Newsweek Highlights Lucem Health’s Approach to Earlier Type 1 Diabetes Detection

Lucem Health was recently featured in Newsweek for a hard truth in care delivery: type 1 diabetes (T1D) is still diagnosed too late, far too often. When nearly half of patients show up in diabetic ketoacidosis (DKA), we are not dealing with a clinical mystery. We are dealing with a system problem. DKA is frequently preventable. Late identification is the norm because early-stage T1D can be silent.

Health systems do not need more good intentions. They need a scalable way to find risk earlier without asking already stretched teams to do more manual work.

What Newsweek covered and why it matters

In the feature, Holly Taylor, general manager of strategic partnerships at Lucem Health, points to the real constraint on early detection. It is not the existence of tests. It is the ability to operationalize early identification in routine clinical practice.

Newsweek highlights Lucem Health’s use of an AI model built on the BEHRT transformer architecture, adapted and validated for U.S. health systems. The goal is straightforward: recognize early, subtle patterns in longitudinal EHR data that can precede disease onset, then focus follow-up where it is most warranted.

The article also references the real-world shift happening inside health systems. Mercy (Hospital St. Louis) is among the organizations partnering with Lucem Health to change the trajectory of serious diseases earlier, not after an avoidable emergency forces the issue.

BEHRT in plain English

BEHRT is a transformer-based approach designed to learn from sequences of events in electronic health records (EHRs). Think of it like this. Transformer models became well known for learning relationships between words in language. BEHRT applies that same idea to healthcare events across time. Visits, diagnoses, medications, and labs form a timeline. The model learns patterns in that timeline that humans rarely have the bandwidth to detect consistently at scale.

If you want the underlying sources, here are two references:

• Transformer foundation paper (“Attention Is All You Need”): https://arxiv.org/abs/1706.03762
• BEHRT paper (Nature Digital Medicine): https://www.nature.com/articles/s41746-019-0110-5

Read the Newsweek feature

This is what the conversation about early detection should sound like. Clear-eyed about the stakes. Serious about scale. Focused on implementation, not hype.

Read the full feature: https://www.newsweek.com/nw-ai/ai-impact-the-cost-of-playing-it-safe-11197101