Twin Health

I don’t think employers need another GLP-1 access strategy. They need a GLP-1 outcomes strategy. That’s a distinction we think about a lot at Twin Health. GLP-1s can be an important tool in care. But utilization isn’t an outcome. If the business model benefits from continued prescription utilization, where is the incentive to help a member reduce or come off medication when it’s clinically appropriate? The point is that the clinical outcome, not continued utilization, should determine success. A successful strategy should be able to answer harder questions: Are members getting healthier? Is that improvement durable? Are medications being used for the right people, for the right amount of time? At Twin, that means looking beyond the prescription. Our Digital Twin Care Platform uses each member’s health data to guide care in the moment, helping members improve their metabolic health while their clinical team can adjust treatment (including medications), based on how their health is actually changing. That's what our four core programs are built to do, whether or not a GLP-1 is part of the picture: meet each member where they are, and keep them progressing before, during, and after any medication. GLP-1s can be part of that strategy. They just shouldn't be the strategy. That's where I think the conversation needs to go next: from who gets access to what that access actually achieves. What is your GLP-1 vendor incentivized to achieve?

AI is no longer a “future state” in healthcare… it’s quietly reshaping the cost curve right now. This week alone, we’re seeing health plans double down on AI for utilization management, risk stratification, and care navigation. But here’s the uncomfortable truth: Most of these investments are optimizing around administrative efficiencies. We’re getting better at predicting who will become high cost…But not nearly good enough at preventing it. That’s where the real opportunity lies. The most forward-thinking plans are starting to pair AI with continuous metabolic care models to actually change outcomes, not just forecast them. This is where companies like Twin Health are flipping the script, using real-time data and AI to reverse chronic conditions like type 2 diabetes at scale. Lower claims. Better outcomes. Same members. That’s the trifecta every plan is chasing. The question isn’t whether AI belongs in healthcare. It’s whether we’re applying it in the right place. Are we building smarter systems to manage chronic disease… Or finally investing in eliminating it? Curious where others stand on this 👇

Our revolutionary invention, the AI Digital Twin along with the compassionate care team, ensures to heal the dysfunctional metabolism, which is the root cause of chronic metabolic diseases like diabetes. Twin Health India

This week marked my 4th work anniversary with Twin Health , and as I reflect on this milestone, I realize that four years is a long time to summarize in a single post. So much has been learned, overcome, and adapted to—both personally and professionally—that words feel like they do no justice for the reality of the experience. I am incredibly proud, happy, and above all, thankful for the trajectory of this chapter. While platforms like LinkedIn are often reserved for the visible wins and the highlight reels, I’ve found that the most vital work happens during the invisible parts of the journey. It’s in those quiet spaces where the real choices are made to expand rather than retreat, and where true evolution takes root. I want to express my deepest gratitude to the people who have inspired me and helped me continue to grow for the better; you have been the catalysts for an evolution I don't take for granted. We all have a journey that remains largely unseen, and today, I’m celebrating both the successes on the surface and the silent growth beneath them.

Twin Health India