Literature DB >> 29295260

Use of EHR Data to Identify Factors Affecting the Time to Fall.

Hyesil Jung1, Hyeoun-Ae Park1.   

Abstract

Although there are many studies of falls occurring in a hospital setting, research on factors affecting time to fall after admission is scarce. It is important for nurses to identify factors contributing to an early fall so that they can pay particular attention to patients with such factors. In this study, patients who sustained a fall were extracted from an adverse event reporting system and narrative nursing records of those hospitalized between January 2015 and May 2016. We used the electronic health records of ten different data sources to extract fall-related variables; the data were integrated according to normalization criteria. Univariate and multiple linear regression analyses were used to identify factors influencing the time to fall from admission. About 49% of fallers fell within the first week after admission. A walking disorder, comorbid disease, intravenous therapy, and arterial lines were related to early falls.

Entities:  

Keywords:  Accidental Falls; Electronic Health Records; Information Storage and Retrieval

Mesh:

Year:  2017        PMID: 29295260

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  1 in total

1.  Patient-Level Fall Risk Prediction Using the Observational Medical Outcomes Partnership's Common Data Model: Pilot Feasibility Study.

Authors:  Hyesil Jung; Sooyoung Yoo; Seok Kim; Eunjeong Heo; Borham Kim; Ho-Young Lee; Hee Hwang
Journal:  JMIR Med Inform       Date:  2022-03-11
  1 in total

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