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  1. Article ; Online: Virtual emergency department: It is not all in the name.

    Staib, Andrew / Gourley, Stephen

    Emergency medicine Australasia : EMA

    2023  Volume 35, Issue 6, Page(s) 894–895

    MeSH term(s) Humans ; Emergency Service, Hospital ; Names
    Language English
    Publishing date 2023-11-16
    Publishing country Australia
    Document type Editorial ; Comment
    ZDB-ID 2161824-0
    ISSN 1742-6723 ; 1742-6731 ; 1035-6851
    ISSN (online) 1742-6723
    ISSN 1742-6731 ; 1035-6851
    DOI 10.1111/1742-6723.14324
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    Zs.A 5940: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Defining emergency medicine.

    Crofton, Andrew K / Staib, Andrew N

    Emergency medicine Australasia : EMA

    2022  Volume 34, Issue 4, Page(s) 642–643

    Abstract: The COVID-19 pandemic has led to the development of alternative means of accessing unplanned care in order to avoid unnecessary ED presentations and hospital admissions. We explore the definition of emergency medicine, which patients are better served by ...

    Abstract The COVID-19 pandemic has led to the development of alternative means of accessing unplanned care in order to avoid unnecessary ED presentations and hospital admissions. We explore the definition of emergency medicine, which patients are better served by accessing unplanned hospital care via alternative pathways, and the concept of emergency care completion.
    MeSH term(s) COVID-19 ; Emergency Medicine ; Emergency Service, Hospital ; Hospitalization ; Humans ; Pandemics ; Retrospective Studies
    Language English
    Publishing date 2022-04-26
    Publishing country Australia
    Document type Journal Article
    ZDB-ID 2161824-0
    ISSN 1742-6723 ; 1742-6731 ; 1035-6851
    ISSN (online) 1742-6723
    ISSN 1742-6731 ; 1035-6851
    DOI 10.1111/1742-6723.14001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5940: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  3. Article ; Online: Forecasting emergency department waiting time using a state space representation.

    Trinh, Kelly / Staib, Andrew / Pak, Anton

    Statistics in medicine

    2023  Volume 42, Issue 24, Page(s) 4458–4483

    Abstract: The provision of waiting time information in emergency departments (ED) has become an increasingly popular practice due to its positive impact on patient experience and ED demand management. However, little scientific attention has been given to the ... ...

    Abstract The provision of waiting time information in emergency departments (ED) has become an increasingly popular practice due to its positive impact on patient experience and ED demand management. However, little scientific attention has been given to the quality and quantity of waiting time information presented to patients. To improve both aspects, we propose a set of state space models with flexible error structures to forecast ED waiting time for low acuity patients. Our approach utilizes a Bayesian framework to generate uncertainties associated with the forecasts. We find that the state-space models with flexible error structures significantly improve forecast accuracy of ED waiting time compared to the benchmark, which is the rolling average model. Specifically, incorporating time-varying and correlated error terms reduces the root mean squared errors of the benchmark by 10%. Furthermore, treating zero-recorded waiting times as unobserved values improves forecast performance. Our proposed model has the ability to provide patient-centric waiting time information. By offering more accurate and informative waiting time information, our model can help patients make better informed decisions and ultimately enhance their ED experience.
    Language English
    Publishing date 2023-08-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.9870
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    Zs.A 1756: Show issues Location:
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  4. Article ; Online: The SNOMED dilemma: How to use it, not whether to use it.

    Staib, Andrew / Hugman, Andrew

    Emergency medicine Australasia : EMA

    2020  Volume 32, Issue 2, Page(s) 361–362

    MeSH term(s) Clinical Coding ; Data Collection ; Emergency Service, Hospital ; New South Wales ; Systematized Nomenclature of Medicine
    Language English
    Publishing date 2020-01-20
    Publishing country Australia
    Document type Letter ; Comment
    ZDB-ID 2161824-0
    ISSN 1742-6723 ; 1742-6731 ; 1035-6851
    ISSN (online) 1742-6723
    ISSN 1742-6731 ; 1035-6851
    DOI 10.1111/1742-6723.13465
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5940: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Modelling a two-stream emergency department segregation and admission system from COVID-19 early rapid antigen testing: A pilot study.

    Johnston, Amy / Wong, Andy / Appo, Casey / Eley, Robert / Staib, Andrew

    Emergency medicine Australasia : EMA

    2023  Volume 36, Issue 2, Page(s) 283–287

    Abstract: Objective: Many factors influence patient flow through an ED, including streaming, treatment spaces and staff resources. This pilot study explored and compared real time patient flow using a single-stream system versus varying configurations of possible ...

    Abstract Objective: Many factors influence patient flow through an ED, including streaming, treatment spaces and staff resources. This pilot study explored and compared real time patient flow using a single-stream system versus varying configurations of possible two-stream systems using computer simulation.
    Methods: Simulation modelling was used to assess the delay in treatment of a rapid-antigen-tested-based, two-stream model for patient flow through ED during the peak phase of the COVID pandemic.
    Results: Modelling two-stream configuration for all patients (minimum time to be seen for both COVID-positive and COVID-negative patients) showed that in the case study ED, a two-stream system and linked changes in bed configuration for managing the risks of infection can impact delays in treatment.
    Conclusions: Data-driven modelling within specific clinical settings can inform the (in)efficiency of patient flow processes and help clinicians and managers make evidence-based decisions about patient transition through EDs. This can assist with reconfiguration of ED patient streaming particularly during periods of unique need, such as the recent COVID-19 pandemic.
    MeSH term(s) Humans ; COVID-19 ; Pilot Projects ; Computer Simulation ; Pandemics ; Emergency Service, Hospital
    Language English
    Publishing date 2023-11-29
    Publishing country Australia
    Document type Journal Article
    ZDB-ID 2161824-0
    ISSN 1742-6723 ; 1742-6731 ; 1035-6851
    ISSN (online) 1742-6723
    ISSN 1742-6731 ; 1035-6851
    DOI 10.1111/1742-6723.14351
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 5940: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Article ; Online: Emergency medicine's COVID future: Facing the triple challenge after flattening the curve.

    Staib, Andrew / Small, Niall

    Emergency medicine Australasia : EMA

    2020  Volume 32, Issue 5, Page(s) 880–882

    Abstract: After successfully avoiding the situations experienced by some countries, Australasian EDs now face a future in which the ongoing threat of COVID-19 is added to the traditional challenges in providing quality emergency care. The contribution of emergency ...

    Abstract After successfully avoiding the situations experienced by some countries, Australasian EDs now face a future in which the ongoing threat of COVID-19 is added to the traditional challenges in providing quality emergency care. The contribution of emergency medicine to the national containment strategy adds a new dimension to the demands placed on emergency medicine in Australia and similarly, to the elimination strategy employed in New Zealand. These demands will best be met by a considered, planned and resourced approach that will challenge traditional measures of 'ED efficiency'.
    MeSH term(s) Australia ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Coronavirus Infections/therapy ; Emergency Medicine/organization & administration ; Emergency Service, Hospital/organization & administration ; Female ; Health Planning/methods ; Humans ; Male ; Needs Assessment ; New Zealand ; Organizational Innovation ; Pandemics/prevention & control ; Pandemics/statistics & numerical data ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Pneumonia, Viral/therapy ; Risk Assessment
    Keywords covid19
    Language English
    Publishing date 2020-06-22
    Publishing country Australia
    Document type Journal Article ; Review
    ZDB-ID 2161824-0
    ISSN 1742-6723 ; 1742-6731 ; 1035-6851
    ISSN (online) 1742-6723
    ISSN 1742-6731 ; 1035-6851
    DOI 10.1111/1742-6723.13566
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5940: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: Introduction of QScript real-time prescription monitoring system was associated with a fall in pregabalin poisoning presentations to a clinical toxicology unit.

    Holford, Amanda / Martin, Christopher / Staib, Andrew / Harris, Keith / Isoardi, Katherine Z

    Emergency medicine Australasia : EMA

    2023  Volume 35, Issue 5, Page(s) 879–881

    Abstract: Objective: To investigate the impact of QScript implementation on pregabalin-related poisoning presentations to the ED.: Methods: This is a retrospective review of pregabalin-related poisoning presentations to a tertiary Australian ED in the 4 years ... ...

    Abstract Objective: To investigate the impact of QScript implementation on pregabalin-related poisoning presentations to the ED.
    Methods: This is a retrospective review of pregabalin-related poisoning presentations to a tertiary Australian ED in the 4 years prior to, and 1 year following the introduction of QScript real-time prescription monitoring system.
    Results: Pregabalin-related poisoning presentations fell by 28% from an average of 98 presentations annually over the 4 years prior to QScript implementation to 71 in 2022. The severity of poisonings was similar over the periods.
    Conclusions: The introduction of QScript was associated with a reduction in pregabalin-related poisoning presentations.
    MeSH term(s) Humans ; Pregabalin/therapeutic use ; Prescription Drug Monitoring Programs ; Australia/epidemiology
    Chemical Substances Pregabalin (55JG375S6M)
    Language English
    Publishing date 2023-08-17
    Publishing country Australia
    Document type Journal Article
    ZDB-ID 2161824-0
    ISSN 1742-6723 ; 1742-6731 ; 1035-6851
    ISSN (online) 1742-6723
    ISSN 1742-6731 ; 1035-6851
    DOI 10.1111/1742-6723.14297
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5940: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  8. Article: Emergency medicine's COVID future: Facing the triple challenge after flattening the curve

    Staib, Andrew / Small, Niall

    Emerg Med Australas

    Abstract: After successfully avoiding the situations experienced by some countries, Australasian EDs now face a future in which the ongoing threat of COVID-19 is added to the traditional challenges in providing quality emergency care. The contribution of emergency ...

    Abstract After successfully avoiding the situations experienced by some countries, Australasian EDs now face a future in which the ongoing threat of COVID-19 is added to the traditional challenges in providing quality emergency care. The contribution of emergency medicine to the national containment strategy adds a new dimension to the demands placed on emergency medicine in Australia and similarly, to the elimination strategy employed in New Zealand. These demands will best be met by a considered, planned and resourced approach that will challenge traditional measures of 'ED efficiency'.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #459184
    Database COVID19

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  9. Article ; Online: Emergency medicine's COVID future

    Staib, Andrew / Small, Niall

    facing the triple challenge after flattening the curve

    2020  

    Abstract: After successfully avoiding the situations experienced by some countries, Australasian EDs now face a future in which the ongoing threat of COVID-19 is added to the traditional challenges in providing quality emergency care. The contribution of emergency ...

    Abstract After successfully avoiding the situations experienced by some countries, Australasian EDs now face a future in which the ongoing threat of COVID-19 is added to the traditional challenges in providing quality emergency care. The contribution of emergency medicine to the national containment strategy adds a new dimension to the demands placed on emergency medicine in Australia and similarly, to the elimination strategy employed in New Zealand. These demands will best be met by a considered, planned and resourced approach that will challenge traditional measures of ‘ED efficiency’.
    Keywords Coronavirus ; COVID-19 ; Pandemic containment ; 2711 Emergency Medicine ; covid19
    Language English
    Publishing date 2020-06-02
    Publisher Wiley-Blackwell Publishing Asia
    Publishing country au
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Predicting waiting time to treatment for emergency department patients.

    Pak, Anton / Gannon, Brenda / Staib, Andrew

    International journal of medical informatics

    2020  Volume 145, Page(s) 104303

    Abstract: Background: The current systems of reporting waiting time to patients in public emergency departments (EDs) has largely relied on rolling average or median estimators which have limited accuracy. This study proposes to use machine learning (ML) ... ...

    Abstract Background: The current systems of reporting waiting time to patients in public emergency departments (EDs) has largely relied on rolling average or median estimators which have limited accuracy. This study proposes to use machine learning (ML) algorithms that significantly improve waiting time forecasts.
    Methods: By implementing ML algorithms and using a large set of queueing and service flow variables, we provide evidence of the improvement in waiting time predictions for low acuity ED patients assigned to the waiting room. In addition to the mean squared prediction error (MSPE) and mean absolute prediction error (MAPE), we advocate to use the percentage of underpredicted observations. The use of ML algorithms is motivated by their advantages in exploring data connections in flexible ways, identifying relevant predictors, and preventing overfitting of the data. We also use quantile regression to generate time forecasts which may better address the patient's asymmetric perception of underpredicted and overpredicted ED waiting times.
    Results: Using queueing and service flow variables together with information on diurnal fluctuations, ML models outperform the best rolling average by over 20 % with respect to MSPE and quantile regression reduces the number of patients with large underpredicted waiting times by 42 %.
    Conclusion: We find robust evidence that the proposed estimators generate more accurate ED waiting time predictions than the rolling average. We also show that to increase the predictive accuracy further, a hospital ED may decide to provide predictions to patients registered only during the daytime when the ED operates at full capacity, thus translating to more predictive service rates and the demand for treatments.
    MeSH term(s) Algorithms ; Emergency Service, Hospital ; Humans ; Length of Stay ; Machine Learning ; Time-to-Treatment ; Waiting Lists
    Language English
    Publishing date 2020-10-18
    Publishing country Ireland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1466296-6
    ISSN 1872-8243 ; 1386-5056
    ISSN (online) 1872-8243
    ISSN 1386-5056
    DOI 10.1016/j.ijmedinf.2020.104303
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