Objective To provide focused evaluation of predictive modeling of electronic medical record
(EMR) data to predict 30 day hospital readmission. Design Systematic review. Data source …

Objectives Hospital readmission risk prediction facilitates the identification of patients
potentially at high risk so that resources can be used more efficiently in terms of cost-benefit …

Background Identifying patients at risk of not achieving meaningful gains in long-term
postsurgical patient-reported outcome measures (PROMs) is important for improving patient …

Despite enormous enthusiasm, machine learning models are rarely translated into clinical
care and there is minimal evidence of clinical or economic impact. New conference venues …

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 …

Background: Hospital readmissions are one of the costliest challenges facing healthcare
systems, but conventional models fail to predict readmissions well. Many existing models …

Healthcare costs due to unplanned readmissions are high and negatively affect health and
wellness of patients. Hospital readmission is an undesirable outcome for elderly patients …

Predictive analytics using electronic health record (EHR) data have rapidly advanced over
the last decade. While model performance metrics have improved considerably, best …

Background Agile projects are statistically more likely to succeed then waterfall projects. The
overall aim of this study was to explore the nursing staffs' experiences with an agile …

Introduction Interoperable health information technologies, like electronic health records
(EHR) and health information exchange (HIE), provide greater access to patient information …