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Daily design algorithms for a better workflow

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.

Antoinne

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 GIPPL example shows how a workflow algorithm can connect patients with complex needs to concrete resources like dedicated nurses and extra primary care appointments. It also highlights why these systems should be regularly audited so “high-risk” flags improve access without reinforcing existing disparities.

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    The GIPPL example shows how a workflow algorithm can quietly shape who receives extra care, from dedicated nurses to more primary care appointments. It’s a useful reminder that “better workflow” should also include regular checks for bias and unintended gaps in access.

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    The GIPPL example shows that an algorithm’s workflow impact goes beyond efficiency: flagging patients can determine who receives dedicated nurses and extra primary care appointments. It also highlights why these systems need ongoing scrutiny, since a design choice can directly shape access to care.

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    The GIPPL example shows how a seemingly helpful workflow tool can quietly shape who receives scarce care resources. Designing these algorithms well means evaluating not only efficiency, but also whether the “high-risk” label reflects patients’ actual needs without reinforcing existing gaps in care.

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    The discussion of GIPPL shows how algorithmic workflow design can shape access to care, not just improve efficiency. It’s especially important that systems flagging patients for extra support are evaluated for fairness and transparency alongside their clinical benefits.

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