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Nurses' critical event risk assessments: a judgement analysis
Carl Thompson1, Tracey Bucknall, Carole A Estabrookes
1Department of Health Sciences, University of York, York, UK. cat4@york.ac.uk
Journal of Clinical Nursing
|November 29, 2007
Summary
Nurses often misinterpret clinical signs of patient deterioration, leading to inaccurate risk assessments for critical events. Improving nurses' decision-making skills requires focusing on accurate information synthesis and potentially supported decision-making strategies.
Area of Science:
- Clinical Nursing
- Patient Safety
- Decision Science
Background:
- Half of inpatient cardiac arrests have preceding, unacted-upon clinical signs of deterioration.
- Nurses may misinterpret basic clinical signs like heart rate and oxygenation.
- Delayed nursing responses to abnormal signs can range from one to three hours.
Purpose of the Study:
- To explore how nurses utilize clinical information for critical event risk assessment.
- To understand the factors influencing nurses' decisions regarding patient risk.
- To identify discrepancies between nurses' risk assessments and actual patient risk.
Main Methods:
- A double system judgement analysis using Brunswik's lens model.
- 245 nurses from four countries judged 50 clinical scenarios.
- Scenarios varied in vital signs, conscious level, and oxygenation support; time pressure and protocols were manipulated.
Main Results:
- Significant variation existed in nurses' risk assessments despite identical information.
- Time pressure and protocols were weighted more heavily than clinical data for critical decisions.
- Nurses' weighting of cues did not align with their contribution to real patient risk.
- Nurses synthesized information non-linearly, reducing decisional accuracy.
Conclusions:
- Nurses overestimated risk and the need for intervention in simulated patients.
- Individualized risk predictions and intervention needs varied considerably among nurses.
- Nurses did not consistently use linear reasoning or unique knowledge for accurate risk prediction.
- Developing judgement expertise requires attention to the quality of clinical experience and potentially supported decision-making tools.
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