Statistical tools used for analyses of frequent users of emergency department: a scoping review

BMJ Open. 2019 May 24;9(5):e027750. doi: 10.1136/bmjopen-2018-027750.


Objective: Frequent users represent a small proportion of emergency department users, but they account for a disproportionately large number of visits. Their use of emergency departments is often considered suboptimal. It would be more efficient to identify and treat those patients earlier in their health problem trajectory. It is therefore essential to describe their characteristics and to predict their emergency department use. In order to do so, adequate statistical tools are needed. The objective of this study was to determine the statistical tools used in identifying variables associated with frequent use or predicting the risk of becoming a frequent user.

Methods: We performed a scoping review following an established 5-stage methodological framework. We searched PubMed, Scopus and CINAHL databases in February 2019 using search strategies defined with the help of an information specialist. Out of 4534 potential abstracts, we selected 114 articles based on defined criteria and presented in a content analysis.

Results: We identified four classes of statistical tools. Regression models were found to be the most common practice, followed by hypothesis testing. The logistic regression was found to be the most used statistical tool, followed by χ2 test and t-test of associations between variables. Other tools were marginally used.

Conclusions: This scoping review lists common statistical tools used for analysing frequent users in emergency departments. It highlights the fact that some are well established while others are much less so. More research is needed to apply appropriate techniques to health data or to diversify statistical point of views.

Keywords: Frequent users; Statistical methods.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Emergency Service, Hospital / statistics & numerical data*
  • Humans
  • Patient Acceptance of Health Care / statistics & numerical data*