AI support for ethical decision-making around resuscitation: proceed with care
Open accessPreprint — published as 10.1136/medethics-2020-106786 · Paper page
Abstract
Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardio-pulmonary resuscitation leading to the determination of a patient’s Do Not Attempt to Resuscitate (DNAR) status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that 1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and 2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions – conceptual, methodological and procedural – that need to be considered in further development and implementation efforts.
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AI systems have shown proficiency in structuring vast medical data, identifying clinical patterns, and validating medical hypotheses.
“For instance, AI systems have shown remarkable proficiency in structuring vast medical data, identifying clinical patterns, and validating medical hypotheses.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [6]
The AI systems discussed are primarily designed to assist with complex clinical decisions that depend heavily on patient preferences, such as resuscitation decisions.
“The AI systems discussed in the paper are primarily designed to assist with complex clinical decisions, especially those that depend heavily on patient preferences, such as resuscitation decisions.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [6]
AI can offer computational assistance for complex clinical decisions such as CPR and DNAR status that aligns with patient preferences while mitigating human biases under stress and time pressure.
“In scenarios like cardio-pulmonary resuscitation and determining Do Not Attempt to Resuscitate (DNAR) status, AI can offer computational assistance that aligns with patient preferences while mitigating human biases in decision-making under stress and time pressure [4][5][6].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [6]
These AI systems can analyze vast data sets to generate suggestions consistent with patient preferences and likely outcomes, potentially reducing the impact of stress, time pressure, personal biases, and conflicts of interest.
“These systems can analyze vast data sets to generate suggestions consistent with patient preferences and likely outcomes, potentially reducing the impact of stress, time pressure, personal biases, and conflicts of interest that may affect human decision-making.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [6]
The study conducted at a university hospital highlights the potential of AI to improve healthcare delivery and decision-making in high-pressure, ethically sensitive situations.
“The study conducted at a university hospital highlights the potential of AI to improve healthcare delivery and decision-making, especially in high-pressure, ethically sensitive situations.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [6]
The AI system would support rather than replace human decision-making, acting as a consultative tool.
“The AI system would support rather than replace human decision-making, acting as a consultative tool to enhance decision-making [6].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [6]
AI-based decision support could significantly improve the status quo by helping healthcare professionals and legal representatives make more informed decisions about a patient's DNAR status.
“It suggests that AI-based decision support could significantly improve the status quo by helping healthcare professionals and legal representatives make more informed decisions about a patient's DNAR status.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [6]
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References 52 · Cited by 67
Research works printed but not linked: 3
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- Authors
- Biller-Andorno, N., Ferrario, A., Joebges, S., Krones, T., Massini, F., Barth, P., Arampatzis, G., & Krauthammer, M.
- Affiliations
- ETH Zurich (CH)
- University of Zurich (CH)
- Venue
- Journal of Medical Ethics
- Publisher
- openRxiv
- Published
- 2020-08-19
- Volume
- 48
- Issue
- 3
- Pages
- 175-183
- ISSN
- 0306-6800 (ISSN-L)
- OpenAlex
- W3134156671
- Cited by
- 67
- References
- 52
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 19 · 2023: 12 · 2022: 7 · 2021: 4
- FWCI
- 9.08
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Journal of Medical Ethics (publishedVersion)
- medRxiv (acceptedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Collegium Helveticum
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Palliative Care and End-of-Life Issues
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- artificial intelligence, clinical decision support, cardiopulmonary resuscitation, do-not-resuscitate orders, ethical decision-making, patient preferences, physician bias, time pressure, COVID-19 pandemic
- MeSH terms
- COVID-19, SARS-CoV-2, Artificial Intelligence, Humans, Resuscitation Orders, Pandemics
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