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

Please wait, while we are loading the content...

Review article: Emergency department data sharing to reduce alcohol-related violence: a systematic review of the feasibility and effectiveness of community-level interventions.

Prehospital management of supraventricular tachycardia in Victoria, Australia: epidemiology and effectiveness of therapies.

Demand for emergency department services in the elderly: an 11 year analysis of the Greater Sydney Area.

Mild traumatic brain injury in children: management practices in the acute care setting.

Overdose with modified-release paracetamol (Panadol Osteo®) presenting to a metropolitan emergency medicine network: a case series.

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

Senior work-up assessment and treatment team in an emergency department: a randomised control trial.

Prospective observational study of emergent endotracheal intubation practice in the intensive care unit and emergency department of an Australian regional tertiary hospital.

Trainee perspectives on usefulness of the Trainee Research Requirement.

Year: 2014 Volume: 26 Issue: 3

Year: 2014 Volume: 26 Issue: 2

Year: 2014 Volume: 26 Issue: 1

Year: 2013 Volume: 25

Year: 2012 Volume: 24

Year: 2011 Volume: 23

Year: 2010 Volume: 22

Year: 2009 Volume: 21

Year: 2008 Volume: 20

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

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Kim, Susan W. Li, Jordan Y. Hakendorf, Paul Teubner, David Jo Ben-Tovim, David I. Thompson, Campbell H.
Spatial Coverage	Australia
Description	Country affiliation: Australia Author Affiliation: Kim SW ( Flinders Centre for Epidemiology and Biostatistics, School of Medicine, Flinders University, Adelaide, South Australia, Australia.)
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.
File Format	HTM / HTML
ISSN	17426731
Issue Number	4
Volume Number	26
e-ISSN	17426723
Journal	Emergency Medicine Australasia
Language	English
Publisher	Wiley-Blackwell
Publisher Date	2014-08-01
Publisher Place	Australia
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Emergency Medicine Emergency Service, Hospital Statistics & Numerical Data Hospitalization Risk Assessment Methods Adult Aged Aged, 80 And Over Australia Clinical Competence Standards Female Hematologic Tests Humans Length Of Stay Logistic Models Male Middle Aged Nursing Assessment Predictive Value Of Tests Retrospective Studies Time Factors Triage Journal Article Observational Study
Content Type	Text
Resource Type	Article
Subject	Emergency Medicine

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Admission blood glucose predicts mortality and length of stay in patients admitted through the emergency department.

The predictive validity of RETTS-HEV as an acuity triage tool in the emergency department of a Danish Regional Hospital.

Triage nurse prediction of hospital admission.

Lower rates of appropriate initial diagnosis in older emergency department patients associated with hospital length of stay.

Model to predict inpatient mortality from information gathered at presentation to an emergency department: The Triage Information Mortality Model (TIMM).

Predictive variables of an emergency department quality and performance indicator: a 1-year prospective, observational, cohort study evaluating hospital and emergency census variables and emergency department time interval measurements.

Common laboratory tests predict imminent medical emergency team calls, intensive care unit admission or death in emergency department patients.

Emergency department rapid medical assessment: overall effect and mechanistic considerations.

Identification of seniors at risk (ISAR) screening tool in the emergency department: implementation using the plan-do-study-act model and validation results.

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

Similar Documents

Admission blood glucose predicts mortality and length of stay in patients admitted through the emergency department.

The predictive validity of RETTS-HEV as an acuity triage tool in the emergency department of a Danish Regional Hospital.

Triage nurse prediction of hospital admission.

Lower rates of appropriate initial diagnosis in older emergency department patients associated with hospital length of stay.

Model to predict inpatient mortality from information gathered at presentation to an emergency department: The Triage Information Mortality Model (TIMM).

Predictive variables of an emergency department quality and performance indicator: a 1-year prospective, observational, cohort study evaluating hospital and emergency census variables and emergency department time interval measurements.

Common laboratory tests predict imminent medical emergency team calls, intensive care unit admission or death in emergency department patients.

Emergency department rapid medical assessment: overall effect and mechanistic considerations.

Identification of seniors at risk (ISAR) screening tool in the emergency department: implementation using the plan-do-study-act model and validation results.

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