Academic journal article Australian Health Review

Frequent Emergency Attenders: Is There a Better Way?

Academic journal article Australian Health Review

Frequent Emergency Attenders: Is There a Better Way?

Article excerpt


The problem of frequent, possibly preventable, emergency attendances has attracted a plethora of research studies in an attempt to reduce overcrowding. Understanding the reasons for frequent reattendances supports the development of strategies to prevent emergency department overcrowding.

Research in a range of countries including Ireland,1 the UK,2-4 Canada,5,6 Sweden,7-9 USA,10-14 Italy,15 Taiwan16,17 and Australia18-21 indicates that frequent attenders are more likely to be from disadvantaged groups and are older, male and non-white ethnic background. Frequently readmitted patients, however, are more likely to be older, present with an urgent condition or as an unplanned returned visit and have a diagnosis of neurosis, chronic disease or ambulatory care sensitive diseases.22 It is important to distinguish between frequent re-attenders and frequently readmitted patients so that solutions can be targeted to different needs.

Hong et al., in a study published in 2007,23 suggested that emergency departments were the wrong place to address complex social problems of frequent re-attenders. Coleman24 contended that we need to understand from patients, their families and caregivers about their preferences for different types of services. Campbell et al.25 found that re-utilisation rates and costs are higher for minor conditions in emergency departments, providing another reason in support of restricting emergency departments for serious and urgent conditions and managing minor conditions more appropriately elsewhere.

Under Andersen's health service utilisation model,26 the way in which people use health services depends on the availability of services and individual patient factors, including patient preferences and health-seeking behaviours. Are frequent emergency re-attenders using emergency departments because they prefer the services or because they cannot access alternative, and maybe more appropriate services? Penchansky's27 framework specifies factors of availability, accessibility, accommodation, affordability and acceptability. In considering possible access barriers the framework helps to pinpoint the way in which access influences utilisation.

The aim of this study was to identify patient characteristics associated with frequent re-attendances to develop solutions to prevent their occurrence.


We carried out a retrospective analysis of 2008 data from the emergency department collected at a 150 bed regional hospital in south-eastern Australia. All patient data were de-identified. The hospital, funded by the State and Commonwealth Governments, has a feeder population of ~100 000.

The primary binary outcome variable was the number of presentations set with a cut offpoint at four or more presentations denoting frequent re-presentations. The variables were: age, sex, date and time of arrival, country of birth, ICD-9 diagnosis, level of urgency,28 type of visit and mode of separation. Each presentation was accorded one primary ICD-9 diagnosis. Date and time of arrival variables were converted into hour, day and season of arrival.

The data were analysed from the perspective of presentations in row-for-each-presentation format or from the perspective of patients in a row-for-each-patient format with nested data for each patient. The primary research question to uncover the patient characteristics associated with frequent re-presentations required the row-for-each-patient format. Preliminary univariate analysis was performed on the row-for-each-presentation format on the variables for which there were different, or potentially different, values for each presentation: hour, day and season of arrival; urgency; unplanned return visit and diagnosis. Univariate analysis was also performed with the variables that were constant for each presentation (age, sex and country of birth).

A logistic regression model was constructed with the independent variables transformed by counting the number of occurrences in the nested variables for each patient and dividing by the total number of presentations for that patient resulting in a variable of the proportion of occurrences. …

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