Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study

Walker, Katie; Jiarpakdee, Jirayus; Loupis, Anne; Tantithamthavorn, Chakkrit; Joe, Keith; Ben‐Meir, Michael; Akhlaghi, Hamed; Hutton, Jennie; Wang, Wei Chun; Stephenson, Michael; Blecher, Gabriel; Buntine, Paul; Sweeny, Amy; Turhan, Burak

doi:10.1016/j.annemergmed.2021.02.010

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Proposals for improving ambulance‐to‐ED transfer times include financial penalties for hospitals with longer offload times, algorithms that predict ambulance waiting times and direct ambulances to less crowded hospitals, dedicated offload zones, and offload nursing coordinators 19‐21 . However, these measures have limited benefits and may bring their own problems, such as increased mortality risk for patients with myocardial infarction if their ambulance is diverted 22 .…”

Section: Discussionmentioning

confidence: 99%

The influence of ambulance offload time on 30‐day risks of death and re‐presentation for patients with chest pain

Dawson

Stephenson

Nehme

et al. 2022

Medical Journal of Australia

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mbulance offload times are an increasing problem in Australian health care. Overcrowding in emergency departments (EDs) is causing more frequent and longer delays of ambulance-to-ED transfers. [1][2][3] The adverse effects of offload delays at the system level include associations with poorer ambulance response times, greater access block, longer ED and admission times, and cancellations of elective admissions and procedures. 2,3 The impact of offload delays at the patient level is less clear. Several studies have found delays to assessment and treatment, 4,5 but implications for clinical endpoints such as mortality and re-admission have not been examined in detail.Nearly one in ten calls for ambulance transport in Australia are for people with chest pain, who subsequently receive diagnoses ranging from the benign to the urgent, such as acute coronary syndrome and acute aortic pathology. 6 As timely care is associated with better outcomes for people with a variety of conditions that cause chest pain, 7-9 they are an ideal group in which to examine the influence of ambulance offload time on clinical outcomes.In a large population-based study of ED presentations by people with non-traumatic chest pain, we assessed whether ambulance offload time influenced the risks of death or ambulance reattendance with chest pain within 30 days of the initial ED presentation. MethodsWe undertook a population-based observational cohort study of consecutive presentations by adults with non-traumatic chest pain transported by ambulance to Victorian EDs during 1 January 2015 -30 June 2019. To follow the complete patient journey, pre-hospital data entered by paramedics into Victorian Ambulance Clinical Information System (VACIS) electronic patient care records were linked to the Victorian Emergency Minimum Dataset (VEMD), the Victorian Admitted Episodes Dataset (VAED), and the Victorian Death Index (VDI) (Supporting Information, table 1). Full details regarding the study population and linkage processes have been published, 10 and are also summarised in the Supporting Information (supplementary methods). Study populationConsecutive patients aged 18 years or more were included in our analysis if their VACIS records included pain in the chest, or they received a final or secondary ambulance diagnosis of ischaemic chest pain, acute coronary syndrome, acute myocardial infarction, pleuritic pain, or angina. We excluded patients with traumatic chest pain or ST elevation myocardial infarction (pre-hospital electrocardiography), and those who

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Section: Discussionmentioning

confidence: 99%

The influence of ambulance offload time on 30‐day risks of death and re‐presentation for patients with chest pain

Dawson

Stephenson

Nehme

et al. 2022

Medical Journal of Australia

Self Cite

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“…In addition, AI is facilitating the harnessing of new technology suitable for ED applications and research, such as natural language processing,13 radiomics14 and machine vision 15. Large data repositories are being curated and leveraged to explore correlations between patient variables and urgent care outcomes 10 16 17. Examples of recent studies with a reasonable rationale for using AI methods are summarised in box 1.…”

Section: What Is the Promise Of Ai For Em?mentioning

confidence: 99%

Understanding and interpreting artificial intelligence, machine learning and deep learning in Emergency Medicine

et al. 2022

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“…Once in an ED, wait estimates are used to meet patient emotional, logistic and physical needs and also assist paramedics whilst patients wait on stretchers [41]. Wait times can be estimated using prediction models, with varied accuracy [2, 37, 40, 42]. Despite the high volume of patients exposed to ED waits, when displays are designed there is little to no input from patients and families who are the end users of the displays.…”

Section: Introductionmentioning

confidence: 99%

Visualising Emergency Department Wait Times; Rapid Iterative Testing to Determine Patient Preferences for Displays

Walker

Potter

Hwang

et al. 2022

Preprint

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Visualising patient wait times in emergency departments for patients and families is increasingly common, following the development of prediction models using routinely collected patient demographic, urgency and flow data. Consumers of an emergency department wait time display will have culturally and linguistically diverse backgrounds, are more likely to be from under-served populations and will have varied data literacy skills. The wait times are uncertain, the information is presented when people are emotionally and physically challenged, and the predictions may inform high stakes decisions. In such a stressful environment, simplicity is crucial and the visual language must cater to the diverse audience. When wait times are conveyed well, patient experience improves. Designers must ensure the visualisation is patient-centred and that data are consistently and correctly interpreted. In this article, we present the results of a design study at three hospitals in Melbourne, Australia, undertaken in 2021. We used rapid iterative testing and evaluation methodology, with patients and families from diverse backgrounds as participants, to develop and validate a wait time display. We present the design process and the results of this project. Patients, families and staff were eligible to participate if they were awaiting care in the emergency department, or worked in patient reception and waiting areas. The patient-centred approach taken in our design process varies greatly from past work led by hospital administrations, and the resulting visualisations are very distinct. Most currently displayed wait time visualisations could be adapted to better meet end-user needs. Also of note, we found that techniques developed by visualisation researchers for conveying temporal uncertainty tended to overwhelm the diverse audience rather than inform. There is a need to balance precise and comprehensive information presentation against the strong need for simplicity in such a stressful environment.

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Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study

Cited by 5 publications

References 19 publications

The influence of ambulance offload time on 30‐day risks of death and re‐presentation for patients with chest pain

The influence of ambulance offload time on 30‐day risks of death and re‐presentation for patients with chest pain

Understanding and interpreting artificial intelligence, machine learning and deep learning in Emergency Medicine

Visualising Emergency Department Wait Times; Rapid Iterative Testing to Determine Patient Preferences for Displays

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