Exploring cognitive bias in destination therapy left ventricular assist device decision making: A retrospective qualitative framework analysis

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Abstract

Background

Cognitive biases are psychological influences, which cause humans to make decisions, which do not seemingly maximize utility. For people with heart failure, the left ventricular assist device (LVAD) is a surgically implantable device with complex tradeoffs. As such, it represents an excellent model within which to explore cognitive bias in a real-world decision. We conducted a framework analysis to examine for evidence of cognitive bias among people deciding whether or not to get an LVAD.

Objectives

The aim of this study was to explore the influence of cognitive bias on the LVAD decision-making process.

Methods

We analyzed previously conducted interviews of patients who had either accepted or declined an LVAD using a deductive, predetermined framework of cognitive biases. We coded and analyzed the interviews using an inductive-deductive framework approach, which also allowed for other themes to emerge.

Results

We interviewed a total of 22 heart failure patients who had gone through destination therapy LVAD decision making (15 who had accepted the LVAD and 7 who had declined). All patients appeared influenced by state dependence, where both groups described high current state of suffering, but the groups differed in whether they believed LVAD would relieve suffering or not. We found evidence of cognitive bias that appeared to influence decision making in both patient groups, but groups differed in terms of which cognitive biases were present. Among accepters, we found evidence of anchoring bias, availability bias, optimism bias, and affective forecasting. Among decliners, we found evidence of errors in affective forecasting.

Conclusions

Medical decision making is often a complicated and multifaceted process that includes cognitive bias as well as other influences. It is important for clinicians to recognize that patients can be affected by cognitive bias, so they can better understand and improve the decision-making process to ensure that patients are fully informed.

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