Background Healthcare systems are under pressure to efficiently and safely reduce acute care admissions to hospital. There is a need to develop a standardised system for assessing emergency department performance which takes into account case-mix variation. The objective of this study was to derive and validate a standardised tool for assessing variations in medical admissions through emergency departments in Hong Kong.
Methods Retrospective study of patients attending emergency departments of 14 acute hospitals in Hong Kong. Data were retrieved from a centralised administrative database.
Results Of 2 531 225 patients who attended emergency departments between 1 January 2001 and 31 December 2003, 780 444 (30.8%) were admitted to medical wards. A model derived from 2001 data shows well-calibrated admission probabilities, with an area under the receiver operating characteristic curve for probability of admission of 90.3 (95% CI ±0.11). The areas under the receiver operating characteristic curves for 2002 and 2003 validation sets were 89.9 (95% CI ±0.11) and 89.0 (95% CI ±0.12), respectively. With an averaged benchmark, reductions in medical admissions of up to 19% could be achieved, while under the most optimistic assumption, reductions of up 36% could be achieved.
Conclusions A tool for benchmarking hospital medical admissions and minimising case-mix variation has been derived and validated in Hong Kong, but it requires further validation in other healthcare systems given the wide variations in admission thresholds internationally. This may be used as one potential method to evaluate the performance of emergency departments against a common standard.
- Acute medical admissions
- case-mix variation
- emergency medicine
- Hong Kong
- acute coronary syndrome
- prehospital care
- paediatric resuscitation
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Competing interests None.
Ethics approval Ethics approval was obtained from the ethics review board of the Joint Chinese University of Hong Kong—New Territories East Cluster of Hong Kong.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Data from this study will not be available for public use.
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