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J Emerg Nurs ; 44(2): 123-131, 2018 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-29223696

RESUMO

BACKGROUND: The percentage of patients who leave the emergency department without being seen by a provider is a measure of efficiency and presents risk-management concerns. The number of patients actually "seen" by a provider is a measure of productivity. The opening of our new emergency department in December 2012, resulted in increases in both demand and the percentage of patients who left without being seen. Operational nursing leadership managed ED patient flow, but the structure was loosely organized on an ad hoc basis. METHODS: Operational nursing leadership roles were re-assigned to personnel with management aptitude and interest. The charge nurse coordinated care throughout all sections (pods) of the department while the pod lead nurse coordinated care in each pod. The flow coordinator nurse accepted transfers and emergency medical services arrivals. Nursing and physician staffing remained unchanged, and measures were calculated over a 3-year period (December 3, 2012, to December 2, 2015). The number of patients seen per day was analyzed using simple linear regression. The percentage of patients who left without being seen was analyzed using fractional logistic regression; P< 0.05 was considered statistically significant. RESULTS: The weekly mean number of patients seen per day rose 13% from 265 to 299 patients. The weekly mean percentage of patients who left without being seen declined 45% from 8.2% to 4.5%. The regression lines for both measures were significant at P < 0.001. CONCLUSION: Measures of efficiency and productivity can be improved significantly with a dedicated operational nursing leadership structure without adding nursing or physician staffing.


Assuntos
Eficiência Organizacional/estatística & dados numéricos , Enfermagem em Emergência/métodos , Serviço Hospitalar de Emergência/normas , Admissão e Escalonamento de Pessoal/estatística & dados numéricos , Avaliação de Programas e Projetos de Saúde/métodos , Aglomeração , Humanos , Tempo de Internação/estatística & dados numéricos , Massachusetts , Fatores de Tempo
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