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Original research
Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study
  1. Katie Walker1,2,3,
  2. Jirayus Jiarpakdee4,
  3. Anne Loupis3,
  4. Chakkrit Tantithamthavorn4,
  5. Keith Joe3,5,
  6. Michael Ben-Meir3,6,
  7. Hamed Akhlaghi7,8,
  8. Jennie Hutton7,
  9. Wei Wang9,10,
  10. Michael Stephenson11,12,
  11. Gabriel Blecher13,14,
  12. Buntine Paul15,16,
  13. Amy Sweeny17,18,
  14. Burak Turhan4,19
  15. Australasian College for Emergency Medicine, Clinical Trials Network
    1. 1 Emergency Department, Casey Hospital, Berwick, Victoria, Australia
    2. 2 Health Services, Faculty of Medicine Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
    3. 3 Emergency Department, Cabrini Institute, Melbourne, Victoria, Australia
    4. 4 Department of Software Systems and Cybersecurity, Monash University, Melbourne, Victoria, Australia
    5. 5 MADA, Monash University, Clayton, Victoria, Australia
    6. 6 Emergency Department, Austin Health, Heidelberg, Victoria, Australia
    7. 7 Department of Emergency Medicine, St Vincent's Hospital Melbourne Pty Ltd, Fitzroy, Victoria, Australia
    8. 8 Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia
    9. 9 Biostatistics, Cabrini Health, Malvern, Victoria, Australia
    10. 10 Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
    11. 11 Ambulance Victoria, Doncaster, Victoria, Australia
    12. 12 Community Emergency Health and Paramedic Practice, Monash University, Melbourne, Victoria, Australia
    13. 13 Emergency Program, Monash Health, Clayton, Victoria, Australia
    14. 14 School of Clinical Sciences, Monash University, Melbourne, Victoria, Australia
    15. 15 Emergency Medicine, Eastern Health, Melbourne, Victoria, Australia
    16. 16 Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia
    17. 17 Emergency, Gold Coast Hospital and Health Service, Southport, Queensland, Australia
    18. 18 Griffith University School of Medicine, Gold Coast, Queensland, Australia
    19. 19 Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Pohjois-⁠Pohjanmaa, Finland
    1. Correspondence to Professor Katie Walker, Emergency Department, Casey Hospital, Berwick, VIC 3806, Australia; katie_walker01{at}yahoo.com.au

    Abstract

    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 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).

    Results There 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.

    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.

    • emergency care systems
    • efficiency
    • emergency departments
    • emergency department management
    • emergency department operations
    • emergency department utilisation

    Data availability statement

    No data are available.

    https://doi.org/10.1136/emermed-2020-211000

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      Data availability statement

      No data are available.

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      Footnotes

      • Handling editor Shammi L Ramlakhan

      • Twitter @gabyblech, @EpidemicAmy

      • Collaborators Rachel Rosler: network sponsor, Melanie Stephenson: literature review, Kim Hansen: risk advisor, Ms Ella Martini: consumer, Dr Hamish Rodda: emergency informatics advisor, project sponsor, Dr Judy Lowthian: district nursing researcher.

      • Contributors Principal investigator: KW. Funding: KJ, KW, MB-M. Study design and protocol: KW, BT, CT, JJ, WW. Study protocol revisions: all authors. Ethics/governance: KW, AL. Site chief investigators: HA, GB, BP, KW, AS. Data collection: AL, HA, BP, KW, AS. Data analysis: JJ, CT, BT. Manuscript: KW, JJ, CT, BT. Manuscript revisions: all authors. Manuscript guaranteed by KW and BT.

      • Funding The Australian government, Medical Research Future Fund, via Monash Partners, funded this study. Researchers contributed in-kind donations of time. The Cabrini Institute and Monash University provided research infrastructure support.

      • Competing interests Some authors and collaborators are emergency physicians or directors, and others work in community health (prehospital and district nursing). One collaborator is a consumer. The Australian government, Medical Research Future Fund, via Monash Partners, funded this study. Researchers contributed in-kind donations of time. The Cabrini Institute and Monash University provided research infrastructure support.

      • Provenance and peer review Not commissioned; externally peer reviewed.

      • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.