Impact and Safety of AI in Decision Making in the ICU: a Simulation Experiment

Study Overview
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Brief Summary
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The impact of deploying artificial intelligence (AI) in healthcare settings in unclear, in particular with regards to how it will influence human decision makers. Previous research demonstrated that AI alerts were frequently ignored (Kamal et al., 2020 ) or could lead to unexpected behaviour with worsening of patient outcomes (Wilson et al., 2021 ). On the other hand, excessive confidence and trust placed in the AI could have several adverse consequences including ability to detect harmful AI decisions, leading to patient harm as well as human deskilling. Some of these aspects relate to automation bias. 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.

Detailed Description
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The impact of deploying artificial intelligence (AI) in healthcare settings in unclear, in particular with regards to how it will influence human decision makers. Previous research demonstrated that AI alerts were frequently ignored (Kamal et al., 2020 ) or could lead to unexpected behaviour with worsening of patient outcomes (Wilson et al., 2021 ). On the other hand, excessive confidence and trust placed in the AI could have several adverse consequences including ability to detect harmful AI decisions, leading to patient harm as well as human deskilling. Some of these aspects relate to automation bias. 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.

Official Title
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Impact and Safety of AI in Decision Making in the ICU: a Simulation Experiment

Conditions
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Sepsis

Intervention / Treatment
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* Other: Hypothetical AI


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Junior (senior house officer) or senior (registrar/fellow/consultant) ICU doctor Exclusion Criteria: Participants not meeting the inclusion criteria.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| ICU Clinicians<br> | Other: Hypothetical AI<br>* n/a - There is no intervention. Clinicians will review the suggestions of a hypothetical AI<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Influence of AI on ICU Clinicians | Influence of AI on ICU Clinicians, this will be divided into the following categories: overall and stratified by safe/unsafe, junior/senior and positive/negative attitude towards AI. | 3 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Participants' characteristics | What are the characteristics of those taking part in the simulation and how does this affect decision making. | 3 months | 
| Trust in AI | How much do ICU clinicians trust the AI system. | 3 months | 
| Confidence in participants' decisions | How much confidence do clinicians place in the AI system | 3 months | 
| Proportion of time with attention on AI explanation | Where is attention focused during the simulation | 3 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial Intelligence