Literature DB >> 24934833

Predicting admission of patients by their presentation to the emergency department.

Susan W Kim1, Jordan Y Li, Paul Hakendorf, David Jo Teubner, David I Ben-Tovim, Campbell H Thompson.   

Abstract

OBJECTIVE: The present study aims to determine the importance of certain factors in predicting the need of hospital admission for a patient in the ED.
METHODS: This is a retrospective observational cohort study between January 2010 and March 2012. The characteristics, including blood test results, of 100,123 patients who presented to the ED of a tertiary referral urban hospital, were incorporated into models using logistic regression in an attempt to predict the likelihood of patients' disposition on leaving the ED. These models were compared with triage nurses' prediction of patient disposition.
RESULTS: Patient age, their initial presenting symptoms or diagnosis, Australasian Triage Scale category, mode of arrival, existence of any outside referral, triage time of day and day of the week were significant predictors of the patient's disposition (P < 0.001). The ordering of blood tests for any patient and the extent of abnormality of those tests increased the likelihood of admission. The accuracy of triage nurses' admission prediction was similar to that offered by a model that used the patients' presentation characteristics. The addition of blood tests to that model resulted in only 3% greater accuracy in prediction of patient disposition.
CONCLUSIONS: Certain characteristics of patients as they present to hospital predict their admission. The accuracy of the triage nurses' prediction for disposition of patients is the same as that afforded by a model constructed from these characteristics. Blood test results improve disposition accuracy only slightly so admission decisions should not always wait for these results.
© 2014 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

Entities:  

Keywords:  hospital emergency service; length of stay; patient admission; routine diagnostic test; triage

Mesh:

Year:  2014        PMID: 24934833     DOI: 10.1111/1742-6723.12252

Source DB:  PubMed          Journal:  Emerg Med Australas        ISSN: 1742-6723            Impact factor:   2.151


  7 in total

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5.  Changes in admission thresholds in English emergency departments.

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6.  Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing.

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7.  Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review.

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  7 in total

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