Healthcare Professionals Risk Assessments for Alert Overrides in High-Risk IV Infusions Using Simulated Scenarios | JoVE Visualize

Area of Science:

Medical Informatics
Patient Safety
Pharmacology

Background:

Intravenous (IV) infusions of high-risk medications require careful management to prevent patient harm.
Assessing the expected risk of harm from medication errors is crucial for developing effective safety interventions.
Understanding how different infusion parameters influence perceived risk is essential for optimizing medication safety protocols.

Purpose of the Study:

To calculate the expected risk of IV infusion harm for simulated high-risk medications exceeding soft limits.
To investigate the impact of various risk factors, including limit types and care areas, on perceived harm risk.
To establish benchmarks for validating future risk quantification models in medication safety.

Main Methods:

30 infusion scenarios were designed for four high-risk medications (propofol, morphine, insulin, heparin) in adult intensive care (AICU) and adult medical/surgical care (AMSU) units.
20 pharmacists and 5 nurses assessed the expected risk of harm for each scenario.
Statistical analyses, including ANOVA, were used to evaluate the effects of field limit types (SoftMax, HardMax), dose limits, and care area-medication combinations on risk.

Main Results:

Overdosing scenarios with continuous and bolus dose limits had significantly higher assessed risks than bolus dose rate limits.
Larger SoftMax limits in AICU were associated with higher overdose risk, but this effect was not observed in AMSU.
Increased drug amounts in AICU and AMSU generally led to higher assessed risks, with some exceptions in AMSU regarding moderate vs. higher amounts.

Conclusions:

Healthcare professionals' assessments provide valuable insights into the expected risk of harm from simulated high-risk IV infusions.
Different field limit types and SoftMax settings significantly influence the perceived risk of medication errors.
These findings serve as crucial benchmarks for developing and validating risk quantification models to enhance patient safety in medication administration.