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Transfusion ; 50(9): 1887-96, 2010 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-20456700

RESUMEN

BACKGROUND: Red blood cell (RBC) product wastage in hospitals is reported to range from 0.1% to 6.7%. Wastage at our institution averaged 4.4% of 63,000 issued RBC products. Data indicated that approximately 87% of wasted RBC units were either individual units that were out of blood bank for more than 30 minutes (dispensed but not administered) or units packed in transport containers that had temperature indicators affixed to each unit. We hypothesized that Lean Sigma methodology could be used to reduce RBC wastage by 50%. STUDY DESIGN AND METHODS: An interdisciplinary hospital team (transfusion medicine, nursing, and anesthesiology) used Lean Sigma methodology as a tool to reduce RBC product wastage, with a focus on container wastage, which was determined to yield the largest impact. Using the five-part Lean Sigma process-define, measure, analyze, improve, and control-the team collected baseline wastage data, identified major factors affecting RBC product wastage, and implemented interventions to reduce amount of wastage. RESULTS: Factors identified as contributors to RBC wastage most amenable to improvement were lack of awareness and training of staff ordering and handling RBC products, management of temperature-validated containers, inconsistent interpretation of RBC temperature indicators, and need for accountability when ordering blood products. Overall RBC product wastage decreased from 4.4% to a sustained rate of less than 2%. This reduction decreased the number of RBC units wasted by approximately 4300 per year, savings approximately $800,000 over the 4-year period of the study. CONCLUSIONS: Lean Sigma methodology was an effective tool for reducing RBC wastage in a large academic hospital.


Asunto(s)
Bancos de Sangre/estadística & datos numéricos , Residuos Sanitarios/prevención & control , Evaluación de Procesos, Atención de Salud/métodos , Eficiencia Organizacional , Eritrocitos , Hospitales/estadística & datos numéricos , Humanos , Residuos Sanitarios/estadística & datos numéricos
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