Predicting hospital readmission using AI
One of the biggest challenges to prevent readmissions is identifying patients who are at risk.
To crackdown on hospitals with high readmission rates and to improve patient healthcare, the Centers for Medicare and Medicaid Services (CMS) apply payment penalties to hospitals with higher than expected readmission rates. As a consequence, about 82 percent of CMS organizations received reduced reimbursements under the Hospital Readmissions Reduction Program in 2019. Healthcare leaders need smarter solutions.
Download our healthcare e-book to see how we use artificial intelligence to improve the prediction of avoidable hospital readmissions, including:
- Identifying patients at risk of readmission
- Replacing the LACE score with a new calculator
- How to reduce hospital financial burden
- Improvements to patient outcomes
About the Author
Ham Pasupaleti has held strategy and operations roles in the IT industry for more than 27 years, implementing and managing business-critical applications and systems infrastructure for Global 1000 companies. At PK, he provides business analytics and optimization solutions to healthcare organizations, enabling them to transform into outcome-based delivery models that are high quality, accountable, patient-centric, and cost-effective.Tags: AI, hospital readmissions, LACE