Ethics: Crisis Standards of Care Simulation.

Area of Science:

• Medical Ethics
• Nursing Education
• Health Policy

Background:

• Pandemics present ethical challenges in resource allocation, particularly for critical care.
• Existing triage artificial intelligence (AI) algorithms may be influenced by implicit bias, impacting decision-making.
• A knowledge gap exists in Adult-Gerontology Acute Care Nurse Practitioner (AG-ACNP) curricula regarding ethics in crisis standards of care (CSC).

Purpose of the Study:

• To address the identified knowledge gap in AG-ACNP education concerning ethical decision-making during CSC.
• To evaluate the impact of implicit bias and moral distress on AG-ACNP students' adherence to AI-driven triage algorithms in a simulated critical care setting.

Main Methods:

• A table-top simulation-based learning (SBL) pilot test was conducted with three AG-ACNP students.
• Students were provided with CSC ethical scenarios, relevant readings, and an AI triage algorithm for resource allocation.
• The simulation involved prioritizing three diverse patients for a single critical care bed.

Main Results:

• Students deviated from the AI triage algorithm recommendations.
• Participants reported experiencing implicit bias and moral distress during the decision-making process.
• Students utilized clinical judgment and experience to manage all patients, rather than strictly adhering to the algorithm.

Conclusions:

• Simulation-based learning effectively addresses knowledge gaps in AG-ACNP education regarding CSC ethics.
• The study highlights the influence of implicit bias and moral distress on clinical decision-making in critical care resource allocation.
• Further development and trialing of SBL incorporating ethical decision-making with standardized patients are recommended.