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Human factors considerations for contraindication alerts.

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  • 1Department of Hospital Pharmacy, Erasmus Medical Centre, Rotterdam, the Netherlands.

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Summary
This summary is machine-generated.

Alert fatigue from clinical decision support systems is a major issue. This study operationalized human factors principles for contraindication alerts, creating a feasible testing instrument.

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Area of Science:

  • Clinical Informatics
  • Human Factors Engineering
  • Patient Safety

Background:

  • Alert fatigue is a significant challenge in clinical decision support systems, potentially compromising patient safety.
  • Existing tools effectively address drug-drug interactions (DDIs) but a similar instrument for contraindications is lacking.
  • Human factors principles are crucial for designing effective drug safety alerts.

Purpose of the Study:

  • To operationalize human factors principles for contraindication alerting.
  • To develop and validate a quantitative instrument for assessing contraindication alerts based on human factors.
  • To establish a test protocol for evaluating the implementation of human factors in contraindication alert design.

Main Methods:

  • Developed a 30-item instrument based on 10 human factors principles for contraindication alerts.
  • Adapted 21 items from a validated DDI-instrument and created 4 new items.
  • Created a test protocol and assessed inter-rater reliability using three independent raters.

Main Results:

  • The newly developed instrument includes 30 items derived from 10 human factors principles.
  • Inter-rater reliability analysis showed moderate agreement (κ=0.540) among testers.
  • The study demonstrated the feasibility of testing human factors in contraindication alert design.

Conclusions:

  • A new instrument for operationalizing human factors principles in contraindication alerting has been developed.
  • The instrument and associated test protocol are feasible for evaluating contraindication alert design.
  • This work addresses a critical gap in optimizing clinical decision support for patient safety.