Displaying emergency patient estimated wait times: A multi‐centre, qualitative study of patient, community, paramedic and health administrator perspectives

Walker, Katie; Stephenson, Melanie; Loupis, Anne; Ben‐Meir, Michael; Joe, Keith; Stephenson, Michael; Lowthian, Judy; Yip, Beatrice; Wu, Elena; Hansen, Kim; Rosler, Rachel; Buntine, Paul; Hutton, Jennie

doi:10.1111/1742-6723.13640

Cited by 9 publications

(12 citation statements)

References 19 publications

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“…The outcome choices and definitions were informed by a large, multisite, qualitative study of community members, consumers, paramedics and health administrators. [5] These participants recommended a prediction accuracy of +/-30 minutes (unpublished data).…”

Section: Discussionmentioning

confidence: 99%

“…The primary outcome of this study was determined by a qualitative study involving patients, the public and other stakeholders. [5] Consumers and community stakeholders contributed to the design and write up of the study.…”

Section: Patient and Public Involvementmentioning

confidence: 99%

“…[1][2][3][4] Wait time visibility assists with meeting physical, logistic and psychological needs of patients. [5] There is increasing consumer advocacy for transparency of information about health service resources. There is also health service interest in displaying wait times.…”

Section: Main Manuscript Introductionmentioning

confidence: 99%

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Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study

Walker¹,

Jiarpakdee

Loupis

et al. 2021

Emerg Med J

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ObjectivePatients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments.MethodsTwelve emergency departments provided 3 years of retrospective administrative data from Australia (2017–2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020).ResultsThere were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period.ConclusionsElectronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

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Section: Discussionmentioning

confidence: 99%

Section: Patient and Public Involvementmentioning

confidence: 99%

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Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study

Walker¹,

Jiarpakdee

Loupis

et al. 2021

Emerg Med J

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“…We chose a single prediction based on consumer feedback that triage categories are not understood by patients and families. [5] Ang et al modelled low-acuity patients only and reported performance in terms of Mean Squared Error, arguing that median based measures tend to underestimate due to right skewed distribution of wait-times. [7] We observed this with Rolling Average models, but not others.…”

Section: Discussionmentioning

confidence: 99%

Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study

Walker¹,

Jiarpakdee

Loupis

et al. 2021

Preprint

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Objective Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. Methods Twelve emergency departments provided three years of retrospective administrative data from Australia (2017-19). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). Results There were 1,930,609 patient episodes analysed and median site wait times varied from 24 to 54 minutes. Individual site model prediction median absolute errors varied from +/-22.6 minutes (95%CI 22.4,22.9) to +/- 44.0 minutes (95%CI 43.4,44.4). Global model prediction median absolute errors varied from +/-33.9 minutes (95%CI 33.4, 34.0) to +/-43.8 minutes (95%CI 43.7, 43.9). Random forest and linear regression models performed the best, rolling average models under-estimated wait times. Important variables were triage category, last-k patient average wait time, and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. Conclusions Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site specific factors.

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“…We recently published a Victorian qualitative study, conducted in ED waiting rooms, the community and health service offices, which explored ED stakeholder views on wait time visibility. 1 Wait time visibility was important to patients, families and paramedics but we did not investigate which wait times consumers valued the most. Emergency medicine operations literature generally assumes that door-to-provider is the most important wait time for patients and families.…”

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confidence: 99%

What emergency department wait times do community members want to see displayed?

Walker

Yip

et al. 2020

Emerg Medicine Australasia

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300 consecutive patients who receive an ED diagnosis of cellulitis. All patients, regardless of antibiotic agent, dose and route of administration will have clinical features assessed during ED and hospital stay as well as at days 3, 7 and 14 after presentation to the ED. We will also assess relevant patient centred outcomes (Table 1). We anticipate that these data will provide valuable information to better understand the natural course and therapeutic response of this common condition and provide a more robust definition of 'treatment failure'.

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Displaying emergency patient estimated wait times: A multi‐centre, qualitative study of patient, community, paramedic and health administrator perspectives

Abstract: doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Cited by 9 publications

References 19 publications

Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study

Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study

Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study

What emergency department wait times do community members want to see displayed?

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