Decision making under uncertainty in the emergency department: studying the effects of cognitive biases in the diagnosis of sepsis

• Main Author: Noonan, Thomas Zachary
• Other Authors: Pennathur, Priyadarshini R.
• Format: Others
• Language: English
• Published: University of Iowa 2018
• Subjects: Industrial Engineering
• Online Access: https://ir.uiowa.edu/etd/6231; https://ir.uiowa.edu/cgi/viewcontent.cgi?article=7563&context=etd

This was a retrospective study analyzing the diagnosis of sepsis, a severe systemic reaction to infection, in the emergency department. Sepsis is one of the leading causes of hospital mortality. Though, despite an increased focus on sepsis awareness in recent years, the rates of sepsis are increasing. Both the root causes and the bodily effects of sepsis are varied which makes screening (the identification of potentially septic patients) and diagnosis (the identification of sepsis by a medical professional) extremely difficult. In the face of this uncertainty, several attempts have been made to formalize the definition of sepsis including the systemic inflammation response syndrome (SIRS) criteria. These well-defined criteria can be used to design screens for identifying septic patients via their electronic health record (EHR), but these alerts tend to not be very selective and as such they produce many false alarms. The aim of this study was to determine how these alerts effect the decision making of physicians in the emergency department in regard sepsis diagnosis. More specifically, the goal was to determine if any of a number of well-known cognitive biases: sequential contrast effects, confirmation bias, and representativeness, could be detected in relation to sepsis diagnosis. Using a retrospective dataset of patients for which SIRS alerts were triggered, a set of behavioral criteria were designed using standard sepsis treatment procedures to determine the physicians’ diagnoses of those patients. The distribution of these diagnoses and the way past alerts were related to the diagnosis rates were analyzed. The patterns found in these analyses were constant with that would be expected in decisions made under the influence the identified biases. Additionally, there was found to be correlation between past alerts and the amount of information physicians use to make diagnoses lending further evidence of this conclusion. These results could be used to help design better alerts in the future or to improve the way medical information is presented to physicians to prevent biases from occurring in sepsis diagnosis.