About 20 years ago, I stepped on stage at one of our Geisinger town halls and looked out upon a sea of people: thousands of full-time employees at an integrated health system charged with the health and well-being of millions of Pennsylvanians. 

Only a fraction of the people in that room were clinicians. 

That was the first time I fully visualized the problem: We employed more people in our revenue cycle department to process bills and reconcile data than we did doctors. And we weren’t alone. It’s the same story at every health system in America, large and small, and over the past two decades, the ratio has become dramatically more disparate. 

Continue to STAT+ to read the full story…

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arXiv:2604.03480v1 Announce Type: new Abstract: Creative thinking is a fundamental aspect of human cognition, and divergent thinking-the capacity to generate novel and varied ideas-is widely regarded as its core generative engine. Large language models (LLMs) have recently demonstrated impressive performance on divergent thinking tests and prior work has shown that models with higher task performance tend to be more aligned to human brain activity. However, existing brain-LLM alignment studies have focused on passive, non-creative tasks. Here, we explore brain alignment during creative thinking using fMRI data from 170 participants performing the Alternate Uses Task (AUT). We extract representations from LLMs varying in size (270M-72B) and measure alignment to brain responses via Representational Similarity Analysis (RSA), targeting the creativity-related default mode and frontoparietal networks. We find that brain-LLM alignment scales with model size (default mode network only) and idea originality (both networks), with effects strongest early in the creative process. We further show that post-training objectives shape alignment in functionally selective ways: a creativity-optimized \texttt{Llama-3.1-8B-Instruct} preserves alignment with high-creativity neural responses while reducing alignment with low-creativity ones; a human behavior fine-tuned model elevates alignment with both; and a reasoning-trained variant shows the opposite pattern, suggesting chain-of-thought training steers representations away from creative neural geometry toward analytical processing. These results demonstrate that post-training objectives selectively reshape LLM representations relative to the neural geometry of human creative thought.