Describing the categories of people that contribute to an Emergency Centre crowd at Khayelitsha hospital, Western Cape, South Africa

Emergency Centre (EC) crowding has globally been recognised to adversely affect patients, staff and visitors. Anecdotally, local ECs are perceived to be fairly crowded, however, not much is known about the size of this crowd and what constitutes it. Although more reliable, resource restrictions render more detailed flow studies less achievable. This study describes the EC crowd at Khayelitsha hospital in Cape Town, South Africa as the number and different categories of people, at predefined times during the day over a four-week period. Methods: A prospective, cross-sectional design was used. Headcounts were made by predefined groups at 09h00, 14h00, and 21h00 every day for four weeks. Predefined groups included doctors, nurses, visitors, patients, and other allied health staff. Summary statistics were used to describe the data. Precision was described using the 95% confidence interval. Results: A total of 16,353 people were counted during the study period. On average, 6370 (39%) of the groups were staff, 5231 (32%) were patients and 4752 (29%) were visitors. Of the staff, 586 (3.6%) were EC doctors, 733 (4.4%) were non-EC doctors, 1488 (9%) were EC nurses, and 445 (3%) were non-EC nurses. Although patient numbers in the EC remained constant, visitors and non-EC staff varied significantly with visitors peaking in the afternoon and non-EC staff drastically reducing in the evening. The EC was consis- tently crowded – average occupancy: 130%. Conclusion: Staff levels fluctuated predictably, reducing at night and over weekends, while patient levels remained constant. Non-EC doctors more than doubled during the day on week shifts, in significantly more numbersthanECdoctors,suggestingthatmanyofthepatientsintheECwerelikelytobeadmissionsboarding inthe EC. Visitor numbers weresubstantialduringvisitinghours and furtheraggravatedcrowding. Resource- light studies involving flow are important to explore crowding in low- and middle income settings