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Checking it twice: an evaluation of checklists for detecting medication errors at the bedside using a chemotherapy
Rachel E White1, Patricia L Trbovich, Anthony C Easty
1Healthcare Human Factors Group, Centre for Global eHealth Innovation, University Health Network, Toronto, Canada. rachel.white@uhn.on.ca
Quality & Safety in Health Care
|August 21, 2010
Summary
Checklists with explicit, step-by-step instructions significantly improve nurses' ability to detect medication errors during chemotherapy administration. Tailoring checklists to workflow enhances usability and efficiency.
Area of Science:
- Patient Safety
- Medication Error Detection
- Healthcare Quality Improvement
Background:
- Medication errors pose a significant risk in healthcare, particularly in complex administration settings like outpatient chemotherapy.
- Effective tools are needed to support clinicians in preventing and identifying these errors at the bedside.
Purpose of the Study:
- To identify specific checklist components that enhance the detection of medication administration errors.
- To evaluate the impact of checklist design on error identification rates in a simulated chemotherapy setting.
Main Methods:
- A high-fidelity simulation study was conducted in a usability laboratory.
- Nurses administered outpatient chemotherapy using two distinct checklists designed to identify specific medication administration errors.
- Error detection rates were quantified across four error categories.
Main Results:
- Checklist effectiveness varied by error type and design, with detection rates ranging from 0% to 90%.
- Explicit, step-by-step instructions were superior to general reminders for error detection.
- A specific instruction for patient identification verification improved detection by 65 percentage points.
- Aligning checklist sequence with clinical workflow improved ease of use and efficiency.
Conclusions:
- Checklists with detailed, step-by-step instructions are valuable for error detection in tasks requiring high cognitive load.
- Further research should explore methods to help clinicians balance mechanistic task execution with abstract clinical reasoning.
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