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Checking it twice: an evaluation of checklists for detecting medication errors at the bedside using a chemotherapy model
OBJECTIVE: To determine what components of a checklist contribute to effective detection of medication errors at the bedside. DESIGN: High-fidelity simulation study of outpatient chemotherapy administration. SETTING: Usability laboratory. PARTICIPANTS: Nurses from an outpatient chemotherapy unit, wh...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BMJ Group
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3002832/ https://www.ncbi.nlm.nih.gov/pubmed/20724398 http://dx.doi.org/10.1136/qshc.2009.032862 |
Sumario: | OBJECTIVE: To determine what components of a checklist contribute to effective detection of medication errors at the bedside. DESIGN: High-fidelity simulation study of outpatient chemotherapy administration. SETTING: Usability laboratory. PARTICIPANTS: Nurses from an outpatient chemotherapy unit, who used two different checklists to identify four categories of medication administration errors. MAIN OUTCOME MEASURES: Rates of specified types of errors related to medication administration. RESULTS: As few as 0% and as many as 90% of each type of error were detected. Error detection varied as a function of error type and checklist used. Specific step-by-step instructions were more effective than abstract general reminders in helping nurses to detect errors. Adding a specific instruction to check the patient's identification improved error detection in this category by 65 percentage points. Matching the sequence of items on the checklist with nurses' workflow had a positive impact on the ease of use and efficiency of the checklist. CONCLUSIONS: Checklists designed with explicit step-by-step instructions are useful for detecting specific errors when a care provider is required to perform a long series of mechanistic tasks under a high cognitive load. Further research is needed to determine how best to assist clinicians in switching between mechanistic tasks and abstract clinical problem solving. |
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