Physicians’ cognitive approach to prognostication after cardiac arrest

Steinberg, Alexis; Grayek, Emily; Arnold, Robert M.; Callaway, Clifton W.; Fischhoff, Baruch; Krishnamurti, Tamar; White, Douglas B.; Elmer, Jonathan

doi:10.1016/j.resuscitation.2022.01.001

Cited by 14 publications

(16 citation statements)

References 33 publications

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“…1), influenced by many factors rarely discussed in the research literature. 49 These factors include clinician characteristics (e.g., background specialty, risk aversion), practice environments (e.g., institutional norms, resource availability), and surrogate behavior (e.g., patient values, medical knowledge). 49,50 Decision science directs that research to look for factors than can induce biases in heuristics that otherwise serve clinicians well.…”

Section: Descriptionmentioning

confidence: 99%

“…49 These factors include clinician characteristics (e.g., background specialty, risk aversion), practice environments (e.g., institutional norms, resource availability), and surrogate behavior (e.g., patient values, medical knowledge). 49,50 Decision science directs that research to look for factors than can induce biases in heuristics that otherwise serve clinicians well. 51,52 People develop well-calibrated heuristics when they perform the same task repeatedly, guided by reliable rules, and informed by prompt feedback on their performance.…”

Section: Descriptionmentioning

confidence: 99%

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Cognitive Biases and Shared Decision Making in Acute Brain Injury

Steinberg,

Fischhoff

2023

Semin Neurol

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Many patients hospitalized after severe acute brain injury are comatose and require life-sustaining therapies. Some of these patients make favorable recoveries with continued intensive care, while others do not. In addition to providing medical care, clinicians must guide surrogate decision makers through high-stakes, emotionally charged decisions about whether to continue life-sustaining therapies. These consultations require clinicians first to assess a patient's likelihood of recovery given continued life-sustaining therapies (i.e., prognosticate), then to communicate that prediction to surrogates, and, finally, to elicit and interpret the patient's preferences. At each step, both clinicians and surrogates are vulnerable to flawed decision making. Clinicians can be imprecise, biased, and overconfident when prognosticating after brain injury. Surrogates can misperceive the choice and misunderstand or misrepresent a patient's wishes, which may never have been communicated clearly. These biases can undermine the ability to reach choices congruent with patients' preferences through shared decision making (SDM). Decision science has extensively studied these biases. In this article, we apply that research to improving SDM for patients who are comatose after acute brain injury. After introducing SDM and the medical context, we describe principal decision science results as they relate to neurologic prognostication and end-of-life decisions, by both clinicians and surrogates. Based on research regarding general processes that can produce imprecise, biased, and overconfident prognoses, we propose interventions that could improve SDM, supporting clinicians and surrogates in making these challenging decisions.

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Section: Descriptionmentioning

confidence: 99%

Section: Descriptionmentioning

confidence: 99%

Cognitive Biases and Shared Decision Making in Acute Brain Injury

Steinberg,

Fischhoff

2023

Semin Neurol

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“…Neuroprognostication is centered around three primary axes: acquisition of diagnostic information, interpretation of this information, and formulation of a prognosis. 81 However, the factors that influence each of these nodes are composed of highly variable components that can be hospital-specific, physician-specific, time-specific, or based on clinical confounders and idiosyncratic test features. 81 One study reported that physician factors are responsible for 26% of variance in prognostication.…”

Section: Clinician Variability In Neuroprognosticationmentioning

confidence: 99%

“…81 However, the factors that influence each of these nodes are composed of highly variable components that can be hospital-specific, physician-specific, time-specific, or based on clinical confounders and idiosyncratic test features. 81 One study reported that physician factors are responsible for 26% of variance in prognostication. 82 Furthermore, studies of prognostic accuracy highlight the much greater prognostic accuracy of clinicians predicting poor outcome than predicting good outcome.…”

Section: Clinician Variability In Neuroprognosticationmentioning

confidence: 99%

Ethical Considerations in Neuroprognostication Following Acute Brain Injury

Lissak,

Edlow,

Rosenthal

et al. 2023

Semin Neurol

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Neuroprognostication following acute brain injury (ABI) is a complex process that involves integrating vast amounts of information to predict a patient's likely trajectory of neurologic recovery. In this setting, critically evaluating salient ethical questions is imperative, and the implications often inform high-stakes conversations about the continuation, limitation, or withdrawal of life-sustaining therapy. While neuroprognostication is central to these clinical “life-or-death” decisions, the ethical underpinnings of neuroprognostication itself have been underexplored for patients with ABI. In this article, we discuss the ethical challenges of individualized neuroprognostication including parsing and communicating its inherent uncertainty to surrogate decision-makers. We also explore the population-based ethical considerations that arise in the context of heterogenous prognostication practices. Finally, we examine the emergence of artificial intelligence-aided neuroprognostication, proposing an ethical framework relevant to both modern and longstanding prognostic tools.

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“…7 Similarly, a recent qualitative study of neurologic prognostication in cardiac arrest showed no differences in approach between generalist and expert physicians but did highlight a complex and iterative diagnostic process; it also indicated the importance of physician and hospital factors, as well as clinical ones. 8 Can copilots and innovative technology help with that complexity, perhaps by more focused processing of those iterative cycles? Peltan et al 6 research cannot answer that question, particularly because the simulations were short and did not focus on this aspect of the collaboration.…”

Section: Gerard Bury MD Dublin Irelandmentioning

confidence: 99%

Tough Decisions in Resuscitation

Bury¹

2022

Chest

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Physicians’ cognitive approach to prognostication after cardiac arrest

Cited by 14 publications

References 33 publications

Cognitive Biases and Shared Decision Making in Acute Brain Injury

Cognitive Biases and Shared Decision Making in Acute Brain Injury

Ethical Considerations in Neuroprognostication Following Acute Brain Injury

Tough Decisions in Resuscitation

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