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J Hosp Med ; 14(1): 22-27, 2019 01.
Artigo em Inglês | MEDLINE | ID: mdl-30667407

RESUMO

BACKGROUND: Discharge delays adversely affect hospital bed availability and thus patient flow. OBJECTIVE: We aimed to increase the percentage of early discharges (EDCs; before 11 am). We hypothesized that obtaining at least 25% EDCs would decrease emergency department (ED) and postanesthesia care unit (PACU) hospital bed wait times. DESIGN: This study used a pre/postintervention retrospective analysis. SETTING: All acute care units in a quaternary care academic children's hospital were included in this study. PATIENTS: The patient sample included all discharges from the acute care units and all hospital admissions from the ED and PACU from January 1, 2014, to December 31, 2016. INTERVENTION: A multidisciplinary team identified EDC barriers, including poor identification of EDC candidates, accountability issues, and lack of team incentives. A total of three successive interventions were implemented using Plan-Do-Check-Act (PDCA) cycles over 10 months between 2015 and 2016 addressing these barriers. Interventions included EDC identification and communication, early rounding on EDCs, and modest incentives. MEASUREMENTS: Calendar month EDC percentage, ED (from time bed requested to the time patient left ED) and PACU (from time patient ready to leave to time patient left PACU) wait times were measured. RESULTS: EDCs increased from an average 8.8% before the start of interventions (May 2015) to 15.8% after interventions (February 2016). Using an interrupted time series, both the jump and the slope increase were significant (3.9%, P = .02 and 0.48%, P < .01, respectively). Wait times decreased from a median of 221 to 133 minutes (P < .001) for ED and from 56 to 36 minutes per patient (P = .002) for PACU. CONCLUSION: A multimodal intervention was associated with more EDCs and decreased PACU and ED bed wait times.


Assuntos
Eficiência Organizacional , Tempo de Internação/estatística & dados numéricos , Alta do Paciente/estatística & dados numéricos , Serviço Hospitalar de Emergência , Humanos , Estudos Retrospectivos , Fatores de Tempo , Listas de Espera
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