Zite AI makes software that helps healthcare organizations open medical facilities in the right place with the right service mix. You input basic information about the facility and are provided the locationa and service mix you need to have a maximal impact on your community and your bottom line.

Health systems use eCART to quickly and efficiently direct clinical resources toward their most critical patients and ensure timely and consistent care. Our sophisticated reporting and benchmarking further empower clinical leadership to understand and drive practice patterns. eCART draws upon readily available patient data from the EHR, rapidly quantifies disease severity, and predicts likelihood of critical illness onset, hours before it happens. Built upon more than a decade of ongoing scientific research, eCART was developed at the University of Chicago by Dr. Dana Edelson, MD, MS and Dr. Matthew Churpek, MD, PhD and is well chronicled in published literature. The analytic and workflow have been in use in clinical practice since 2016.

At AgileMD, we are building the most advanced real-time predictive analytics and clinical pathways platform for hospitals.

To date, AgileMD tools have been used on nearly 2.5 million patients by more than 80,000 providers in over 150 hospitals, including some of the nation's leading academic medical centers. AgileMD is backed by premier accelerators MATTER, YCombinator, and Rock Health, and our cutting-edge analytics are built on a decade of ongoing research at the University of Chicago. In 2021, AgileMD garnered nearly $3 million in combined federal research and development funding from the National Institutes of Health (NIH) and Health and Human Services (HHS) to further advance our platform.

We predict the performance of proposed facilities in seconds. 

We use tens of thousands of factors describing the nuanced characteristics of the surrounding community to drive our predictions in moments. Our AI models have unparalled accuracy. 

• 10+ years of published academic literature on value over other early warning scores
• Analytics derived and validated in robust patient datasets 
• Built by clinicians for clinicians, with full cross-team workflow to ensure risk scores are understood and acted upon

• 8+ years in clinical use
• High sensitiviy and specificity, meaning fewer false alarms and more efficient focus on the highest risk patients
• Integrated workflow to ensure that risk scores are not ignored and action is taken
• Robust reporting and benchmarking to drive quality improvement
• Deep EHR-integration