Emergency centre reorganization in preparation to the COVID-19 pandemic: A district hospital's dynamic adaptation response

The COVID-19 global pandemic forced healthcare facilities to put special isolation measures in place to limit nosocomial transmission. Cohorting is such a measure and refers to placing infected patients (or under investigation) together in a designated area. This report describes the physical reorganisation of the emergency centre at Khayelitsha Hospital, a district level hospital in Cape Town, South Africa in preparation to the COVID-19 pandemic. The preparation included the identification of a person under investigation (PUI) room, converting short stay wards into COVID-19 isolation areas, and relocating the paediatric section to an area outside the emergency centre. Finally, we had to divide the emergency centre into a respiratory and non-respiratory side by utilising part of the hospital's main reception. We are positive that the preparation and reorganization of the emergency centre will limit nosocomial transmission during the expected COVID-19 surge. Our experience in adapting to COVID-19 may have useful implications for ECs throughout South Africa and in low-and-middle income countries that are preparing for this pandemic

Clinical teams' experiences of crowding in public emergency centres in Cape Town, South Africa

alone in reckoning with high patient demand and insufficient resources to treat these patients; staff-to-patient ratios are generally lower than in the Global North. The study of crowding and its consequences for patient care is a key research priority for strengthening the quality and efficacy of emergency care in South Africa. The study set out to understand frontline staff's perspectives on crowding in Cape Town public ECs to learn how they cope in such high- pressure working conditions, determine what they see as the factors contributing to crowding, and obtain their recommendations for reform. Methods: This research is a qualitative study from interviews and observations at five ECs in Cape Town, conducted in June and July 2017. In total 43 staff were interviewed individually or in pairs. The interviews included physicians of varying levels of experience (25), and registered or enrolled nurses (18). Data were analysed with the qualitative text-analysis software NVivo. Results: Both doctors and nurses saw crowding as a consequence of three factors: 1) limited bed space in the EC, 2) insufficient health professionals to care for admitted patients, and 3) the presence of boarders. Systemic or organizational factors as well as human resource scarcity were determined to be the key reasons for crowding. Discussion: With its high patient acuity and volume and its limited human and material resources, South Africa is an important case study for understanding how emergency care providers manage working in crowded conditions. The solutions to crowding recommended by interviewees were to expand the EC workforce and to add discharge lounges and examination tables

The association between hospital arrival time, transport method, prehospital time intervals, and in-hospital mortality in trauma patients presenting to Khayelitsha Hospital, Cape Town

Introduction:Trauma is a leading cause of unnatural death and disability in South Africa. The aim of the study was to determine whether method of transport, hospital arrival time or prehospital transport time intervals were associated with in-hospital mortality among trauma patients presenting to Khayelitsha Hospital, a district-level hospital on the outskirts of Cape Town, South Africa.Methods:The Khayelitsha Hospital Emergency Centre database was retrospectively analysed for trauma-related patients presenting to the resuscitation area between 1 November 2014 and 30 April 2015. Missing data and additional variables were collected by means of a chart review. Eligible patients' folders were scrutinised for hospital arrival time, transport time intervals, transport method and in-hospital mortality. Descriptive statistics were presented for all variables. Categorical data were analysed using the Fisher's Exact test and Chi-square, continuous data by logistic regression and the Mann Whitney test. A confidence interval of 95% was used to describe variance and a p-value of <0.05 was deemed significant.Results:The majority of patients were 19­44 year old males (n = 427, 80.3%) and penetrating trauma the most frequent mechanism of injury (n = 343, 64.5%). In total, 258 (48.5%) patients arrived with their own transport, 254 (47.7%) by ambulance and 20 (3.8%) by the police service. The arrival of trauma patients peaked during the weekend, and was especially noticeable between midnight and six a.m. In-hospital mortality (n = 18, 3.4%) was not significantly affected by transport method (p = 0.26), hospital arrival time (p = 0.22) or prehospital transport time intervals (all p-values >0.09).Discussion:Method of transport, hospital arrival time and prehospital transport time intervals did not have a substantially measurable effect on in-hospital mortality. More studies with larger samples are suggested due to the small event rate.African relevance

The association between hospital arrival time, transport method, prehospital time intervals, and in-hospital mortality in trauma patients presenting to Khayelitsha Hospital, Cape Town

Introduction:Trauma is a leading cause of unnatural death and disability in South Africa. The aim of the study was to determine whether method of transport, hospital arrival time or prehospital transport time intervals were associated with in-hospital mortality among trauma patients presenting to Khayelitsha Hospital, a district-level hospital on the outskirts of Cape Town, South Africa.Methods:The Khayelitsha Hospital Emergency Centre database was retrospectively analysed for trauma-related patients presenting to the resuscitation area between 1 November 2014 and 30 April 2015. Missing data and additional variables were collected by means of a chart review. Eligible patients' folders were scrutinised for hospital arrival time, transport time intervals, transport method and in-hospital mortality. Descriptive statistics were presented for all variables. Categorical data were analysed using the Fisher's Exact test and Chi-square, continuous data by logistic regression and the Mann Whitney test. A confidence interval of 95% was used to describe variance and a p-value of <0.05 was deemed significant.Results:The majority of patients were 19­44 year old males (n = 427, 80.3%) and penetrating trauma the most frequent mechanism of injury (n = 343, 64.5%). In total, 258 (48.5%) patients arrived with their own transport, 254 (47.7%) by ambulance and 20 (3.8%) by the police service. The arrival of trauma patients peaked during the weekend, and was especially noticeable between midnight and six a.m. In-hospital mortality (n = 18, 3.4%) was not significantly affected by transport method (p = 0.26), hospital arrival time (p = 0.22) or prehospital transport time intervals (all p-values >0.09). Discussion:Method of transport, hospital arrival time and prehospital transport time intervals did not have a substantially measurable effect on in-hospital mortality. More studies with larger samples are suggested due to the small event rate