Real-time prediction of mortality, readmission, and length of stay using electronic health record data.

ABSTRACT:

Objective

To develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs).

Materials and methods

A Bayesian Network model was built to estimate the probability of a hospitalized patient being "at home," in the hospital, or dead for each of the next 7 days. The network utilizes patient-specific administrative and laboratory data and is updated each time a new pathology test result becomes available. Electronic health records from 32 634 patients admitted to a Sydney metropolitan hospital via the emergency department from July 2008 through December 2011 were used. The model was tested on 2011 data and trained on the data of earlier years.

Results

The model achieved an

SUBMITTER: Cai X 

PROVIDER: S-EPMC7839923 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature