Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care.

ABSTRACT:

Background/objective

Between 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate prediction of hospitalization risk can be one strategy to prevent them. In recent years, machine learning-based predictive modeling has become widely used for building risk models. This study aimed to compare the predictive performance of four risk models built with various data sources for hospitalization and ED visits in HHC.

Methods

Four risk models were built using different variables from two data sources: structured data (i.e., Outcome and Assessment Information Set (OASIS) and other assessment items from the electronic health record (EHR)) and unst

SUBMITTER: Song J 

PROVIDER: S-EPMC9825202 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature