Students develop app that aims to predict readmission rates, cut associated costs

Students at Binghamton University, State University of New York, have developed an app meant to reduce readmission rates in hospitals with a data-based assembled prediction model, reports News Medical.

Amirhosein Gholami, Qi Jia, Lu He, and Runpei Xu are the brains behind the Android-based mobile application called "Post Discharge Treatment and Readmission Predictor." Basing their app on the research of Chun-An Chou and Sang Won Yoon, assistant professors of systems science and industrial engineering, the graduate students analyzed the previous research on hospital readmission to predict the readmission rate for high or low-risk patient when they are discharged from the hospital.

"The benefit for the patient is that they don't have to go to the hospital to wait for some services, or even with emergency services, they don't need to call 911," said Gholami. "They can just get help from the healthcare provider. The biggest benefit of this app for hospitals and healthcare providers is to prevent them from paying penalties, which is not a small amount."

The app includes a message system between healthcare providers and patients to extend the range of data mining techniques to the field of healthcare, specifically home treatment. The readmission rate is then evaluated using a data-based prediction model, which increases the prediction accuracy by 20 percent compared to the outdated logistic regression prediction models.

"For this app, predicting the readmission rate is only one part. The other part of this app is instant communication between the doctor/nurse and the patient," said Gholami. "The patient can send information directly to the healthcare provider and, based on the new information, the provider can predict the new readmission rate and send some instructions to the patient to take this medicine, to do this, etc."

The app is in need of further development as the team works to improve the overall quality and easiness of use for patients as well as extending the app onto other devices such as tablets and iOS devices.