistical sense and still changes nothing at the bedside. That gap between a correct output and an acted-on output is the real test for machine learning in healthcare system design, and most failed pilots fail there rather than in the math. The rule is narrow on purpose. Machine learning earns its place in a healthcare system only where it moves a decision or reshapes a workflow. Organizations working with healthcare IT consulting services often begin by identifying these high-impact workflows before selecting or building machine learning solutions. Everything else is expensive dashboard decoration. That standard sounds harsh until the alternative shows up on a budget line: seat licenses for tools clinicians ... Continue reading →
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