Citation

Knol R, Kelly E, Paul E, Cleland H, Wellington-Boyd A, Lambert C, Harms L. Burns 2019; ePub(ePub): ePub.

Affiliation

Department of Social Work, The University of Melbourne, Victoria 3010, Australia. Electronic address: louisekh@unimelb.edu.au.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.burns.2019.08.001

PMID

31466920

Abstract

INTRODUCTION: Psychosocial risk and protective factors specific to acute burn patients have been shown to impact on longer-term health outcomes. Yet the nature and impact of such factors throughout the acute hospital admission phase have not been extensively examined to date. This study analysed the psychosocial pre-admission, hospitalisation and discharge factors for a sample of burn patients at an Australian specialist acute burns unit (The Alfred) and the relationship with their admission length.

METHODS: A retrospective audit was undertaken of psychosocial factors documented in patient medical records, Victorian Adult Burns Service registry data, and social work files (January-December 2014).

RESULTS: Two hundred and forty-nine patients were identified, with a mean age of 42.83 years and a preponderance of men (73.9%). The majority (71%) had a low burn severity (<10%), and an average of eight psychosocial factors. Independent of the severity of the burn, two psychosocial domains were strongly predictive of length of stay - coping with treatment and discharge planning - along with pre-existing psychosocial factors and family support.

CONCLUSION: A diversity of psychosocial factors was identified, highlighting the clinical complexity of this patient group. Given three domains of admission-related psychosocial factors were predictive of the length of patient stay, more targeted psychosocial interventions in these areas may ensure patients and their families are supported more effectively throughout an admission, and length of stay costs may also be reduced.

Copyright © 2019 Elsevier Ltd and ISBI. All rights reserved.

Language: en

Keywords

Burns; Data mining; Hospital; Length of stay; Psychosocial; Support