Volume 33, Issue 2 pp. 310-315

Original Research

Lost bed capacity in emergency departments: A descriptive analysis and data visualisation exploration

Paul M Middleton,

Paul M Middleton

South Western Emergency Research Institute, Sydney, New South Wales, Australia

South Western Sydney Clinical School, The University of New South Wales, Sydney, New South Wales, Australia

Search for more papers by this author

Nicholas Moore,

Corresponding Author

Nicholas Moore

[email protected]

orcid.org/0000-0002-7623-9697

South Western Emergency Research Institute, Sydney, New South Wales, Australia

South Western Sydney Clinical School, The University of New South Wales, Sydney, New South Wales, Australia

Correspondence: Dr Nicholas Moore, South Western Emergency Research Institute, c/o Ingham Institute for Applied Medical Research, 1 Campbell Street, Liverpool, NSW 2170, Australia. Email: [email protected]

Search for more papers by this author

Shiquan Ren,

Shiquan Ren

South Western Emergency Research Institute, Sydney, New South Wales, Australia

South Western Sydney Clinical School, The University of New South Wales, Sydney, New South Wales, Australia

Search for more papers by this author

Paul M Middleton,

Paul M Middleton

South Western Emergency Research Institute, Sydney, New South Wales, Australia

South Western Sydney Clinical School, The University of New South Wales, Sydney, New South Wales, Australia

Search for more papers by this author

Nicholas Moore,

Corresponding Author

Nicholas Moore

[email protected]

orcid.org/0000-0002-7623-9697

South Western Emergency Research Institute, Sydney, New South Wales, Australia

South Western Sydney Clinical School, The University of New South Wales, Sydney, New South Wales, Australia

Search for more papers by this author

Shiquan Ren,

Shiquan Ren

South Western Emergency Research Institute, Sydney, New South Wales, Australia

South Western Sydney Clinical School, The University of New South Wales, Sydney, New South Wales, Australia

Search for more papers by this author

First published: 13 October 2020

https://doi.org/10.1111/1742-6723.13633

Paul M Middleton, RGN, MBBS, DipIMCRCS (Ed), MMed (ClinEpi), MD, FRCS, FANZCP, FRCEM, FACEM, Clinical Associate Professor, Emergency Physician; Nicholas Moore, MBBS, MPH, Emergency Physician; Shiquan Ren, BSc, MSc, PhD, Lecturer, Statistician.

Share a link

Email
Wechat
Bluesky

Abstract

Objective

To map utilisation of bed resources within an ED over time, in order to determine the proportions of patient stay spent receiving emergency care versus non-emergent care. To produce visualisations that effectively convey this bed utilisation.

Methods

This was a retrospective cohort study using routinely collected administrative data, derived from the ED electronic health record (FirstNet; Cerner, North Kansas City, MO, USA), on all patients presenting to a single tertiary-level ED during a 1-month period and who were triaged to an acute area bed in order to undergo their emergency care. Resuscitation, subacute, fast track and paediatric area patients were excluded from analysis as firstly, in our institution the acute area processing appears to contribute most to barriers to patient flow and secondly, using acute area patients allowed us to approximate standardisation of acuity. Lost bed capacity (LBC) was defined as the time spent in an ED bed after an emergency clinician indicated that they were ready to depart the ED, as recorded in the electronic medical record.

Results

The overall proportion of time spent in an ED bed after completion of emergency care was 38.5% (interquartile range 21.8–47.2%). This time differed significantly between ‘discharged’ (1 min), ‘admitted-ward’ (72 min) and ‘admitted-critical care’ (110 min) groups. This was clearly revealed in our novel LBC heatmaps.

Conclusions

A considerable proportion of ED length of stay is because of patients remaining in an ED bed after their emergency care is concluded. Absolute time is much greater for admitted patients than for discharged patients, and therefore efforts to reduce LBC are likely to reduce overall ED length of stay. LBC heatmaps may provide an intuitively useful, potentially automated tool to understand these problems.

Open Research

Data availability statement

Data available on request due to privacy/ethical restrictions.

References

1 Australian Bureau of Statistics. Media Release – Australia's Population Grew by 1.5 per Cent (Media Release). 2019. [Cited 12 Jun 2020.] Available from URL: https://www.abs.gov.au/ausstats/[email protected]/lookup/3101.0Media%20Release1Jun%202019#:~:text=Australia's%20population%20grew%20by%201.5%20per%20cent%20during%20the%20year,annual%20increase%20of%20381%2C600%20people.%22
Google Scholar
2 Independent Hospital Pricing Authority. National Hospital Cost Data Collection Report, Public Sector, Round 22 (Financial Year 2017–18). 2020. [Cited 12 Jun 2020.] Available from URL: https://www.ihpa.gov.au/publications/national-hospital-cost-data-collection-report-public-sector-round-22-financial-year
Google Scholar
3Lowthian J, Curtis A, Jolley DJ, Stoelwinder JU, Mcneil J, Cameron P. Demand at the ED front door: ten-year trends in emergency department presentations. Med. J. Aust. 2012; 196: 128–32.
10.5694/mja11.10955
PubMed Web of Science® Google Scholar
4Forero R, Hillman KM, McCarthy S, Fatovich DM, Joseph AP, Richardson DB. Access block and ED overcrowding. Emerg. Med. Australas. 2010; 22: 119–35.
10.1111/j.1742-6723.2010.01270.x
CAS PubMed Web of Science® Google Scholar
5Kulstad EB, Sikka R, Sweis RT, Kelley KM, Rzechula KH. ED overcrowding is associated with an increased frequency of medication errors. Am. J. Emerg. Med. 2010; 28: 304–9.
10.1016/j.ajem.2008.12.014
PubMed Web of Science® Google Scholar
6Weiss SJ, Derlet R, Arndahl J et al. Estimating the degree of emergency department overcrowding in academic medical centers: results of the National ED Overcrowding Study (NEDOCS). Acad. Emerg. Med. 2004; 11: 38–50.
10.1197/j.aem.2003.07.017
PubMed Web of Science® Google Scholar
7Krochmal P, Riley TA. Increased health care costs associated with ED overcrowding. Am. J. Emerg. Med. 1994; 12: 265–6.
10.1016/0735-6757(94)90135-X
CAS PubMed Web of Science® Google Scholar
8Salehi L, Phalpher P, Valani R et al. Emergency department boarding: a descriptive analysis and measurement of impact on outcomes. CJEM 2018; 20: 929–37.
10.1017/cem.2018.18
PubMed Google Scholar
9Boulain T, Malet A, Maitre O. Association between long boarding time in the emergency department and hospital mortality: a single-center propensity score-based analysis. Intern. Emerg. Med. 2020; 15: 479–89.
10.1007/s11739-019-02231-z
PubMed Web of Science® Google Scholar
10Cleveland WS, Grosse E, Shyu WM. Local regression models. In: JM Chambers, TJ Hastie, eds. Chapter 8 in Statistical Models in S. Pac Grove, CA: Wadsworth BrooksCole, 1992; 608.
Google Scholar
11Asplin BR, Flottemesch TJ, Gordon BD. Developing models for patient flow and daily surge capacity research. Acad. Emerg. Med. 2006; 13: 1109–13.
10.1197/j.aem.2006.07.004
PubMed Web of Science® Google Scholar

Volume33, Issue2

April 2021

Pages 310-315

Lost bed capacity in emergency departments: A descriptive analysis and data visualisation exploration

Abstract

Objective

Methods

Results

Conclusions

Open Research

Data availability statement

References

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

Lost bed capacity in emergency departments: A descriptive analysis and data visualisation exploration

Abstract

Objective

Methods

Results

Conclusions

Open Research

Data availability statement

References

References

Related

Information