PubMed · 12741951
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Daniel M Fatovich. 2003-05-19. Access block: problems and progress.. https://pubmed.ncbi.nlm.nih.gov/12741951/
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AIM: To describe the effect of a pilot national telephone advice service (Healthline) on a public hospital emergency department. METHODS: We prospectively gathered information from the Christchurch Hospital Emergency Department (ED) computer- and non-computer-based information systems, for a six-month period during the operation of Healthline. We compared the data with five earlier periods when Healthline was not running. In addition, Healthline collected and analysed data from call log information. RESULTS: There was a small increase (1.1%) in ED attendance during the study period. Patients referred by Healthline had a similar triage distribution to the general ED population, but a lower admission rate (29% vs 47%). Telephone calls to the ED dropped dramatically during the study period. CONCLUSIONS: Healthline had little effect on overall ED census and appeared to refer patients with similar acuity to the general ED census. It decreased the workload for ED nursing staff charged with answering advice calls.
BACKGROUND AND OBJECTIVE: Emergency department (ED) overcrowding has been increasing over the last years. The aims are to define ED overcrowding, and to determine and quantify which factors explain it. PATIENTS AND METHOD: For 3 consecutive weeks throughout 3 years (2000-2002), we recorded every 3-hour period, the arrivals, the occupancy rate (OR) of patients in ED, in first aid area (FAA), and in observation area (OA) according to the reason for their stay. The data was subjected to multiple logistic regression analysis including as a dependent variable non overcrowding/overcrowding for each area (ED, FAA, and OA). Overcrowding was defined as an OR >= 100%. Models from the three areas were calculated according to goodness of fit and were discriminated by ROC methodology. Models were set up after randomizing data in two groups: selection set (88% of data) and validation set (12% of data). RESULTS: Variables associated with overcrowding in the ED model were OR of patients waiting for test results, for a bed going to be left, to find a bed, for test performed out of ED, and for outcome. In the FAA model, they were OR of patients being seen, and waiting for test results. Finally, in the OA model they were OR of patients waiting for a bed going to be left, to find a bed, for test performed out of ED, and for outcome. For all models sensitivity and specificity were greater than 85%, with a ROC area greater than 0.97. We did not find any relationship between number of arrivals and overcrowding for none model. Results were corroborated on the validation data set. CONCLUSIONS: Patients remaining in the ED due to factors related to both hospital (waiting for a bed going to be left, or to find a bed), and ED itself (waiting for outcome) are the main reason for ED overcrowding.
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