Rehospitalization Analytics: Modeling and Reducing the Risks of Rehospitalization


National Science Foundation Award # 1231742


Project Duration: 10/1/12-9/30/16


Principal Investigator:

       Chandan Reddy


Graduate Students:

       Bhanukiran Vinzamuri

       Yan Li

       Karthik Padthe

       Martin Alther



       David Lanfear, Henry Ford Health System

       Lihua Qu, William Beaumont Hospital

       Shankar Madhavan, Blue Cross Blue Shield of Michigan


Project Summary:

Hospitalizations account for more than 30% of the 2 trillion annual cost of healthcare in the United States. Experts estimate that as many as 20% of all hospital admissions occur within 30 days of a previous discharge. Such rehospitalizations are not only expensive but are also potentially harmful, and most importantly, they are often preventable. Providing special care for a targeted group of patients who are at a high risk of rehospitalization can significantly improve the chances of avoiding rehospitalization. Estimating the predictive power of the clinical data collected during the hospitalization of a patient and effectively making predictions from such diverse patient records requires new analytical models. This project develops a 'rehospitalization analytics' framework that can identify, characterize and reduce the risks of rehospitalization for patients using a wide range of electronic health records. Specifically, research objectives of this project are to develop: (i) integrated models that can effectively leverage multiple heterogeneous patient information sources and transfer the acquired knowledge about rehospitalization between different hospitals and patient groups in the presence of only few patient records. (ii) novel adaptable time-sensitive models that make predictions of the risk estimates in the presence of inherent concept drifts in the clinical data. (iii) new regularization methods that can extract the population-specific risk factors effectively despite the presence of multiple correlations and grouped categorical clinical predictors. The methods are evaluated using heart failure patient records collected at the Henry Ford Health System in Detroit. The performance of the proposed models is compared against the state-of-the-art statistical and clinical tools that are currently being used for risk prediction.


This project provides a comprehensive and accurate assessment of risk of rehospitalization and has the potential to direct more aggressive treatments towards specific high-risk patients. Accurate and timely predictive models developed in this project could be widely adopted and can have national impact on improving the lives of patients (by reducing exacerbations and avoiding hospitalization) and reducing overall health care costs (by reducing the number of costly hospitalizations). The computational models developed in this project could also be applied to other chronic diseases that have high rates of utilization and could benefit from improved targeting of intervention/resources. The educational objective of this project is to train the next generation of interdisciplinary researchers in the fields of data analytics and healthcare informatics. The progress of the project and the research findings are disseminated via the project website (



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Software Download:

Will be made available soon.


Poster Presentation: [ Slide ]


Point of Contact: Chandan Reddy : reddy (at) cs (dot) vt (dot) edu

Date of Last Update: 5/31/16