Impact and Safety of AI in Decision Making in the ICU: a Simulation Experiment
NCT ID: NCT05495438
Last Updated: 2023-02-27
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
38 participants
OBSERVATIONAL
2022-07-22
2022-10-31
Brief Summary
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In this simulation study, the investigators intend to measure whether medical decisions in areas of high clinical uncertainty are modified by the use of an AI-based clinical decision support tool. How the dose of intravenous fluids (IVF) and vasopressors administered by doctors in adult patients with sepsis (severe infection with organ failure) in the ICU), changes as a result of disclosing the doses suggested by a hypothetical AI will be measured. The area of sepsis resuscitation is poorly codified, with high uncertainty leading to high variability in practice. This study will not specifically mention the AI Clinician (Komorowski et al., 2018). Instead, the investigators will describe a hypothetical AI for which there is some evidence of effectiveness on retrospective data in another clinical setting (e.g. a model that was retrospectively validated using data from a different country than the source data used for model training) but no prospective evidence of effectiveness or safety. As such, it is possible for this hypothetical AI to provide unsafe suggestions. The investigators will intentionally introduce unsafe AI suggestions (in random order), to measure the sensitivity of our participants at detecting these.
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Detailed Description
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In this simulation study, the investigators intend to measure whether medical decisions in areas of high clinical uncertainty are modified by the use of an AI-based clinical decision support tool. How the dose of intravenous fluids (IVF) and vasopressors administered by doctors in adult patients with sepsis (severe infection with organ failure) in the ICU), changes as a result of disclosing the doses suggested by a hypothetical AI will be measured. The area of sepsis resuscitation is poorly codified, with high uncertainty leading to high variability in practice. This study will not specifically mention the AI Clinician (Komorowski et al., 2018). Instead, the investigators will describe a hypothetical AI for which there is some evidence of effectiveness on retrospective data in another clinical setting (e.g. a model that was retrospectively validated using data from a different country than the source data used for model training) but no prospective evidence of effectiveness or safety. As such, it is possible for this hypothetical AI to provide unsafe suggestions. The investigators will intentionally introduce unsafe AI suggestions (in random order), to measure the sensitivity of our participants at detecting these.
The investigators will examine what participant characteristics are linked with an increase likelihood of being influenced by the AI, and conduct a number of pre-specified subgroup analyses, e.g. junior versus senior ICU doctors, and separating those with a positive or a negative attitude towards AI.
Conditions
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Study Design
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OTHER
PROSPECTIVE
Study Groups
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ICU Clinicians
Hypothetical AI
n/a - There is no intervention. Clinicians will review the suggestions of a hypothetical AI
Interventions
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Hypothetical AI
n/a - There is no intervention. Clinicians will review the suggestions of a hypothetical AI
Eligibility Criteria
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Inclusion Criteria
18 Years
ALL
Yes
Sponsors
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University of York
OTHER
Imperial College London
OTHER
Responsible Party
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Principal Investigators
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Matthieu Komorowski, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Imperial College London
Locations
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Imperial College Hospitals NHS Trust
London, , United Kingdom
Countries
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Other Identifiers
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22CX7592
Identifier Type: -
Identifier Source: org_study_id
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