Consumers who self-diagnose are more likely to believe they have a serious illness because they focus on their symptoms rather than the likelihood of a particular disease, according to a new study in the Journal of Consumer Research. This has significant implications for public health professionals as well as consumers.
"In today's wired world, self-diagnosis via internet search is very common. Such symptom-matching exercises may lead consumers to overestimate the likelihood of getting a serious disease because they focus on their symptoms while ignoring the very low likelihood that their symptoms are related to any serious illness," write authors Dengfeng Yan and Jaideep Sengupta (both Hong Kong University of Science and Technology).
Consumers often fear the worst when it comes to their own health while maintaining a calm objectivity with regard to others. For example, when someone else suffers from indigestion, we tend to accurately perceive it as indigestion, but experiencing the same symptom might lead us to panic and worry that we're having a heart attack.
The authors asked consumers to imagine that they or someone else were suffering from common symptoms such as cough, fever, runny nose, and headache. They were then asked to assess the likelihood that they or the other person had contracted either H1N1 (swine flu) or regular flu. Consumers were much more accurate when assessing other people's symptoms. Since they are more likely to misdiagnose themselves, consumers may end up taking unnecessary medical action, which is bad for them, and also bad from a societal cost perspective.
"One of the easiest ways to get rid of this bias is to see a real doctor instead of Dr. Google. A real doctor possesses much more knowledge and will take the prevalence of a disease into consideration because she is viewing the patient from a distance. This will prevent symptoms from exerting a disproportionate influence on the diagnosis," the authors conclude.
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More information: Dengfeng Yan and Jaideep Sengupta. "The Influence of Base Rate and Case Information on Health Risk Perceptions: A Unified Model of Self-Positivity and Self-Negativity." Journal of Consumer Research: February 2013.