Public health is a big deal, and it's not just about doctors and hospitals. It's about how money is spent across different areas of public policy. To make sure everyone gets the care they need, governments must figure out how to use their budgets wisely. But this is tricky. It often involves experts spending a lot of time and money to connect budgets to health outcomes.

A new tool called Categorical Perplexity-based Uncertainty Quantification (CPUQ) might make this easier. It uses Large Language Models (LLMs) to create maps that show how money is linked to health results. This isn't just about guessing; it's about understanding the uncertainty in these predictions. The tool uses special prompts to generate distributions that help policymakers see the bigger picture.

The cool thing about CPUQ is that it can handle different types of data. It can show how budgets relate to health indicators and how these indicators connect to each other. This gives a more detailed view than other methods. The predictions made by CPUQ are pretty close to what experts say, which is a good sign.

But is this the ultimate solution? Maybe not. It's a step in the right direction, but it's important to keep testing and improving these tools. The goal is to make sure that every dollar spent on public health makes a real difference. This way, the idea of Health-for-All can become a reality, not just a dream.

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