Identification of patients at risk for 30-day readmission: clinical insight beyond big data predicti...
Identification of patients at risk for 30-day readmission: clinical insight beyond big data prediction
About this item
Full title
Author / Creator
Publisher
Paterna: Ubiquity Press
Journal title
Language
English
Formats
Publication information
Publisher
Paterna: Ubiquity Press
Subjects
More information
Scope and Contents
Contents
IntroductionAutomated models based on electronic health records (EHR) predict high-risk readmission patients with moderate accuracy, however, clinicians alone do not have the ability to predict readmissions more effectively. The potential benefit of combining providers’ and automated capabilities in readmission risk prediction has not been previous...
Alternative Titles
Full title
Identification of patients at risk for 30-day readmission: clinical insight beyond big data prediction
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_3100587333
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3100587333
Other Identifiers
ISSN
1568-4156
E-ISSN
1568-4156
DOI
10.5334/ijic.ICIC20401
