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Development of a risk predictive scoring system to identify patients at risk of representation to emergency department: a retrospective population-based analysis in Australia
  1. Euijoon Ahn1,
  2. Jinman Kim1,2,
  3. Khairunnessa Rahman3,
  4. Tanya Baldacchino2,
  5. Christine Baird3
  1. 1 School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia
  2. 2 Nepean Telehealth Technology Centre, Nepean Hospital, Penrith, New South Wales, Australia
  3. 3 Integrated Care Initiative, Nepean Hospital, Penrith, New South Wales, Australia
  1. Correspondence to Dr Jinman Kim; jinman.kim{at}sydney.edu.au

Abstract

Objective To examine the characteristics of frequent visitors (FVs) to emergency departments (EDs) and develop a predictive model to identify those with high risk of a future representations to ED among younger and general population (aged ≤70 years).

Design and setting A retrospective analysis of ED data targeting younger and general patients (aged ≤70 years) were collected between 1 January 2009 and 30 June 2016 from a public hospital in Australia.

Participants A total of 343 014 ED presentations were identified from 170 134 individual patients.

Main outcome measures Proportion of FVs (those attending four or more times annually), demographic characteristics (age, sex, indigenous and marital status), mode of separation (eg, admitted to ward), triage categories, time of arrival to ED, referral on departure and clinical conditions. Statistical estimates using a mixed-effects model to develop a risk predictive scoring system.

Results The FVs were characterised by young adulthood (32.53%) to late-middle (26.07%) aged patients with a higher proportion of indigenous (5.7%) and mental health-related presentations (10.92%). They were also more likely to arrive by ambulance (36.95%) and leave at own risk without completing their treatments (9.8%). They were also highly associated with socially disadvantage groups such as people who have been divorced, widowed or separated (12.81%). These findings were then used for the development of a predictive model to identify potential FVs. The performance of our derived risk predictive model was favourable with an area under the receiver operating characteristic (ie, C-statistic) of 65.7%.

Conclusion The development of a demographic and clinical profile of FVs coupled with the use of predictive model can highlight the gaps in interventions and identify new opportunities for better health outcome and planning.

  • population analysis
  • risk predictive modelling
  • emergency department
  • integrated care
  • health planning
  • health policy

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Contributors EA, JK, TB and CB were responsible for the conceptualisation of this project. KR, EA and CB were responsible for creation of the datasets in this project. EA and JK were responsible for statistical analysis and development of the predictive modelling. All authors contributed to the preparation of the manuscript.

  • Funding This research was supported by New South Wales (NSW) Health, Australia as part of the Integrated and Intensive Care Management Across Sector Collaboration (IICMASC) project. The resulting analyses and models are being used by the Nepean Blue Mountains Local Health District (NBMLHD).

  • Competing interests None declared.

  • Patient consent Not required.

  • Ethics approval Human Research Ethics Committees (HRECs) at the Nepean Blue Mountains Local Health District (NBMLHD), NSW, Australia.

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

  • Data sharing statement No additional data are available.