Identifying diverse concepts of discharge failure patients at emergency department in the USA: a large-scale retrospective observational study

BMJ Open. 2019 Jun 27;9(6):e028051. doi: 10.1136/bmjopen-2018-028051.

Abstract

Objectives: Identifying patients who are at high risk for discharge failure allows for implementation of interventions to improve their care. However, discharge failure is currently defined in literature with great variability, making targeted interventions more difficult. We aim to derive a screening tool based on the existing diverse discharge failure models.

Design, setting and participants: This is a single-centre retrospective cohort study in the USA. Data from all patients discharged from the emergency department were collected from 1 January 2015 through 31 December 2017 and followed up within 30 days.

Methods: Scoring systems were derived using modified Framingham methods. Sensitivity, specificity and area under the receiver operational characteristic (AUC) were calculated and compared using both the broad and restricted discharge failure models.

Results: A total of 227 627 patients were included. The Screening for Healthcare fOllow-Up Tool (SHOUT) scoring system was derived based on the broad and restricted discharge failure models and applied back to the entire study cohort. A sensitivity of 80% and a specificity of 71% were found in SHOUT scores to identify patients with broad discharge failure with AUC of 0.83 (95% CI 0.83 to 0.84). When applied to a 3-day restricted discharge failure model, a sensitivity of 86% and a specificity of 60% were found to identify patients with AUC of 0.79 (95% CI 0.78 to 0.80).

Conclusion: The SHOUT scoring system was derived and used to screen and identify patients that would ultimately become discharge failures, especially when using broad definitions of discharge failure. The SHOUT tool was internally validated and can be used to identify patients across a wide spectrum of discharge failure definitions.

Keywords: health policy; quality in health care; risk management.

Publication types

  • Observational Study

MeSH terms

  • Aftercare / standards
  • Aftercare / statistics & numerical data
  • Emergency Service, Hospital / standards*
  • Female
  • Hospitals, Urban
  • Humans
  • Length of Stay / statistics & numerical data
  • Male
  • Patient Discharge / statistics & numerical data*
  • Retrospective Studies
  • Risk Factors
  • Tertiary Care Centers
  • United States