Everett Wetchler

Informing the public about our data, our findings and what the data means is of the utmost importance to the Texas Justice Initiative. More information about our data sets can be found here, and all of our data is available through data.world (free account required) here. Below, you’ll find the Texas Justice Initiative’s latest press releases and reports.

Dr. Kirtan Upadhyaya is not included in the author byline. He should be listed as the sixth author, and his affiliation is 1: Bayes Impact, Technology 501(c)(3) Non-profit, San Francisco, California, United States of America. The contributions of this author are as follows: Conceptualization, Investigation, Validation, and Writing – Review & Editing.

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission.