Risk Factors for Emergency Department Short Time Readmission in Stratified Population.
Background. Emergency department (ED) readmissions are considered an indicator of healthcare quality that is particularly relevant in older adults. The primary objective of this study was to identify key factors for predicting patients returning to the ED within 30 days of being discharged. Methods....
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 8 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/17/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=113630074&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113630074 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/17/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 113630074 113630074 113630074 10.1155/2015/685067 113630074 ppf: 1 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Risk Factors for Emergency Department Short Time Readmission in Stratified Population. aug: au: Besga, Ariadna Ayerdi, Borja Alcalde, Guillermo Manzano, Alberto Lopetegui, Pedro Graña, Manuel González-Pinto, Ana affil: Emergency Department, Álava University Hospital, 01010 Vitoria, Spain sug: subj: Emergency Service Readmission Risk Assessment Time Factors Human Emergency Patients Emergency Care Stratified Random Sample Aged Comorbidity Referral and Consultation Quality of Health Care Aged: 65+ years ab: Background. Emergency department (ED) readmissions are considered an indicator of healthcare quality that is particularly relevant in older adults. The primary objective of this study was to identify key factors for predicting patients returning to the ED within 30 days of being discharged. Methods. We analysed patients who attended our ED in June 2014, stratified into four groups based on the Kaiser pyramid. We collected data on more than 100 variables per case including demographic and clinical characteristics and drug treatments. We identified the variables with the highest discriminating power to predict ED readmission and constructed classifiers using machine learning methods to provide predictions. Results. Classifier performance distinguishing between patients who were and were not readmitted (within 30 days), in terms of average accuracy (AC). The variables with the greatest discriminating power were age, comorbidity, reasons for consultation, social factors, and drug treatments. Conclusions. It is possible to predict readmissions in stratified groups with high accuracy and to identify the most important factors influencing the event. Therefore, it will be possible to develop interventions to improve the quality of care provided to ED patients. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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