Ethics: Crisis Standards of Care Simulation.
Diane Fuller SwitzerSuzan Griffis KnowlesPublished in: Advanced emergency nursing journal (2024)
Ethical dilemmas exist with decision-making regarding resource allocations, such as critical care, ventilators and other critical equipment, and pharmaceuticals during pandemics. Triage artificial intelligence (AI) algorithms based on prognostication tools exist to guide these decisions; however, implicit bias may affect the decision-making process leading to deviation from the algorithm recommendations. Conflict within the ethical domain may be affected as well. A knowledge gap was identified within the Adult-Gerontology Acute Care Nurse Practitioner (AG-ACNP) curriculum regarding ethics in crisis standards of care (CSC) medical decision-making. Incorporating a CSC simulation looked to address this knowledge gap. A simulation-based learning (SBL) experience was designed as a critical access setting where CSC are in place and three diverse, medically complex patients in need of critical care present to the hospital where one critical care bed remains open. Given the complexity of the simulation scenario, a table-top pilot test was selected. Three AG-ACNP fourth-quarter students in their critical care rotation volunteered for the pilot test. Students were provided with the topic, "ethics crisis standards of care" and the article, "A catalogue of tools and variables from crisis and routine care to support decision-making during pandemics" by M. Cardona et al. (2021), to read in advance. Students were provided with the triage AI algorithm (M. Cardona et al., 2021) utilizing prognostication tools to prioritize which patient requires the critical care bed. The expectation was that implicit bias would enter the decision-making process, causing deviation from the triage AI algorithm and moral distress. The debriefing session revealed that students deviated from the triage AI algorithm, experienced implicit bias, moral distress, and utilized clinical judgment and experience to care for all three patients. The pilot test results support that a CSC SBL experience addresses a critical knowledge gap in AG-ACNP education and an SBL experience incorporating ethical decision-making curriculum with standardized patients should be developed and trialed as the next step.
Keyphrases
- decision making
- healthcare
- artificial intelligence
- machine learning
- public health
- deep learning
- quality improvement
- end stage renal disease
- emergency department
- palliative care
- big data
- ejection fraction
- newly diagnosed
- chronic kidney disease
- primary care
- acute care
- prognostic factors
- randomized controlled trial
- study protocol
- quantum dots
- pain management
- patient reported outcomes
- minimally invasive
- clinical trial
- virtual reality
- clinical practice
- patient reported
- visible light
- emergency medicine