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Randomized Controlled Trial
. 2017 Aug 1;119(2):231-238.
doi: 10.1093/bja/aex129.

Intelligent dynamic clinical checklists improved checklist compliance in the intensive care unit

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Free article
Randomized Controlled Trial

Intelligent dynamic clinical checklists improved checklist compliance in the intensive care unit

A J R De Bie et al. Br J Anaesth. .
Free article

Abstract

Background: Checklists can reduce medical errors. However, the effectiveness of checklists is hampered by lack of acceptance and compliance. Recently, a new type of checklist with dynamic properties has been created to provide more specific checklist items for each individual patient. Our purpose in this simulation-based study was to investigate a newly developed intelligent dynamic clinical checklist (DCC) for the intensive care unit (ICU) ward round.

Methods: Eligible clinicians were invited to participate as volunteers. Highest achievable scores were established for six typical ICU scenarios to determine which items must be checked. The participants compared the DCC with the local standard of care. The primary outcomes were the caregiver satisfaction score and the percentages of checked items overall and of critical items requiring a direct intervention.

Results: In total, 20 participants were included, who performed 116 scenarios. The median percentage of checked items was 100.0% with the DCC and 73.6% for the scenarios completed with local standard of care ( P <0.001). Critical items remained unchecked in 23.1% of the scenarios performed with local standard of care and 0.0% of the scenarios where the DCC was available ( P <0.001). The mean satisfaction score of the DCC was 4.13 out of 5.

Conclusions: This simulation study indicates that an intelligent DCC significantly increases compliance with best practice by reducing the percentage of unchecked items during ICU ward rounds, while the user satisfaction rate remains high. Real-life clinical research is required to evaluate this new type of checklist further.

Keywords: checklist; clinical; decision support systems; intensive care units; medical errors; patient safety.

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