The algorithm is used by health care providers to screen patients for “high-risk care management” intervention. Under this system, patients who have especially complex medical needs are automatically flagged by the algorithm. Once selected, they may receive additional care resources, like more attention from doctors. As the researchers note, the system is widely used around the United States, and for good reason. Extra benefits like dedicated nurses and more primary care appointments are costly for health care providers.
Taking a deeper look
Apple said its profit increased to $11. 52 billion in its most recent quarter, up nearly a third from the same period a year earlier, showing that the company’s enormous business selling iPhones and other gadgets continues to breeze along.
Perfection is achieved not when there is nothing more to add but when there is not much left to take away.
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Why it matters
The strong results beat Wall Street estimates, sending the company’s shares about 4 percent higher on Wednesday to a new record and for a market value of more than $950 billion.
As a result, he said, algorithms have magnified our worst tendencies and “rogue actors and even governments” have used our data against us “to deepen divisions, incite violence and even undermine our shared sense of what is true and what is false.”
In one piercing portion, Mr. Cook criticized how companies like Facebook and Google — while taking care not to mention them by name — deliver personalized news feeds that lead to so-called filter bubbles and confirmation bias.
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The excerpt jumps between several unrelated topics, so it reads more like a collection of fragments than a coherent article. The claims about algorithms in health care and the Apple earnings are interesting, but without context they are hard to evaluate.
The article touches on several important issues, but it jumps between health care algorithms and Apple’s earnings without much connection. The point about algorithms amplifying bias and filter bubbles is especially relevant, though it deserves more depth than a brief mention.
The article doesn’t seem to focus on global design components at all; it discusses health care algorithms and Apple’s earnings, with a brief quote on perfection. That makes the title feel disconnected from the actual text.
The article seems to jump between unrelated topics—health care algorithms, Apple’s earnings, and criticism of personalized news feeds—so it’s hard to tell what the through-line is meant to be. If the point is that algorithms are shaping decisions in medicine, tech, and media, that could be interesting, but the excerpt doesn’t connect those pieces clearly.
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The algorithm example shows how automated screening can widen access to extra care, but also raises fairness concerns when resources are limited.
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The health-care algorithm example is striking: automatically flagging “high-risk” patients can direct scarce care resources, so the criteria and oversight matter as much as the efficiency gains.
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The excerpt moves from a health-care screening algorithm to Apple’s earnings and then to Mr. Cook’s critique of personalized feeds, so the connection between the pieces isn’t clear; the apparent throughline is concern about automated systems and their social consequences.
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The excerpt jumps abruptly from health-care algorithms to Apple’s earnings and then to Tim Cook’s comments on personalized feeds, so the connection between these sections isn’t clear.
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The excerpt raises a useful tension: cost-saving algorithms can shape health care decisions, while personalized feeds can reinforce bias—both need careful oversight.
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The excerpt jumps from a health-care screening algorithm to Apple’s earnings and Tim Cook’s remarks on filter bubbles; the connections between these sections aren’t clear, so it reads more like assembled fragments than a coherent argument.
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The excerpt jumps between an algorithm used for high-risk care management, Apple’s earnings, and comments on filter bubbles, so it’s hard to follow as a single piece. The point about algorithms amplifying existing tendencies and personalized feeds creating filter bubbles is worth discussing, but the article would benefit from a clearer focus.
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