Reducing overcrowding in an emergency department: a pilot study

SUMMARY OBJECTIVE Exploring the use of forecasting models and simulation tools to estimate demand and reduce the waiting time of patients in Emergency Departments (EDs). METHODS The analysis was based on data collected in May 2013 in the ED of Recanto das Emas, Federal District, Brasil, which uses a Manchester Triage System. A total of 100 consecutive patients were included: 70 yellow (70%) and 30 green (30%). Flow patterns, observed waiting time, and inter-arrival times of patients were collected. Process maps, demand, and capacity data were used to build a simulation, which was calibrated against the observed flow times. What-if analysis was conducted to reduce waiting times. RESULTS Green and yellow patient arrival-time patterns were similar, but inter-arrival times were 5 and 38 minutes, respectively. Wait-time was 14 minutes for yellow patients, and 4 hours for green patients. The physician staff comprised four doctors per shift. A simulation predicted that allocating one more doctor per shift would reduce wait-time to 2.5 hours for green patients, with a small impact in yellow patients' wait-time. Maintaining four doctors and allocating one doctor exclusively for green patients would reduce the waiting time to 1.5 hours for green patients and increase it in 15 minutes for yellow patients. The best simulation scenario employed five doctors per shift, with two doctors exclusively for green patients. CONCLUSION Waiting times can be reduced by balancing the allocation of doctors to green and yellow patients and matching the availability of doctors to forecasted demand patterns. Simulations of EDs' can be used to generate and test solutions to decrease overcrowding.

RESUMO OBJETIVO Explorar o uso de modelos de previsão e ferramentas de simulação para estimar a demanda e reduzir o tempo de espera dos pacientes em Departamentos de Emergência (DE). METODOLOGIA A análise foi baseada em dados coletados em maio de 2013, no DE do Recanto das Emas, Distrito Federal, Brasil, que utiliza o Protocolo de Manchester como sistema de triagem. Um total de 100 pacientes consecutivos foram incluídos: 70 amarelos (70%) e 30 verdes (30%). Padrões de fluxo, tempo de espera observado e tempos entre as chegadas dos pacientes foram registrados. Mapas de processo, demanda e dados de capacidade foram utilizados na construção de uma simulação que foi calibrada de acordo com o fluxo observado. Uma análise do tipo "e se..." foi conduzida para reduzir os tempos de espera. RESULTADOS Os padrões de tempo de chegada para pacientes verdes e amarelos foram semelhantes, mas os tempos entre chegadas foram 5 e 38 minutos, respectivamente. O tempo de espera foi de 14 minutos para pacientes amarelos e 4 horas para pacientes verdes. A equipe médica era composta por quatro médicos por turno. Uma simulação previu que a inclusão de mais um médico por turno reduziria o tempo de espera para 2,5 horas para pacientes verdes, com um impacto pequeno no tempo de espera dos pacientes amarelos. A manutenção de quatro médicos e a inclusão de um médico exclusivamente para pacientes verdes reduziria o tempo de espera para 1,5 horas para pacientes verdes e aumentaria em 15 minutos para os pacientes amarelos. O melhor cenário simulado utilizou cinco médicos por plantão, com dois médicos exclusivos para pacientes verdes. CONCLUSÃO Os tempos de espera podem ser reduzidos equilibrando a distribuição de médicos para pacientes verdes e amarelos e relacionando a disponibilidade dos médicos aos padrões de demanda previstos. Simulações de DE podem ser utilizadas para gerar e testar soluções para diminuir a superlotação.

Reducing overcrowding in an emergency department: a pilot study.

OBJECTIVE: Exploring the use of forecasting models and simulation tools to estimate demand and reduce the waiting time of patients in Emergency Departments (EDs). METHODS: The analysis was based on data collected in May 2013 in the ED of Recanto das Emas, Federal District, Brasil, which uses a Manchester Triage System. A total of 100 consecutive patients were included: 70 yellow (70%) and 30 green (30%). Flow patterns, observed waiting time, and inter-arrival times of patients were collected. Process maps, demand, and capacity data were used to build a simulation, which was calibrated against the observed flow times. What-if analysis was conducted to reduce waiting times. RESULTS: Green and yellow patient arrival-time patterns were similar, but inter-arrival times were 5 and 38 minutes, respectively. Wait-time was 14 minutes for yellow patients, and 4 hours for green patients. The physician staff comprised four doctors per shift. A simulation predicted that allocating one more doctor per shift would reduce wait-time to 2.5 hours for green patients, with a small impact in yellow patients' wait-time. Maintaining four doctors and allocating one doctor exclusively for green patients would reduce the waiting time to 1.5 hours for green patients and increase it in 15 minutes for yellow patients. The best simulation scenario employed five doctors per shift, with two doctors exclusively for green patients. CONCLUSION: Waiting times can be reduced by balancing the allocation of doctors to green and yellow patients and matching the availability of doctors to forecasted demand patterns. Simulations of EDs' can be used to generate and test solutions to decrease overcrowding.

Assessing the performance of surgical teams.

BACKGROUND: High-performing and high-reliability teams are an important component of service delivery. With a focused emphasis on safety in acute care hospitals, understanding the nature of surgical teams and team performance is an essential component to achieving high-quality surgical care. More information is needed about the challenges to effective team functioning in the operating room, the influence of working conditions, and the environmental context on surgical team performance. PURPOSE: The purpose of this study is to describe the nature of surgical teams and how they perform in the operating room to contribute to a broader knowledge about high-performing and high-reliability teams in health care settings. METHODOLOGY/APPROACH: We conducted a qualitative study involving direct observation and semistructured interviews. Field observations of 10 high-complexity surgeries and face-to-face interviews with 26 members of surgical teams were completed at one university medical center. A conceptual framework derived from the literature was developed to guide the selection of surgeries and surgical teams to be observed. Data were transcribed and analyzed to identify the factors and different conditions that influence the performance of these surgical teams. FINDINGS: The type of coordination and the degree of independent and interdependent coordination vary among the seven observed stages of the surgical process. Most of the surgical teams were ad hoc teams and as such, further challenged by consistently frequent "hand-offs" for break relief. Additional role demands influence the situational dynamics which can alter the adaptive capacity of the team. PRACTICE IMPLICATIONS: The surgical event evokes a changing degree of coordination and adaptation to complexity and uncertainty. In such environments, relational coordination through leadership can contribute to a successful surgical result, improvement of the overall process, including error reduction, and enhanced knowledge creation and dissemination, particularly germane in research university teaching hospitals.