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Comparison of the International Crowding Measure in Emergency Departments (ICMED) and the National Emergency Department Overcrowding Score (NEDOCS) to measure emergency department crowding: pilot study
  1. Adrian Boyle1,
  2. Gary Abel2,
  3. Pramin Raut3,
  4. Richard Austin4,
  5. Vijayasankar Dhakshinamoorthy5,
  6. Ravi Ayyamuthu6,
  7. Iona Murdoch4,
  8. Joel Burton4
  1. 1Emergency Department, Cambridge University Hospitals Foundation Trust, Cambridge, Cambridgeshire, UK
  2. 2Cambridge Centre for Health Services Research, University of Cambridge Institute of Public Health Forvie Site, Cambridge, UK
  3. 3Clinical School, Cambridge University, Cambridge, Cambridgeshire, UK
  4. 4Emergency Department, Addenbrookes Hospital, Cambridge University Hospitals Foundation Trust, Cambridge, Cambridgeshire, UK
  5. 5Emergency Department, Peterborough City Hospital, Peterborough and Stamford Hospitals Foundation Trust, Peterborough, Cambridgeshire, UK
  6. 6Emergency Department, West Suffolk Hospital, Bury St Edmunds, Suffolk, UK
  1. Correspondence to Dr Adrian Boyle, Emergency Department, Cambridge University Hospitals Foundation Trust, Hills Road, Cambridge, Cambridgeshire, CB2 2QQ, UK; Adrian.boyle{at}


Introduction There is uncertainty about the best way to measure emergency department crowding. We have previously developed a consensus-based measure of crowding, the International Crowding Measure in Emergency Departments (ICMED). We aimed to obtain pilot data to evaluate the ability of a shortened form of the ICMED, the sICMED, to predict senior emergency department clinicians’ concerns about crowding and danger compared with a very well-studied measure of emergency department crowding, the National Emergency Department Overcrowding Score (NEDOCS).

Methods We collected real-time observations of the sICMED and NEDOCS and compared these with clinicians’ perceptions of crowding and danger on a visual analogue scale. Data were collected in four emergency departments in the East of England. Associations were explored using simple regression, random intercept models and models accounting for correlation between adjacent time points.

Results We conducted 82 h of observation in 10 observation sets. Naive modelling suggested strong associations between sICMED and NEDOCS and clinician perceptions of crowding and danger. Further modelling showed that, due to clustering, the association between sICMED and danger persisted, but the association between these two measures and perception of crowding was no longer statistically significant.

Conclusions Both sICMED and NEDOCS can be collected easily in a variety of English hospitals. Further studies are required but initial results suggest both scores may have potential use for assessing crowding variation at long timescales, but are less sensitive to hour-by-hour variation. Correlation in time is an important methodological consideration which, if ignored, may lead to erroneous conclusions. Future studies should account for such correlation in both design and analysis.

  • emergency care systems
  • emergency care systems, emergency departments
  • effectiveness
  • crowding
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