Literature DB >> 22906702

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.

Enrique Casalino1, Christophe Choquet, Julien Bernard, Abigael Debit, Benoit Doumenc, Audrey Berthoumieu, Mathias Wargon.   

Abstract

OBJECTIVE: Emergency department (ED) crowding impacts negatively on quality of care. The aim was to determine the association between ED quality and input, throughput and output-associated variables.
METHODS: This 1-year, prospective, observational, cohort study determined the daily percentage of patients leaving the ED in <4 h (ED quality and performance indicator; EDQPI). According to the median EDQPI two groups were defined: best-days and bad-days. Hospital and ED variables and time interval metrics were evaluated as predictors.
RESULTS: Data were obtained for 67 307 patients over 364 days. Differences were observed between the two groups in unadjusted analysis: number of daily visits, number of patients as a function of final disposition, number boarding in the ED, and time interval metrics including wait time to triage nurse and ED provider, time from ED admission to decision, time from decision to departure and length of stay (LOS) as a function of final disposition. Five variables remained significant predictors for bad-days in multivariate analysis: wait time to triage nurse (OR 2.36; 95% CI 1.36 to 4.11; p=0.002), wait time to ED provider (OR 1.93; 95% CI 1.05 to 3.54; p=0.03), number of patients admitted to hospital (OR 1.86; 95% CI 1.09 to 3.19; p=0.02), LOS of non-admitted patients (OR 9.5; 95% CI 5.17 to 17.48; p<0.000001) and LOS of patients admitted to hospital (OR 2.46; 95% CI 1.44 to 4.2; p=0.0009).
CONCLUSIONS: Throughput is the major determinant of EDQPI, notably time interval reflecting the work dynamics of medical and nursing teams and the efficacy of fast-track routes for low-complexity patients. Output also significantly impacted on EDQPI, particularly the capacity to reduce the LOS of admitted patients.

Entities:  

Keywords:  Acute coronary syndrome; HIV; bacterial; cardiac care; efficiency; emergency care systems; emergency department management; infectious diseases; neurology; stroke

Mesh:

Year:  2012        PMID: 22906702     DOI: 10.1136/emermed-2012-201404

Source DB:  PubMed          Journal:  Emerg Med J        ISSN: 1472-0205            Impact factor:   2.740


  15 in total

1.  [National data set "emergency department": development, structure and approval by the Deutsche Interdisziplinäre Vereinigung für Intensivmedizin und Notfallmedizin].

Authors:  M Kulla; R Röhrig; M Helm; M Bernhard; A Gries; R Lefering; F Walcher
Journal:  Anaesthesist       Date:  2014-03       Impact factor: 1.041

2.  [Potential for the survey of quality indicators based on a national emergency department registry : A systematic literature search].

Authors:  A C Hörster; M Kulla; D Brammen; R Lefering
Journal:  Med Klin Intensivmed Notfmed       Date:  2016-06-29       Impact factor: 0.840

3.  Emergency Medicine Resident Efficiency and Emergency Department Crowding.

Authors:  Ryan Kirby; Richard D Robinson; Sasha Dib; Daisha Mclarty; Sajid Shaikh; Radhika Cheeti; Amy F Ho; Chet D Schrader; Nestor R Zenarosa; Hao Wang
Journal:  AEM Educ Train       Date:  2019-02-27

4.  Management of information within emergencies departments in developing countries: analysis at the National Emergency Department in Benin.

Authors:  Yolaine Glèlè Ahanhanzo; Alphonse Kpozehouen; Ghislain Sopoh; Charles Sossa-Jérôme; Laurent Ouedraogo; Michèle Wilmet-Dramaix
Journal:  Pan Afr Med J       Date:  2016-07-21

5.  The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia.

Authors:  Michael M Dinh; Saartje Berendsen Russell; Kendall J Bein; Kris Rogers; David Muscatello; Richard Paoloni; Jon Hayman; Dane R Chalkley; Rebecca Ivers
Journal:  BMC Emerg Med       Date:  2016-12-03

6.  Predicting Length of Stay among Patients Discharged from the Emergency Department-Using an Accelerated Failure Time Model.

Authors:  Chung-Hsien Chaou; Hsiu-Hsi Chen; Shu-Hui Chang; Petrus Tang; Shin-Liang Pan; Amy Ming-Fang Yen; Te-Fa Chiu
Journal:  PLoS One       Date:  2017-01-20       Impact factor: 3.240

7.  Discovering the underlying typology of emergency departments.

Authors:  Marine Demarquet; Laurie Fraticelli; Julie Freyssenge; Clément Claustre; Mikaël Martinez; Jonathan Duchenne; Carlos El Khoury; Abdesslam Redjaline; Karim Tazarourte
Journal:  BMC Med Res Methodol       Date:  2021-06-05       Impact factor: 4.615

8.  Emergency Department Performance Indexes Before and After Establishment of Emergency Medicine.

Authors:  Behrooz Hashemi; Alireza Baratloo; Farhad Rahmati; Mohammad Mehdi Forouzanfar; Maryam Motamedi; Saeed Safari
Journal:  Emerg (Tehran)       Date:  2013

9.  Measuring and Analyzing Waiting Time Indicators of Patients' Admitted in Emergency Department: A Case Study.

Authors:  Saeed Amina; Ahmad Barrati; Jamil Sadeghifar; Marzeyh Sharifi; Zahra Toulideh; Hasan Abolghasem Gorji; Negar Feazbakhsh
Journal:  Glob J Health Sci       Date:  2015-05-17

10.  Improved quality and efficiency after the introduction of physician-led team triage in an emergency department.

Authors:  Lena Burström; Marie-Louise Engström; Maaret Castrén; Tony Wiklund; Mats Enlund
Journal:  Ups J Med Sci       Date:  2015-11-09       Impact factor: 2.384

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