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Explanation: Predictive analysis in healthcare leverages historical patient data, such as medical history, lab results, and symptoms, to forecast potential diagnoses. For example, machine learning models can predict the likelihood of diseases like diabetes or heart conditions based on early indicators. This approach allows for early intervention, personalized treatment plans, and resource optimization, ultimately improving patient outcomes. By identifying high-risk patients, healthcare providers can allocate resources effectively and reduce costs while enhancing care quality. Option A: While analyzing historical data improves patient care, it is not predictive but rather retrospective in nature. Option B: Automating inventory management optimizes operations but does not directly impact patient outcomes. Option D: AI complements medical professionals but cannot replace their expertise, especially in critical care scenarios. Option E: Hospital design improvements might enhance patient experience but are unrelated to predictive analysis.
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