Impact and Safety of AI in Decision Making in the ICU: a Simulation Experiment
Study Details
Study Description
Brief Summary
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
Detailed Description
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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| ICU Clinicians
|
Other: Hypothetical AI
n/a - There is no intervention. Clinicians will review the suggestions of a hypothetical AI
|
Outcome Measures
Primary Outcome Measures
- Influence of AI on ICU Clinicians [3 months]
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.
Secondary Outcome Measures
- Participants' characteristics [3 months]
What are the characteristics of those taking part in the simulation and how does this affect decision making.
- Trust in AI [3 months]
How much do ICU clinicians trust the AI system.
- Confidence in participants' decisions [3 months]
How much confidence do clinicians place in the AI system
- Proportion of time with attention on AI explanation [3 months]
Where is attention focused during the simulation
Eligibility Criteria
Criteria
Inclusion Criteria:
- Junior (senior house officer) or senior (registrar/fellow/consultant) ICU doctor
Exclusion Criteria:
- Participants not meeting the inclusion criteria.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Imperial College Hospitals NHS Trust | London | United Kingdom | W2 1PG |
Sponsors and Collaborators
- Imperial College London
- University of York
Investigators
- Principal Investigator: Matthieu Komorowski, MD, PhD, Imperial College London
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 22CX7592