For more than 20 years, I have worked at the intersection of marketing and gerontology including roles at Marriott Senior Living, Age Wave, J. Walter Thompson's mature market group, and as chair of the Alzheimer's Association Tennessee Chapter.

Shelby Livingston

Researchers at the University of Kentucky Sanders-Brown Center on Aging have uncovered evidence that a single genetic variant may influence the risk of two of the diseases people fear most — dementia and cancer — but in opposite ways. The findings, recently published in the Journal of Neuropathology & Experimental Neurology, indicate that a particular variation in the GRN gene is associated with an increased risk of LATE, a common form of age-related dementia, while also being linked to a lower risk of cancer. 

Cigna’s $100 million investment to support a new AI-powered pharmacy program and Peak Rock Capital's purchase of Asembia may spur even more specialty pharmacy deals.

Health Enterprise Partners recently published 2024 Reflections & Predictions in partnership with L.E.

Accurate disease trajectory prediction is critical for early intervention, resource allocation, and improving long-term outcomes. While electronic health records (EHRs) provide a rich longitudinal view of patient health in clinical environments, models trained on curated research cohorts may not reflect routine deployment settings, and those trained on single-hospital datasets capture only fragments of each patient's trajectory. This highlights the importance of leveraging large, multi-hospital health systems for training and validation to better reflect real-world clinical complexity. In this work, we develop DT-Transformer, a foundation model trained on 57.1M structured EHR entries over 1.7M patients from Mass General Brigham (MGB), spanning 11 hospitals and a broad network of outpatient clinics. DT-Transformer achieves strong discrimination in both held-out and prospective validation settings. Next-event prediction achieves a median age- and sex-stratified AUC of 0.871 across 896 disease categories, with all categories exceeding AUC 0.5. These results support health system-scale training as a path toward foundation models suited to real-world clinical forecasting.